yambda-discovery-ranking-two-tower-base / gemini-3-8-flash-high-base-fixed-r02-3d6fca60 Recorded model: gemini-3.8-flash ## Step 1 · user · 2026-09-29T05:48:43.173Z This is the Gemini CLI. We are setting up the context for our chat. Today's date is Tuesday, September 29, 2026 (formatted according to the user's locale). My operating system is: linux The project's temporary directory is: /home/agent/.gemini/tmp/app - **Workspace Directories:** - /app - /task - **Directory Structure:** Showing up to 200 items (files + folders). /app/ └───solution/ Showing up to 200 items (files + folders). /task/ ├───.keep ├───evaluate_public.py ├───ranking_evaluation.py ├───requirements.txt └───data/ ├───candidate_catalog.parquet ├───target_users.parquet ├───test_eligible_candidates.npz ├───test_preference_state.parquet ├───train_dislikes.parquet ├───train_likes.parquet ├───train_listens.parquet ├───train_undislikes.parquet ├───train_unlikes.parquet ├───val_dislikes.parquet ├───val_likes.parquet ├───val_listens.parquet ├───val_undislikes.parquet ├───val_unlikes.parquet ├───validation_eligible_candidates.npz └───validation_preference_state.parquet ## Step 2 · user · 2026-09-29T05:48:43.219Z # Build a latency-efficient PyTorch two-tower neural-network ranker Build a two-tower neural network in CPU PyTorch for the "Made for You" shelf of a music streaming service. Although the supplied benchmark is small, it represents a production system with millions of users and millions of items; the training approach must scale to that setting and therefore must not construct or decompose a global user-item interaction matrix. The production shelf has a strict request-time latency budget. Candidate item embeddings are computed offline and stored in a nearest-neighbor index. At request time, the system computes one user embedding and retrieves items by maximum inner-product search. The model must therefore decompose exactly into a user vector and an independently precomputable item vector of the same dimensionality, with the final relevance score equal to their dot product. Both vectors must be produced by separate trainable user and item towers implemented as PyTorch neural networks. The user tower must derive its representation from interaction history rather than a user-ID lookup. Per-user candidate eligibility, cutoff-specific explicit preference state, and a temporal backtest are provided. Candidate eligibility is platform-owned; all modeling within that candidate boundary is in scope. ## Data All inputs are under `/task/data/`. Training data ends at timestamp `24786800`. A 30-minute gap precedes a seven-day public validation window. The hidden test is the following seven-day window after another 30-minute gap. For each event family there is a `train_*.parquet` and `val_*.parquet` file: | Family | Meaning | Columns | | --- | --- | --- | | `listens` | Playback events | `uid`, `timestamp`, `item_id`, `is_organic`, `played_ratio_pct`, `track_length_seconds` | | `likes` | Explicit positive actions | `uid`, `timestamp`, `item_id`, `is_organic` | | `dislikes` | Explicit negative actions | `uid`, `timestamp`, `item_id`, `is_organic` | | `unlikes` | Reversal of a prior like | `uid`, `timestamp`, `item_id`, `is_organic` | | `undislikes` | Reversal of a prior dislike | `uid`, `timestamp`, `item_id`, `is_organic` | `is_organic=1` means the listener sought out the track; `0` means the action came through a recommendation surface. A listen is a conventional positive at 50% completion. The discovery-quality objective below uses the stronger 80% threshold. The two public windows have different roles: 1. Build and tune a validation ranker using only `train_*` as history and `val_*` as subsequent outcomes. 2. After selecting the approach, refit it on `train_* + val_*`. At hidden-test time the validation events are observed history, not recommendable outcomes. Do not use `val_*` events as features when measuring a validation ranking. They may and should be used when refitting the final hidden-test ranker. Additional files under `/task/data/`: - `candidate_catalog.parquet`: the 30,000 retrieval-catalog tracks, ordered by training positive-listen popularity. - `target_users.parquet`: users with at least 20 positive listens in the 90 days before the training cutoff, selected without future labels. - `validation_preference_state.parquet`: active explicit state at the training cutoff, for validation ranking. - `test_preference_state.parquet`: active explicit state after validation, for the final hidden-test ranking. State is `liked` or `disliked`; a pair may have both active channels. - `validation_eligible_candidates.npz` and `test_eligible_candidates.npz`: cutoff-correct candidate sets for every target user. Each contains aligned arrays `uids`, `item_ids`, and `packed_eligible`. For user row `r`, recover the Boolean item mask with `np.unpackbits(packed_eligible[r], count=len(item_ids), bitorder="little")`. The exact available Python distribution inventory is listed in `/task/requirements.txt`. Future test interactions are hidden and used only by the separate Harbor verifier. ## Discovery objective Rank only the candidates returned for the relevant cutoff. Relevance is based on organic behavior in the immediately following window: - grade 3: the user organically likes the track; - grade 1: the user organically listens to at least 80% of the track; - grade 0: otherwise. If both positive events occur, grade 3 wins. Ranking quality is measured with graded NDCG@10. DCG uses gain `2^grade - 1` and discount `1/log2(rank + 1)`, then averages per-user NDCG over users with at least one relevant item. The public evaluator reports this objective for development, but its score is not a separate acceptance stage. Final acceptance is determined on the hidden test using the same objective. The Yambda logs contain interactions, not impression propensities. Treat the organic objective as a robust offline target, not as a causal estimate of online lift. ## Public tools `/task/evaluate_public.py` scores a validation-stage ranking against the public window. Pass the path to a ranking with the same schema as the final deliverable: ```sh python /task/evaluate_public.py solution/validation.parquet ``` Stage-independent evaluation helpers are available in `/task/ranking_evaluation.py`. The builder installs the same checked-in module in the public agent image and private verifier image so scoring mechanics cannot drift. The public copy contains no hidden labels, evaluator-owned data paths, or pass thresholds. The public evaluator uses `validation_eligible_candidates.npz`; it is not a validator for the final artifact. No model implementation, fitted representation, or example submission is supplied. Design and tune the required PyTorch neural two-tower model, then refit the selected approach as described above. ## Deliverable Write `/app/solution/submission.parquet` or `/app/solution/submission.csv`, with exactly 100 rows for every target user and columns `uid`, `item_id`, `rank`. Ranks must be the integers 1-100 with no ties or duplicate items. Every submitted item must be present in that user's `test_eligible_candidates.npz` candidate set. Leave the code that defines, trains, and applies both towers, along with a concise `/app/solution/README.md`, in `/app/solution/`. The README should state the model architecture, training objective, public validation score, and final submission command. Keep the best valid submission saved throughout the attempt so it survives the deadline. ## Constraints - Write deliverables and intermediate artifacts only under `/app/solution/`. - Treat `/task/` and the installed Python environment as read-only. - Do not invoke nested agents or attempt to access verifier files, credentials, other trials, or host paths. - Do not install packages or use network access except for the model provider connection managed by Harbor. - The environment includes CPU PyTorch 2.8.0. No accelerator is available. - You have up to 60 minutes. Improve the model as much as possible within that limit. Structural validity alone is not sufficient; the final submission is also evaluated for ranking quality. ## Step 3 · agent · 2026-09-29T05:48:48.743Z ### update_topic · 2026-09-29T05:48:48.743Z { "strategic_intent": "Explore the task requirements, project structure, dataset statistics, and evaluate_public script.", "title": "Research & Exploration", "summary": "Starting the project to build a PyTorch two-tower neural-network ranker. Exploring task files, requirements, data properties, and evaluation mechanics." } ### Tool output · 2026-09-29T05:48:48.753Z Current topic: "Research & Exploration" Topic summary: Starting the project to build a PyTorch two-tower neural-network ranker. Exploring task files, requirements, data properties, and evaluation mechanics. Strategic Intent: Explore the task requirements, project structure, dataset statistics, and evaluate_public script. ## Step 4 · agent · 2026-09-29T05:48:51.142Z ### read_file · 2026-09-29T05:48:51.142Z { "file_path": "/task/evaluate_public.py" } ### Tool output · 2026-09-29T05:48:51.153Z #!/usr/bin/env python3 """Score a validation-stage ranking against public discovery labels. ``build_task.py`` publishes this module with the solver-visible task files. It loads target users, validation eligibility, and validation-window events from the adjacent ``data`` directory, then evaluates one ranking supplied on the command line. Stage-independent mechanics come from the adjacent public ``ranking_evaluation.py`` module. The builder installs the same checked-in module in the private judge, so public and private scoring mechanics cannot drift. A relevant candidate receives grade 3 for an organic like or grade 1 for an organic listen with at least 80 percent completion; a like takes precedence when both events exist. Before scoring, the ranking must contain exactly 100 distinct eligible items for every target user, with integer-valued ``uid``, ``item_id``, and ``rank`` columns and ranks 1 through 100 exactly once per user. Extra columns are ignored. Quality is graded NDCG@10, averaged only over users who have at least one eligible relevant item in the validation window. The evaluator prints a JSON object containing the model path and scores. It does not apply the task's pass threshold; its purpose is model development and selection. The private judge applies the threshold to the final submission. Usage:: python evaluate_public.py """ from __future__ import annotations import json import sys from pathlib import Path import pandas as pd from ranking_evaluation import ( CandidateSet, RankingValidationError, ranked_items, read_ranking, score_rankings, validate_ranking, ) HERE = Path(__file__).resolve().parent DATA = HERE / "data" LISTEN_STRONG = 80 def validation_truth( eligibility: CandidateSet, targets: set[int] ) -> dict[int, dict[int, int]]: """Build cutoff-correct graded relevance from public validation events. Only target-user events for eligible candidates are retained. Organic listens completed to at least 80 percent receive grade 1, and organic likes receive grade 3. Repeated user-item events are collapsed, and processing likes second ensures that grade 3 wins over grade 1. Args: eligibility: Validation-cutoff candidate membership service. targets: Complete set of target user IDs. Returns: A nested ``uid -> item_id -> grade`` mapping. Users without an eligible relevant event are omitted. """ listens = pd.read_parquet(DATA / "val_listens.parquet") likes = pd.read_parquet(DATA / "val_likes.parquet") strong = listens[ listens.uid.isin(targets) & (listens.is_organic == 1) & (listens.played_ratio_pct >= LISTEN_STRONG) ][["uid", "item_id"]].drop_duplicates() organic_likes = likes[ likes.uid.isin(targets) & (likes.is_organic == 1) ][["uid", "item_id"]].drop_duplicates() graded: dict[int, dict[int, int]] = {} for raw_uid, raw_item in strong.itertuples(index=False, name=None): uid, item = int(raw_uid), int(raw_item) if eligibility.is_eligible(uid, item): graded.setdefault(uid, {})[item] = 1 for raw_uid, raw_item in organic_likes.itertuples(index=False, name=None): uid, item = int(raw_uid), int(raw_item) if eligibility.is_eligible(uid, item): graded.setdefault(uid, {})[item] = 3 return graded def load_submission( path: Path, eligibility: CandidateSet, targets: set[int] ) -> dict[int, list[int]]: """Load and validate a public validation ranking. Files ending in ``.parquet`` are read as parquet; every other suffix is read as CSV. The required columns must be integer-valued, cover exactly the target-user set, contain 100 rows and ranks 1 through 100 per user, avoid duplicate user-item pairs, and contain only cutoff-eligible items. Args: path: Ranking file to read. eligibility: Validation-cutoff candidate membership service. targets: Exact set of users the ranking must cover. Returns: A ``uid -> ranked item IDs`` mapping ordered by ascending rank. Raises: ValueError: If the ranking violates the required schema or invariants. """ frame = validate_ranking(read_ranking(path), targets) if not all( eligibility.is_eligible(uid, item) for uid, item in frame[["uid", "item_id"]].itertuples(index=False, name=None) ): raise RankingValidationError( "submission contains an item rejected by eligibility service" ) return ranked_items(frame) def score( ranked: dict[int, list[int]], graded: dict[int, dict[int, int]], ) -> dict[str, float | int]: """Aggregate per-user NDCG@10 over users with public relevance labels. Args: ranked: Ranked item IDs for every target user. graded: Eligible validation relevance keyed by user and item. Returns: The six-decimal ``quality_ndcg@10`` value and number of scored users. """ return score_rankings(ranked, graded) def main() -> None: """Run the public evaluator CLI and print its JSON score report.""" targets_frame = pd.read_parquet(DATA / "target_users.parquet") targets = set(targets_frame.uid.astype(int)) eligibility = CandidateSet(DATA / "validation_eligible_candidates.npz") graded = validation_truth(eligibility, targets) if len(sys.argv) != 2: raise SystemExit( "usage: evaluate_public.py " ) path = Path(sys.argv[1]) ranked = load_submission(path, eligibility, targets) print(json.dumps({"model": str(path), "scores": score(ranked, graded)}, indent=2)) if __name__ == "__main__": main() ### read_file · 2026-09-29T05:48:51.142Z { "file_path": "/task/ranking_evaluation.py" } ### Tool output · 2026-09-29T05:48:51.153Z """Shared public primitives for Yambda discovery-ranking evaluation. Both the solver-visible validation evaluator and the private submission judge import this module from the generated task's ``public`` directory. It owns the stage-independent contract: reading a ranking, validating its shape, converting it to per-user rank order, looking up packed candidate eligibility, and computing graded NDCG@10. The module deliberately contains no event-derived labels, private artifact paths, or pass thresholds. Each evaluator supplies its own relevance data and per-cutoff eligibility checks around these common operations. """ from __future__ import annotations from pathlib import Path import numpy as np import pandas as pd __all__ = [ "CandidateSet", "QUALITY_K", "RankingValidationError", "REQUIRED_COLUMNS", "TOP_N", "ndcg_at_k", "ranked_items", "read_ranking", "score_rankings", "validate_ranking", ] TOP_N = 100 QUALITY_K = 10 REQUIRED_COLUMNS = ("uid", "item_id", "rank") class RankingValidationError(ValueError): """Report a ranking-schema or structural-contract violation.""" class CandidateSet: """Provide membership lookups over one packed candidate-set archive. The archive contains aligned ``uids``, ``item_ids``, and ``packed_eligible`` arrays. Candidate column ``c`` is represented by bit ``c % 8`` of byte ``c // 8`` using little-endian bit order. Arrays are copied into memory so they remain valid after the archive is closed. """ def __init__(self, path: Path): """Load an eligibility archive and build ID-to-position indexes. Args: path: Path to a ``.npz`` archive with the candidate-set schema. """ with np.load(path, allow_pickle=False) as artifact: self.uids = artifact["uids"].astype(np.int64, copy=True) self.item_ids = artifact["item_ids"].astype(np.int64, copy=True) self.packed = artifact["packed_eligible"].astype(np.uint8, copy=True) self.uid_row = {int(uid): row for row, uid in enumerate(self.uids)} self.item_column = { int(item): column for column, item in enumerate(self.item_ids) } def is_eligible(self, uid: int, item_id: int) -> bool: """Return whether ``item_id`` is eligible for ``uid``. IDs absent from the target-user or candidate-item arrays are treated as ineligible rather than raising an exception. Args: uid: Target user ID to query. item_id: Candidate item ID to query. Returns: ``True`` only when the corresponding packed eligibility bit is set. """ row = self.uid_row.get(int(uid)) column = self.item_column.get(int(item_id)) if row is None or column is None: return False byte, bit = divmod(column, 8) return bool((int(self.packed[row, byte]) >> bit) & 1) def read_ranking(path: Path) -> pd.DataFrame: """Read a parquet or CSV ranking into a pandas frame. Args: path: Input path. A ``.parquet`` suffix selects parquet; every other suffix is interpreted as CSV. Returns: The ranking frame as loaded by pandas. """ return pd.read_parquet(path) if path.suffix == ".parquet" else pd.read_csv(path) def validate_ranking( frame: pd.DataFrame, targets: set[int], ) -> pd.DataFrame: """Normalize and validate the stage-independent ranking contract. Required values must be finite integers. The user set must exactly match ``targets``; every user must have 100 rows, distinct items, and each rank from 1 through 100 exactly once. Cutoff-specific candidate eligibility is intentionally left to the calling evaluator. Args: frame: Raw ranking frame. Extra columns are discarded from an internal copy; the caller's frame is not modified. targets: Exact set of target user IDs the ranking must cover. Returns: A normalized frame containing only the required ``int64`` columns. Raises: RankingValidationError: If the ranking violates the shared contract. """ required = list(REQUIRED_COLUMNS) if not all(column in frame for column in required): raise RankingValidationError(f"submission requires columns {required}") normalized = frame[required].copy() for column in required: numeric = pd.to_numeric(normalized[column], errors="coerce") values = numeric.to_numpy(dtype=float) if numeric.isna().any() or not np.isfinite(values).all(): raise RankingValidationError( f"submission {column} must be finite numeric values" ) if not np.equal(numeric, np.floor(numeric)).all(): raise RankingValidationError( f"submission {column} must contain integers" ) normalized[column] = numeric.astype(np.int64) submitted_users = set(map(int, normalized.uid.unique())) if submitted_users != targets: raise RankingValidationError( f"submission user set mismatch: missing={len(targets - submitted_users)} " f"extra={len(submitted_users - targets)}" ) counts = normalized.groupby("uid").size() if not (counts == TOP_N).all(): raise RankingValidationError( f"submission must contain exactly {TOP_N} rows for every target user" ) if normalized.duplicated(["uid", "item_id"]).any(): raise RankingValidationError( "submission contains duplicate (uid, item_id) pairs" ) valid_ranks = set(range(1, TOP_N + 1)) ranks_are_valid = normalized.groupby("uid")["rank"].apply( lambda values: set(map(int, values)) == valid_ranks ) if not ranks_are_valid.all(): raise RankingValidationError( f"each user must have every rank from 1 through {TOP_N} exactly once" ) return normalized def ranked_items(frame: pd.DataFrame) -> dict[int, list[int]]: """Convert a validated ranking frame to ordered items keyed by user. Args: frame: Frame that has passed :func:`validate_ranking`. Returns: A ``uid -> item IDs`` mapping ordered by ascending rank. """ ordered = frame.sort_values(["uid", "rank"]) return { int(uid): list(map(int, values)) for uid, values in ordered.groupby("uid").item_id } def ndcg_at_k(ranked: list[int], gold: dict[int, int], k: int) -> float: """Compute graded normalized discounted cumulative gain at ``k``. Relevance gain is ``2**grade - 1`` and the discount at one-based rank ``r`` is ``1 / log2(r + 1)``. Items absent from ``gold`` have grade zero. Args: ranked: Item IDs in predicted rank order. gold: Mapping from relevant item IDs to integer relevance grades. k: Maximum number of predicted positions to score. Returns: For a valid duplicate-free ranking, NDCG in ``[0, 1]``; returns ``0.0`` when the ideal gain is zero. """ gains = np.asarray([gold.get(item, 0) for item in ranked[:k]], dtype=np.float64) discounts = 1.0 / np.log2(np.arange(2, len(gains) + 2)) dcg = float(np.sum((np.power(2.0, gains) - 1.0) * discounts)) ideal = np.asarray(sorted(gold.values(), reverse=True)[:k], dtype=np.float64) ideal_discounts = 1.0 / np.log2(np.arange(2, len(ideal) + 2)) idcg = float(np.sum((np.power(2.0, ideal) - 1.0) * ideal_discounts)) return dcg / idcg if idcg else 0.0 def score_rankings( ranked: dict[int, list[int]], graded: dict[int, dict[int, int]], ) -> dict[str, float | int]: """Aggregate per-user NDCG@10 over users with relevance labels. Args: ranked: Ranked item IDs for every target user. graded: Relevant item grades keyed by user and item. Returns: The six-decimal ``quality_ndcg@10`` value and number of scored users. """ quality = float( np.mean( [ ndcg_at_k(ranked[uid], gold, QUALITY_K) for uid, gold in graded.items() ] ) ) return { "quality_ndcg@10": round(quality, 6), "quality_users": len(graded), } ### read_file · 2026-09-29T05:48:51.142Z { "file_path": "/task/requirements.txt" } ### Tool output · 2026-09-29T05:48:51.153Z # Exact distribution inventory for the Yambda two-tower task family. # # This is an input to `uv pip sync`, not a list of minimum requirements. A # conforming task environment contains exactly these third-party Python # distributions and no others. Python itself is pinned by task.toml and both # Harbor image Dockerfiles. attrs==26.1.0 bleach==6.4.0 certifi==2026.7.22 chardet==7.6.0 charset-normalizer==3.5.1 fastjsonschema==2.22.2 filelock==3.32.3 fsspec==2026.7.0 idna==3.18 Jinja2==3.1.6 joblib==1.5.3 jsonschema==4.26.0 jsonschema-specifications==2025.9.1 jupyter-core==5.9.1 jupytext==1.19.5 kaggle==2.2.4 kagglesdk==0.1.37 markdown-it-py==4.2.0 MarkupSafe==3.0.3 mdit-py-plugins==0.6.1 mdurl==0.1.2 mpmath==1.3.0 nbformat==5.11.1 networkx==3.6.1 numpy==2.2.6 packaging==26.3 pandas==2.3.3 pillow==12.3.0 pip==26.0.1 platformdirs==4.11.3 protobuf==7.35.1 pyarrow==18.1.0 python-dateutil==2.9.0.post0 python-dotenv==1.2.3 python-slugify==8.0.4 pytz==2026.3.post1 PyYAML==6.0.3 referencing==0.37.0 reportlab==4.2.5 requests==2.34.2 rpds-py==2026.6.3 scikit-learn==1.7.2 scipy==1.15.3 setuptools==84.0.0 six==1.17.0 sympy==1.14.0 text-unidecode==1.3 threadpoolctl==3.6.0 torch==2.8.0 tqdm==4.70.0 traitlets==5.16.1 typing-extensions==4.16.0 tzdata==2026.3 urllib3==2.7.0 webencodings==0.6.1 ## Step 5 · agent · 2026-09-29T05:48:57.249Z ### Recorded reasoning summary Analyzing Current Data My focus has been on understanding the fundamental parameters. I've noted the specific PyTorch version and CPU requirement, along with the scale of the candidate catalog and target user datasets. The evaluation metric, Graded NDCG@, has also been assimilated. ### Recorded reasoning summary Refining Metric Interpretation I'm now deeply analyzing the Graded NDCG@ metric. I've grasped its components: grading for relevance, the DCG calculation with its exponential gain, and the logarithmic discount, all averaged over eligible users. This clarity is crucial for the ranking process. ### Recorded reasoning summary Developing User Representations My thoughts are now centered on how user representations are generated. I understand they must come from interaction history, not a simple user ID lookup, and will be produced by a distinct PyTorch neural network tower. This approach avoids constructing a full interaction matrix. ### run_shell_command · 2026-09-29T05:48:57.249Z { "description": "Check CPU cores and available RAM", "command": "lscpu | grep \"Model name\\|CPU(s):\"; free -h" } ### Tool output · 2026-09-29T05:48:57.279Z Output: CPU(s): 8 Model name: AMD EPYC 7R13 Processor NUMA node0 CPU(s): 0-7 /usr/bin/bash: line 4: free: command not found Exit Code: 127 Process Group PGID: 1618 ## Step 6 · agent · 2026-09-29T05:48:59.901Z ### run_shell_command · 2026-09-29T05:48:59.901Z { "command": "python3 -c \"import psutil, os; print('CPUs:', os.cpu_count(), 'RAM (GB):', round(psutil.virtual_memory().total / (1024**3), 2))\"", "description": "Check available RAM and CPU count via python" } ### Tool output · 2026-09-29T05:48:59.936Z Output: Traceback (most recent call last): File "", line 1, in ModuleNotFoundError: No module named 'psutil' Exit Code: 1 Process Group PGID: 1623 ## Step 7 · agent · 2026-09-29T05:49:01.578Z ### run_shell_command · 2026-09-29T05:49:01.578Z { "command": "cat /proc/meminfo | grep -E \"MemTotal|MemAvailable\"", "description": "Check memory from /proc/meminfo" } ### Tool output · 2026-09-29T05:49:01.594Z Output: MemTotal: 15988968 kB MemAvailable: 14483824 kB Process Group PGID: 1626 ## Step 8 · agent · 2026-09-29T05:49:06.383Z ### Recorded reasoning summary Exploring Data Sources My current focus is on understanding the data. I'm initiating a Python script to examine the dataset located at `/task/data/`. Specifically, I aim to determine the number of unique target users and the total count of items present. ### run_shell_command · 2026-09-29T05:49:06.383Z { "command": "python3 -c \"\nimport pandas as pd\nimport numpy as np\nfrom pathlib import Path\n\ndata_dir = Path('/task/data')\nprint('Files in data dir:', sorted([f.name for f in data_dir.iterdir()]))\n\ntargets = pd.read_parquet(data_dir / 'target_users.parquet')\nprint('Target users count:', len(targets), 'Columns:', targets.columns.tolist())\n\ncatalog = pd.read_parquet(data_dir / 'candidate_catalog.parquet')\nprint('Candidate catalog count:', len(catalog), 'Columns:', catalog.columns.tolist())\n\nfor split in ['train', 'val']:\n for fam in ['listens', 'likes', 'dislikes', 'unlikes', 'undislikes']:\n p = data_dir / f'{split}_{fam}.parquet'\n df = pd.read_parquet(p)\n print(f'{split}_{fam}: {len(df)} rows, uids: {df.uid.nunique()}, items: {df.item_id.nunique()}')\n if 'timestamp' in df.columns and len(df) > 0:\n print(f' min ts: {df.timestamp.min()}, max ts: {df.timestamp.max()}')\n\nval_pref = pd.read_parquet(data_dir / 'validation_preference_state.parquet')\nprint('val_pref:', len(val_pref), val_pref.columns.tolist(), val_pref.head(2).to_dict(orient='records'))\n\ntest_pref = pd.read_parquet(data_dir / 'test_preference_state.parquet')\nprint('test_pref:', len(test_pref), test_pref.columns.tolist(), test_pref.head(2).to_dict(orient='records'))\n\nval_npz = np.load(data_dir / 'validation_eligible_candidates.npz')\nprint('val_npz uids:', len(val_npz['uids']), 'item_ids:', len(val_npz['item_ids']), 'packed shape:', val_npz['packed_eligible'].shape)\n\ntest_npz = np.load(data_dir / 'test_eligible_candidates.npz')\nprint('test_npz uids:', len(test_npz['uids']), 'item_ids:', len(test_npz['item_ids']), 'packed shape:', test_npz['packed_eligible'].shape)\n\"", "description": "Inspect datasets in /task/data" } ### Tool output · 2026-09-29T05:49:09.615Z Output: Files in data dir: ['candidate_catalog.parquet', 'target_users.parquet', 'test_eligible_candidates.npz', 'test_preference_state.parquet', 'train_dislikes.parquet', 'train_likes.parquet', 'train_listens.parquet', 'train_undislikes.parquet', 'train_unlikes.parquet', 'val_dislikes.parquet', 'val_likes.parquet', 'val_listens.parquet', 'val_undislikes.parquet', 'val_unlikes.parquet', 'validation_eligible_candidates.npz', 'validation_preference_state.parquet'] Target users count: 8456 Columns: ['uid', 'recent_positive_listens', 'history_positive_listens'] Candidate catalog count: 30000 Columns: ['item_id', 'positive_listens', 'organic_positive_listens', 'popularity_rank'] train_listens: 43484159 rows, uids: 9238, items: 845395 min ts: 0, max ts: 24786795 train_likes: 814831 rows, uids: 8144, items: 172303 min ts: 80, max ts: 24786790 train_dislikes: 101075 rows, uids: 5732, items: 50876 min ts: 75, max ts: 24786580 train_unlikes: 292665 rows, uids: 6197, items: 113072 min ts: 250, max ts: 24786570 train_undislikes: 19960 rows, uids: 2769, items: 14725 min ts: 15950, max ts: 24785395 val_listens: 1501697 rows, uids: 8196, items: 191747 min ts: 24788600, max ts: 25393395 val_likes: 37795 rows, uids: 4114, items: 22902 min ts: 24788815, max ts: 25393390 val_dislikes: 3563 rows, uids: 955, items: 3135 min ts: 24789920, max ts: 25393280 val_unlikes: 9346 rows, uids: 1313, items: 8080 min ts: 24789005, max ts: 25393385 val_undislikes: 606 rows, uids: 205, items: 579 min ts: 24810715, max ts: 25393035 val_pref: 469124 ['uid', 'item_id', 'state'] [{'uid': 100, 'item_id': 410868, 'state': 'liked'}, {'uid': 100, 'item_id': 732449, 'state': 'liked'}] test_pref: 484776 ['uid', 'item_id', 'state'] [{'uid': 100, 'item_id': 410868, 'state': 'liked'}, {'uid': 100, 'item_id': 732449, 'state': 'liked'}] val_npz uids: 8456 item_ids: 30000 packed shape: (8456, 3750) test_npz uids: 8456 item_ids: 30000 packed shape: (8456, 3750) Process Group PGID: 1630 ## Step 9 · agent · 2026-09-29T05:49:16.721Z ### run_shell_command · 2026-09-29T05:49:16.721Z { "description": "Check file sizes in /task/data", "command": "ls -lh /task/data/" } ### Tool output · 2026-09-29T05:49:16.740Z Output: total 340M -r--r--r-- 1 root root 342K Sep 29 05:30 candidate_catalog.parquet -r--r--r-- 1 root root 83K Sep 29 05:30 target_users.parquet -r--r--r-- 1 root root 7.4M Sep 29 05:30 test_eligible_candidates.npz -r--r--r-- 1 root root 1.1M Sep 29 05:31 test_preference_state.parquet -r--r--r-- 1 root root 838K Sep 29 05:30 train_dislikes.parquet -r--r--r-- 1 root root 5.2M Sep 29 05:30 train_likes.parquet -r--r--r-- 1 root root 305M Sep 29 05:30 train_listens.parquet -r--r--r-- 1 root root 153K Sep 29 05:30 train_undislikes.parquet -r--r--r-- 1 root root 1.8M Sep 29 05:30 train_unlikes.parquet -r--r--r-- 1 root root 36K Sep 29 05:30 val_dislikes.parquet -r--r--r-- 1 root root 292K Sep 29 05:30 val_likes.parquet -r--r--r-- 1 root root 9.5M Sep 29 05:30 val_listens.parquet -r--r--r-- 1 root root 7.7K Sep 29 05:30 val_undislikes.parquet -r--r--r-- 1 root root 71K Sep 29 05:30 val_unlikes.parquet -r--r--r-- 1 root root 7.3M Sep 29 05:30 validation_eligible_candidates.npz -r--r--r-- 1 root root 1.1M Sep 29 05:31 validation_preference_state.parquet Process Group PGID: 1650 ## Step 10 · agent · 2026-09-29T05:49:23.726Z ### Recorded reasoning summary Analyzing Training Data Size I'm currently assessing the `train_listens.parquet` file, which is a manageable 305MB. My focus is on optimizing its loading speed with pyarrow and identifying the essential columns required for training, while also clarifying the meaning of the timestamps. ### Recorded reasoning summary Clarifying Time Gaps I've confirmed the validation and testing windows are precisely seven days each, separated by 30-minute gaps following the training cutoff. I'm now verifying the candidate catalog's item IDs against validation and test sets. ### run_shell_command · 2026-09-29T05:49:23.726Z { "command": "python3 -c \"\nimport pandas as pd\nimport numpy as np\n\ncatalog = pd.read_parquet('/task/data/candidate_catalog.parquet')\nval_npz = np.load('/task/data/validation_eligible_candidates.npz')\ntest_npz = np.load('/task/data/test_eligible_candidates.npz')\ntargets = pd.read_parquet('/task/data/target_users.parquet')\n\nprint('catalog item_ids == val item_ids:', np.array_equal(catalog.item_id.to_numpy(), val_npz['item_ids']))\nprint('catalog item_ids == test item_ids:', np.array_equal(catalog.item_id.to_numpy(), test_npz['item_ids']))\nprint('targets uids == val uids:', np.array_equal(targets.uid.to_numpy(), val_npz['uids']))\nprint('targets uids == test uids:', np.array_equal(targets.uid.to_numpy(), test_npz['uids']))\n\n# check how many eligible candidates per user\n# packed_eligible: 8456 x 3750 (3750 * 8 = 30000 bits)\nval_packed = val_npz['packed_eligible']\nbits = np.unpackbits(val_packed, axis=1)[:, :30000]\nprint('Val eligible per user: min, median, max, mean:', bits.sum(axis=1).min(), np.median(bits.sum(axis=1)), bits.sum(axis=1).max(), bits.sum(axis=1).mean())\n\ntest_packed = test_npz['packed_eligible']\nbits_test = np.unpackbits(test_packed, axis=1)[:, :30000]\nprint('Test eligible per user: min, median, max, mean:', bits_test.sum(axis=1).min(), np.median(bits_test.sum(axis=1)), bits_test.sum(axis=1).max(), bits_test.sum(axis=1).mean())\n\"", "description": "Check catalog and eligibility alignment and statistics" } ### Tool output · 2026-09-29T05:49:26.070Z Output: catalog item_ids == val item_ids: True catalog item_ids == test item_ids: True targets uids == val uids: True targets uids == test uids: True Val eligible per user: min, median, max, mean: 24657 29242.0 30000 29067.306291390727 Test eligible per user: min, median, max, mean: 24552 29220.0 30000 29041.3178807947 Process Group PGID: 1653 ## Step 11 · agent · 2026-09-29T05:49:33.562Z ### Recorded reasoning summary Confirming Data Integrity My analysis confirms the candidate catalog contains 30,000 items, and I've identified 8,456 target users. Notably, each user has access to approximately 29,000 tracks from the catalog. I'm now proceeding to the next verification step. ### run_shell_command · 2026-09-29T05:49:33.562Z { "command": "python3 -c \"\nimport pandas as pd\nimport numpy as np\nfrom pathlib import Path\nfrom ranking_evaluation import CandidateSet\n\ndata_dir = Path('/task/data')\ntargets = pd.read_parquet(data_dir / 'target_users.parquet')\ncatalog = pd.read_parquet(data_dir / 'candidate_catalog.parquet')\nval_npz = np.load(data_dir / 'validation_eligible_candidates.npz')\n\n# catalog is already ordered by training positive-listen popularity!\n# Check:\nprint('Catalog popularity_rank head:', catalog.popularity_rank.head(5).tolist())\n\n# For each user, pick top 100 eligible items in order of popularity\npacked = val_npz['packed_eligible']\nitem_ids = val_npz['item_ids']\nuids = val_npz['uids']\n\nrecords = []\nfor i, uid in enumerate(uids):\n mask = np.unpackbits(packed[i], count=len(item_ids), bitorder='little').astype(bool)\n # top 100 eligible\n eligible_items = item_ids[mask][:100]\n for r, itm in enumerate(eligible_items, 1):\n records.append((uid, itm, r))\n\ndf = pd.DataFrame(records, columns=['uid', 'item_id', 'rank'])\ndf.to_parquet('/tmp/val_pop.parquet')\nprint('Saved /tmp/val_pop.parquet')\n\" && python3 /task/evaluate_public.py /tmp/val_pop.parquet", "description": "Run popularity baseline against validation set" } ### Tool output · 2026-09-29T05:49:35.061Z Output: Traceback (most recent call last): File "", line 5, in ModuleNotFoundError: No module named 'ranking_evaluation' Exit Code: 1 Process Group PGID: 1670 ## Step 12 · agent · 2026-09-29T05:49:40.530Z ### run_shell_command · 2026-09-29T05:49:40.530Z { "description": "Generate popularity baseline and evaluate with public evaluator", "command": "PYTHONPATH=/task python3 -c \"\nimport pandas as pd\nimport numpy as np\nfrom pathlib import Path\n\ndata_dir = Path('/task/data')\ntargets = pd.read_parquet(data_dir / 'target_users.parquet')\ncatalog = pd.read_parquet(data_dir / 'candidate_catalog.parquet')\nval_npz = np.load(data_dir / 'validation_eligible_candidates.npz')\n\n# For each user, pick top 100 eligible items in order of popularity\npacked = val_npz['packed_eligible']\nitem_ids = val_npz['item_ids']\nuids = val_npz['uids']\n\nrecords = []\nfor i, uid in enumerate(uids):\n mask = np.unpackbits(packed[i], count=len(item_ids), bitorder='little').astype(bool)\n # top 100 eligible\n eligible_items = item_ids[mask][:100]\n for r, itm in enumerate(eligible_items, 1):\n records.append((uid, itm, r))\n\ndf = pd.DataFrame(records, columns=['uid', 'item_id', 'rank'])\ndf.to_parquet('/tmp/val_pop.parquet')\nprint('Saved /tmp/val_pop.parquet')\n\" && python3 /task/evaluate_public.py /tmp/val_pop.parquet" } ### Tool output · 2026-09-29T05:49:46.957Z Output: Saved /tmp/val_pop.parquet { "model": "/tmp/val_pop.parquet", "scores": { "quality_ndcg@10": 0.01071, "quality_users": 4263 } } Process Group PGID: 1681 ## Step 13 · agent · 2026-09-29T05:49:55.768Z ### Recorded reasoning summary Reviewing All Specs I'm double-checking all the prompt's specifications and constraints to ensure I have a complete grasp of the task. This includes revisiting the requirement for building a two-tower neural network. ### Recorded reasoning summary Processing Core Constraints I am prioritizing the core functional requirements, particularly the constraint against global user-item matrix factorization. My focus is on developing a scalable, two-tower neural network architecture, as mandated, ensuring independent user and item vector generation for efficient offline computation and request-time retrieval. ### Recorded reasoning summary Implementing User Interactions I'm currently focusing on the user tower's implementation, ensuring it derives representations from interaction history, not just user IDs. This involves an in-depth look at how to effectively aggregate recent interactions, perhaps using attention or GRUs, to create dynamic user embeddings, crucial for handling cold starts and evolving preferences. ### Recorded reasoning summary Refining Recommendation Metrics I'm now integrating the specific relevance grading system, understanding that grade 3 (organic liking) supersedes grade 1 (80% listen). My focus is on accurately calculating graded NDCG@10 using the provided DCG formula, ensuring per-user averaging for robust ranking evaluation. ### Recorded reasoning summary Generating Submission Command I am now focused on constructing the final submission command, ensuring it correctly points to the trained model artifacts and specifies the output directory for predictions, while adhering to all formatting requirements. ### run_shell_command · 2026-09-29T05:49:55.768Z { "description": "Inspect target users and candidate catalog statistics", "command": "python3 -c \"\nimport pandas as pd\nimport numpy as np\n\ndata_dir = pd.read_parquet('/task/data/target_users.parquet')\nprint('target users summary:')\nprint(data_dir.describe())\n\ncatalog = pd.read_parquet('/task/data/candidate_catalog.parquet')\nprint('\\ncandidate catalog summary:')\nprint(catalog.describe())\n\n# Check how many catalog items are in train_listens / train_likes\nprint('\\nCatalog item id range:', catalog.item_id.min(), catalog.item_id.max())\n\"" } ### Tool output · 2026-09-29T05:49:57.368Z Output: target users summary: uid recent_positive_listens history_positive_listens count 8456.000000 8456.000000 8456.000000 mean 502289.108325 1251.076750 3236.440752 std 287953.395912 1414.340904 3697.575286 min 100.000000 20.000000 20.000000 25% 254375.000000 281.000000 630.000000 50% 503850.000000 784.500000 1920.500000 75% 751725.000000 1706.250000 4494.250000 max 1000000.000000 19592.000000 26597.000000 candidate catalog summary: item_id ... popularity_rank count 3.000000e+04 ... 30000.000000 mean 4.698278e+06 ... 15000.500000 std 2.700274e+06 ... 8660.398374 min 2.060000e+02 ... 1.000000 25% 2.382368e+06 ... 7500.750000 50% 4.687992e+06 ... 15000.500000 75% 7.040711e+06 ... 22500.250000 max 9.390294e+06 ... 30000.000000 [8 rows x 4 columns] Catalog item id range: 206 9390294 Process Group PGID: 1722 ## Step 14 · agent · 2026-09-29T05:50:07.602Z ### Recorded reasoning summary Exploring User Data Differences I'm analyzing the discrepancies between our target user base and the broader `train_listens` dataset. Specifically, I'm verifying if any users present in `train_listens` are absent from the `target_users` group, a crucial step for understanding our user segmentation. ### Recorded reasoning summary Verifying Target User Overlap I'm digging into the temporal overlap of `train_listens` with our target user set. My current focus is to isolate listens within specific recent windows (90, 30, and 14 days) preceding the maximum timestamp and assess their intersection with `target_users`. I'm also examining the validation truth mechanisms, specifically `val_likes` and `val_listens`, to understand how ground truth is established. ### run_shell_command · 2026-09-29T05:50:07.602Z { "description": "Check validation ground truth statistics", "command": "python3 -c \"\nimport pandas as pd\nfrom ranking_evaluation import CandidateSet\nfrom evaluate_public import validation_truth\nfrom pathlib import Path\n\ndata_dir = Path('/task/data')\ntargets = set(pd.read_parquet(data_dir / 'target_users.parquet').uid.astype(int))\nelig = CandidateSet(data_dir / 'validation_eligible_candidates.npz')\ntruth = validation_truth(elig, targets)\n\ntotal_items = sum(len(items) for items in truth.values())\ngrade_counts = {}\nfor items in truth.values():\n for g in items.values():\n grade_counts[g] = grade_counts.get(g, 0) + 1\n\nprint('Users with truth:', len(truth))\nprint('Total (user, item) relevant pairs:', total_items)\nprint('Grade counts:', grade_counts)\nprint('Items per user in truth: min, median, max, mean:', \n min(len(v) for v in truth.values()), \n sorted([len(v) for v in truth.values()])[len(truth)//2],\n max(len(v) for v in truth.values()),\n total_items / len(truth))\n\"" } ### Tool output · 2026-09-29T05:50:09.111Z Output: Traceback (most recent call last): File "", line 3, in ModuleNotFoundError: No module named 'ranking_evaluation' Exit Code: 1 Process Group PGID: 1739 ## Step 15 · agent · 2026-09-29T05:50:17.443Z ### run_shell_command · 2026-09-29T05:50:17.443Z { "command": "PYTHONPATH=/task python3 -c \"\nimport pandas as pd\nfrom ranking_evaluation import CandidateSet\nfrom evaluate_public import validation_truth\nfrom pathlib import Path\n\ndata_dir = Path('/task/data')\ntargets = set(pd.read_parquet(data_dir / 'target_users.parquet').uid.astype(int))\nelig = CandidateSet(data_dir / 'validation_eligible_candidates.npz')\ntruth = validation_truth(elig, targets)\n\ntotal_items = sum(len(items) for items in truth.values())\ngrade_counts = {}\nfor items in truth.values():\n for g in items.values():\n grade_counts[g] = grade_counts.get(g, 0) + 1\n\nprint('Users with truth:', len(truth))\nprint('Total (user, item) relevant pairs:', total_items)\nprint('Grade counts:', grade_counts)\nprint('Items per user in truth: min, median, max, mean:', \n min(len(v) for v in truth.values()), \n sorted([len(v) for v in truth.values()])[len(truth)//2],\n max(len(v) for v in truth.values()),\n total_items / len(truth))\n\"", "description": "Check validation ground truth statistics with PYTHONPATH" } ### Tool output · 2026-09-29T05:50:19.553Z Output: Users with truth: 4263 Total (user, item) relevant pairs: 37769 Grade counts: {1: 33352, 3: 4417} Items per user in truth: min, median, max, mean: 1 4 442 8.859723199624677 Process Group PGID: 1750 ## Step 16 · agent · 2026-09-29T05:50:30.368Z ### Recorded reasoning summary Analyzing Eligible Items I've identified that a significant portion of our target users, 4263 out of 8456, possess eligible items within the defined validation window. This translates to 37,769 relevant pairs, with a breakdown of 33,352 grade 1 and 4,417 grade 3. My next step is to investigate the specific characteristics of these pairs further. ### run_shell_command · 2026-09-29T05:50:30.368Z { "command": "PYTHONPATH=/task python3 -c \"\nimport pandas as pd\nfrom ranking_evaluation import CandidateSet\nfrom evaluate_public import validation_truth\nfrom pathlib import Path\n\ndata_dir = Path('/task/data')\ntargets = set(pd.read_parquet(data_dir / 'target_users.parquet').uid.astype(int))\nelig = CandidateSet(data_dir / 'validation_eligible_candidates.npz')\ntruth = validation_truth(elig, targets)\n\n# Let's check likes and recent listens\nlikes = pd.read_parquet(data_dir / 'train_likes.parquet')\nlikes_pairs = set(zip(likes.uid, likes.item_id))\n\npref = pd.read_parquet(data_dir / 'validation_preference_state.parquet')\nliked_pref_pairs = set(zip(pref[pref.state == 'liked'].uid, pref[pref.state == 'liked'].item_id))\n\ntruth_pairs = [(u, itm) for u, itms in truth.items() for itm in itms]\nin_likes = sum((u, itm) in likes_pairs for u, itm in truth_pairs)\nin_pref = sum((u, itm) in liked_pref_pairs for u, itm in truth_pairs)\n\nprint(f'Total truth pairs: {len(truth_pairs)}')\nprint(f'In train_likes: {in_likes} ({in_likes / len(truth_pairs):.2%})')\nprint(f'In val_pref liked: {in_pref} ({in_pref / len(truth_pairs):.2%})')\n\"", "description": "Check repeat rate of validation truth against likes and preference state" } ### Tool output · 2026-09-29T05:50:33.757Z Output: Total truth pairs: 37769 In train_likes: 0 (0.00%) In val_pref liked: 0 (0.00%) Process Group PGID: 1770 ## Step 17 · agent · 2026-09-29T05:50:37.332Z ### Recorded reasoning summary Investigating Filtering Logic I'm currently digging into the candidate generation process. Specifically, I'm perplexed by zero `in_pref` and `in_likes` values and want to verify if already liked tracks are being excluded from the `validation_eligible_candidates.npz` pool. My next step is to examine the filtering mechanisms. ### run_shell_command · 2026-09-29T05:50:37.332Z { "command": "PYTHONPATH=/task python3 -c \"\nimport pandas as pd\nfrom ranking_evaluation import CandidateSet\nfrom pathlib import Path\n\ndata_dir = Path('/task/data')\nelig = CandidateSet(data_dir / 'validation_eligible_candidates.npz')\npref = pd.read_parquet(data_dir / 'validation_preference_state.parquet')\n\n# Check if any (uid, item_id) in pref is eligible\nliked = pref[pref.state == 'liked']\ndisliked = pref[pref.state == 'disliked']\n\nliked_elig = sum(elig.is_eligible(int(r.uid), int(r.item_id)) for r in liked.itertuples())\ndisliked_elig = sum(elig.is_eligible(int(r.uid), int(r.item_id)) for r in disliked.itertuples())\n\nprint('Total liked in pref:', len(liked), 'Eligible:', liked_elig)\nprint('Total disliked in pref:', len(disliked), 'Eligible:', disliked_elig)\n\"", "description": "Check if preference state items are eligible" } ### Tool output · 2026-09-29T05:50:39.800Z Output: Total liked in pref: 418435 Eligible: 0 Total disliked in pref: 50689 Eligible: 0 Process Group PGID: 1790 ## Step 18 · agent · 2026-09-29T05:50:54.714Z ### Recorded reasoning summary Analyzing Eligibility Filters My current focus is on understanding how liked and disliked items are being excluded by platform filters. It appears that all items I've previously indicated a preference for are currently ineligible for discovery. ### run_shell_command · 2026-09-29T05:50:54.714Z { "command": "PYTHONPATH=/task python3 -c \"\nimport pandas as pd\nfrom ranking_evaluation import CandidateSet\nfrom evaluate_public import validation_truth\nfrom pathlib import Path\n\ndata_dir = Path('/task/data')\ntargets = set(pd.read_parquet(data_dir / 'target_users.parquet').uid.astype(int))\nelig = CandidateSet(data_dir / 'validation_eligible_candidates.npz')\ntruth = validation_truth(elig, targets)\n\n# Let's sample train_listens for target users in recent window\n# Read pyarrow dataset or parquet metadata\nimport pyarrow.parquet as pq\nimport pyarrow.compute as pc\n\ntable = pq.read_table(\n data_dir / 'train_listens.parquet',\n columns=['uid', 'item_id', 'timestamp'],\n filters=[('timestamp', '>=', 24786800 - 30 * 86400)]\n)\nprint('Recent 30-day listens:', table.num_rows)\nrecent_df = table.to_pandas()\nrecent_pairs = set(zip(recent_df.uid, recent_df.item_id))\n\ntruth_pairs = [(u, itm) for u, itms in truth.items() for itm in itms]\nin_recent_listens = sum((u, itm) in recent_pairs for u, itm in truth_pairs)\n\nprint(f'Truth pairs in recent 30-day listens: {in_recent_listens} ({in_recent_listens / len(truth_pairs):.2%})')\n\n# Check how many of recent listens are eligible\nsample_recent = recent_df.sample(min(10000, len(recent_df)), random_state=42)\nrecent_elig = sum(elig.is_eligible(int(r.uid), int(r.item_id)) for r in sample_recent.itertuples())\nprint(f'Sample of recent listens eligible: {recent_elig} / {len(sample_recent)} ({recent_elig / len(sample_recent):.2%})')\n\"", "description": "Check if recent listens are in truth and if they are eligible" } ### Tool output · 2026-09-29T05:51:01.181Z Output: Recent 30-day listens: 6126103 Truth pairs in recent 30-day listens: 0 (0.00%) Sample of recent listens eligible: 0 / 10000 (0.00%) Process Group PGID: 1808 ## Step 19 · agent · 2026-09-29T05:51:09.317Z ### Recorded reasoning summary Refining Candidate Eligibility I'm currently focusing on refining how I determine eligible candidates for recommendations. My immediate goal is to ensure I'm not presenting items the user has already engaged with, thereby maximizing the novelty and relevance of suggestions. ### Recorded reasoning summary Filtering Unseen Content My current thinking is zeroing in on ensuring recommendations strictly exclude items a user has already interacted with, whether positively or negatively. This dramatically narrows the pool of eligible items, but it's a crucial step for delivering truly novel suggestions. ### Recorded reasoning summary Prioritizing Latency Constraints I'm now considering the tight latency budget for the production shelf. My focus is on how to efficiently manage per-user candidate eligibility and preference states to meet these real-time demands without sacrificing recommendation quality. ### run_shell_command · 2026-09-29T05:51:09.317Z { "description": "Check catalog and listen table columns", "command": "python3 -c \"\nimport pandas as pd\n\ncat = pd.read_parquet('/task/data/candidate_catalog.parquet')\nprint('cat columns:', cat.columns.tolist(), cat.dtypes)\nprint(cat.head(5))\n\nlistens = pd.read_parquet('/task/data/val_listens.parquet')\nprint('listens columns:', listens.columns.tolist(), listens.dtypes)\nprint(listens.head(5))\n\"" } ### Tool output · 2026-09-29T05:51:10.911Z Output: cat columns: ['item_id', 'positive_listens', 'organic_positive_listens', 'popularity_rank'] item_id uint32 positive_listens int64 organic_positive_listens uint64 popularity_rank int32 dtype: object item_id positive_listens organic_positive_listens popularity_rank 0 5862961 26615 20620 1 1 6901374 25536 17347 2 2 5635052 24691 17194 3 3 3542184 24075 17699 4 4 9378983 24036 18195 5 listens columns: ['uid', 'timestamp', 'item_id', 'is_organic', 'played_ratio_pct', 'track_length_seconds'] uid uint32 timestamp uint32 item_id uint32 is_organic uint8 played_ratio_pct uint16 track_length_seconds uint32 dtype: object uid timestamp item_id is_organic played_ratio_pct track_length_seconds 0 100 24943430 9321269 0 100 195 1 100 24943605 5452713 0 100 175 2 100 24943765 6448777 0 100 160 3 100 24951960 6008056 0 8 175 4 100 25246895 7968587 0 45 565 Process Group PGID: 1836 ## Step 20 · agent · 2026-09-29T05:51:17.905Z ### Recorded reasoning summary Verifying Candidate Catalog I'm currently confirming that all items from `val_npz['item_ids']` and `test_npz['item_ids']` are indeed present within the existing candidate catalog of 30,000 items, a check that has already been validated. ### run_shell_command · 2026-09-29T05:51:17.905Z { "description": "Check proportion of catalog items in train listens and likes", "command": "python3 -c \"\nimport pandas as pd\nimport pyarrow.parquet as pq\n\ncat = pd.read_parquet('/task/data/candidate_catalog.parquet')\ncat_items = set(cat.item_id)\n\ntable = pq.read_table('/task/data/train_listens.parquet', columns=['uid', 'item_id', 'timestamp', 'played_ratio_pct', 'is_organic'])\ndf = table.to_pandas()\nprint('Total train_listens:', len(df))\nin_cat = df.item_id.isin(cat_items)\nprint('train_listens in cat:', in_cat.sum(), f'({in_cat.mean():.2%})')\n\n# In recent 90 days:\ncutoff = 24786800\nrecent_90 = df[df.timestamp >= cutoff - 90 * 86400]\nprint('Recent 90d listens:', len(recent_90), 'in cat:', recent_90.item_id.isin(cat_items).mean())\n\n# In recent 30 days:\nrecent_30 = df[df.timestamp >= cutoff - 30 * 86400]\nprint('Recent 30d listens:', len(recent_30), 'in cat:', recent_30.item_id.isin(cat_items).mean())\n\n# Check train_likes\nlikes = pd.read_parquet('/task/data/train_likes.parquet')\nprint('train_likes:', len(likes), 'in cat:', likes.item_id.isin(cat_items).mean())\n\"" } ### Tool output · 2026-09-29T05:51:25.425Z Output: Total train_listens: 43484159 train_listens in cat: 31249613 (71.86%) Recent 90d listens: 16769274 in cat: 0.7226290774424701 Recent 30d listens: 6126103 in cat: 0.7098610650196381 train_likes: 814831 in cat: 0.6809068383505291 Process Group PGID: 1856 ## Step 21 · agent · 2026-09-29T05:51:48.500Z ### Recorded reasoning summary Analyzing Catalog Performance My current focus is on understanding the strong engagement within our existing catalog, noting that approximately 70% of listens and likes come from the 30,000 catalog items. I'm now shifting to examining the target objective and evaluation window, particularly how validation labels are defined within the public validation window. ### Recorded reasoning summary Refining Evaluation Metrics I'm now scrutinizing the validation setup, specifically how we've defined the grades within the public validation window. It appears all relevant validation outcomes are indeed within the 30,000 candidate catalog items. My next step is to thoroughly re-examine the two-tower neural network architecture to ensure alignment with the user and item vector decomposition requirements. ### Recorded reasoning summary Developing Item Tower I am now focusing on the item tower's role in producing item vectors, specifically considering its PyTorch implementation. My aim is to ensure this component aligns with the requirements for generating embeddings for candidate items. ### Recorded reasoning summary Investigating Item Embeddings I'm confirming if the item tower can utilize item ID embeddings, as opposed to deriving representations solely from interaction history like the user tower. The prompt contrasts these approaches, suggesting a difference in how user and item vectors are generated. ### Recorded reasoning summary Generating Item Vectors I am confirming that the item tower will indeed generate item vectors from item inputs like ID or features via PyTorch. This contrasts with the user tower, which derives its vector from interaction history. ### Recorded reasoning summary Aggregating User Interactions I'm now analyzing how the user tower processes interaction history. I'm exploring aggregation strategies like average pooling, attention mechanisms, and self-attention layers, followed by an MLP, to generate user vectors. This will ensure comprehensive representation based on recent interactions and potentially summary features. ### Recorded reasoning summary Confirming Dot Product Use I'm confirming that the user and item embeddings will indeed be dot-producted for relevance scoring, as stated. This approach, $\text{score}(u, i) = \langle u, v_i \rangle$, enables precomputation of item vectors and efficient inference via matrix multiplication, ensuring low latency even with millions of users. ### Recorded reasoning summary Evaluating Training Strategy My focus has shifted to the training methodology. I'm confirming that in-batch negative sampling, rather than constructing a full interaction matrix, is the appropriate approach for this production-scale system. This aligns with common industry practices for two-tower retrieval models. ### run_shell_command · 2026-09-29T05:51:48.500Z { "command": "python3 -c \"\nimport pandas as pd\n\nvpref = pd.read_parquet('/task/data/validation_preference_state.parquet')\nprint('vpref shape:', vpref.shape)\nprint('vpref state value counts:\\n', vpref.state.value_counts())\nprint('vpref unique uids:', vpref.uid.nunique())\n\ntpref = pd.read_parquet('/task/data/test_preference_state.parquet')\nprint('tpref shape:', tpref.shape)\nprint('tpref state value counts:\\n', tpref.state.value_counts())\nprint('tpref unique uids:', tpref.uid.nunique())\n\"", "description": "Check preference state files" } ### Tool output · 2026-09-29T05:51:50.123Z Output: vpref shape: (469124, 3) vpref state value counts: state liked 418435 disliked 50689 Name: count, dtype: int64 vpref unique uids: 7038 tpref shape: (484776, 3) tpref state value counts: state liked 432659 disliked 52117 Name: count, dtype: int64 tpref unique uids: 7092 Process Group PGID: 1878 ## Step 22 · agent · 2026-09-29T05:51:57.391Z ### Recorded reasoning summary Analyzing User Preference Data My current focus is on user preference data. I've observed a substantial volume of liked and disliked pairs for both validation and testing sets, with a significant portion of target users contributing. I am now investigating the specific data availability for each user. ### Recorded reasoning summary Determining Data Timelines I'm now segmenting the data based on the validation and testing cutoffs. For validation, I'm considering all training interactions up to timestamp 24786800, along with the preference state at that point. The test set encompasses all interactions up to 25393400. ### run_shell_command · 2026-09-29T05:51:57.391Z { "command": "python3 -c \"\nimport pyarrow.parquet as pq\n\ncutoff = 24786800\nt_7d = cutoff - 7 * 86400\ntable = pq.read_table('/task/data/train_listens.parquet', columns=['timestamp'], filters=[('timestamp', '>=', t_7d)])\nprint('7-day train listens:', table.num_rows)\n\"", "description": "Check 7-day train listens count" } ### Tool output · 2026-09-29T05:51:59.235Z Output: 7-day train listens: 1491699 Process Group PGID: 1896 ## Step 23 · agent · 2026-09-29T05:52:11.173Z ### Recorded reasoning summary Observing Listen Rates The recent data shows a consistent generation rate of approximately 1.5 million listens weekly. I'm now analyzing how this can best inform our training data selection for optimal model performance. ### Recorded reasoning summary Refining User Tower Inputs I'm focusing on how to best construct the user tower. The mandate specifies using interaction history, not user ID lookups, so I'm currently considering the best signals from past listens and likes to represent user preferences effectively for the two-tower model's retrieval and ranking objectives. ### Recorded reasoning summary Designing User Tower Components My current focus is on translating the interaction history into a robust user representation. I'm evaluating how to best incorporate sequences of listens and sets of likes/dislikes, while carefully considering the implications of items outside the primary 30,000-track catalog. ### Recorded reasoning summary Incorporating Item Indexing I've finalized the item indexing strategy, accommodating catalog items and an out-of-catalog token within a manageable embedding table size. This allows for efficient handling of all user interactions moving forward. ### run_shell_command · 2026-09-29T05:52:11.173Z { "description": "Analyze target users history counts for candidate catalog items", "command": "PYTHONPATH=/task python3 -c \"\nimport pandas as pd\nfrom ranking_evaluation import CandidateSet\nfrom evaluate_public import validation_truth\nfrom pathlib import Path\n\ndata_dir = Path('/task/data')\ntargets = set(pd.read_parquet(data_dir / 'target_users.parquet').uid.astype(int))\nelig = CandidateSet(data_dir / 'validation_eligible_candidates.npz')\ntruth = validation_truth(elig, targets)\n\n# Check user interaction history length in candidate catalog\ncatalog = pd.read_parquet(data_dir / 'candidate_catalog.parquet')\ncat_items = set(catalog.item_id)\n\nlikes = pd.read_parquet(data_dir / 'train_likes.parquet')\nlikes_target = likes[likes.uid.isin(targets) & likes.item_id.isin(cat_items)]\nuser_likes_count = likes_target.groupby('uid').size()\nprint('Catalog likes per target user: mean', user_likes_count.mean(), 'median', user_likes_count.median(), 'min', user_likes_count.min(), 'max', user_likes_count.max(), 'users with >=1 like:', len(user_likes_count))\n\npref = pd.read_parquet(data_dir / 'validation_preference_state.parquet')\npref_liked = pref[(pref.state == 'liked') & pref.uid.isin(targets) & pref.item_id.isin(cat_items)]\npref_liked_count = pref_liked.groupby('uid').size()\nprint('Val pref liked per target user: mean', pref_liked_count.mean(), 'median', pref_liked_count.median(), 'users with >=1:', len(pref_liked_count))\n\"" } ### Tool output · 2026-09-29T05:52:13.501Z Output: Catalog likes per target user: mean 68.79386092522549 median 31.0 min 1 max 1680 users with >=1 like: 6874 Val pref liked per target user: mean 61.816368739843405 median 28.0 users with >=1: 6769 Process Group PGID: 1924 ## Step 24 · agent · 2026-09-29T05:52:36.025Z ### Recorded reasoning summary Analyzing User Engagement Metrics I'm currently dissecting user activity, focusing on the 6,874 individuals with at least one liked item, observing a median of 31 likes and a mean of about 69. My next step is to broaden this analysis to encompass all 8,456 target users. ### run_shell_command · 2026-09-29T05:52:36.025Z { "command": "PYTHONPATH=/task python3 -c \"\nimport pandas as pd\nimport pyarrow.parquet as pq\nfrom pathlib import Path\n\ndata_dir = Path('/task/data')\ntargets = set(pd.read_parquet(data_dir / 'target_users.parquet').uid.astype(int))\ncatalog = pd.read_parquet(data_dir / 'candidate_catalog.parquet')\ncat_items = set(catalog.item_id)\n\ncutoff = 24786800\ntable = pq.read_table(\n data_dir / 'train_listens.parquet',\n columns=['uid', 'item_id', 'timestamp', 'played_ratio_pct', 'is_organic'],\n filters=[('timestamp', '>=', cutoff - 30 * 86400)]\n)\ndf = table.to_pandas()\ndf_targets = df[df.uid.isin(targets) & df.item_id.isin(cat_items)]\nuser_listens_count = df_targets.groupby('uid').size()\nprint('Target users with catalog listens in last 30d:', len(user_listens_count), 'out of', len(targets))\nprint('Listens per user in last 30d: median', user_listens_count.median(), 'mean', user_listens_count.mean())\n\"", "description": "Check catalog listens in last 30d for target users" } ### Tool output · 2026-09-29T05:52:38.922Z Output: Target users with catalog listens in last 30d: 8196 out of 8456 Listens per user in last 30d: median 338.0 mean 529.4548560273304 Process Group PGID: 1944 ## Step 25 · agent · 2026-09-29T05:53:10.492Z ### Recorded reasoning summary Analyzing User Engagement My current focus is on understanding user listening patterns. Over the last month, an impressive 97% of target users engaged with content, averaging over 500 listens per user. Looking at the last 90 days, nearly all target users have interacted with the catalog. Next, I'm diving into the two-tower model. ### Recorded reasoning summary Developing Two-Tower Model I've been focusing on the implementation of the two-tower architecture. Specifically, I'm exploring how to represent the item tower using PyTorch's `nn.Module`, considering both item identifiers and potential item features from our catalog data. The goal is to generate item embedding vectors efficiently. ### Recorded reasoning summary Incorporating Item Features I'm considering enhancing the item tower by incorporating features like positive listen counts and popularity rank. The core question is whether combining these with the item ID embedding before an MLP yields better results than using the ID embedding alone. Testing both approaches is feasible given the model's compact parameter size. ### Recorded reasoning summary Processing User History I am currently analyzing how to best represent a user's listening history within the user tower. I'm considering using item embeddings from history, potentially weighted by factors like listen completion or recency, and exploring different pooling mechanisms like weighted averages, attention, or sequential models. I'm also thinking about how to incorporate user-level features. ### Recorded reasoning summary Designing User Tower I am currently working on the user tower's structure. The plan involves feeding item embeddings from a user's history through a dedicated MLP. The final user vector and item vector will then interact via a dot product to determine relevance. ### Recorded reasoning summary Optimizing Training Approach I'm now focusing on the training methodology for the two-tower model. My current analysis centers on in-batch sampled softmax (InfoNCE), leveraging the inherent structure of our data to create effective negative samples for learning. I'm exploring how to best utilize these within the training loop. ### Recorded reasoning summary Evaluating Loss Functions I am now evaluating different loss functions for training. I'm specifically comparing sampled softmax with popularity correction against Binary Cross-Entropy and BPR. The goal is to determine how to best penalize less relevant items while accounting for inherent popularity biases. ### Recorded reasoning summary Analyzing Training Samples I am currently examining how positive training samples are formed, specifically considering the temporal cutoffs for user history and the evaluation window. My analysis focuses on understanding the exact definition of ground truth, which includes organic likes and organic interactions within the specified period. This is crucial for aligning training with the evaluation metric of graded NDCG@10. ### Recorded reasoning summary Forming Training Instances I'm now investigating how to construct training instances from our data, focusing on temporal windows. I'm considering using multiple cutoff points within the 287 days of training data to define user history and subsequent target outcomes, specifically focusing on organic likes and listens above 80% within defined 7-day periods. ### run_shell_command · 2026-09-29T05:53:10.492Z { "description": "Check strong organic interactions in 7 days before cutoff", "command": "PYTHONPATH=/task python3 -c \"\nimport pandas as pd\nimport pyarrow.parquet as pq\n\ncutoff = 24786800\nt_7d = cutoff - 7 * 86400\n\nlistens_table = pq.read_table(\n '/task/data/train_listens.parquet',\n columns=['uid', 'item_id', 'timestamp', 'played_ratio_pct', 'is_organic'],\n filters=[('timestamp', '>=', t_7d)]\n)\nlistens_df = listens_table.to_pandas()\nstrong_listens = listens_df[(listens_df.is_organic == 1) & (listens_df.played_ratio_pct >= 80)]\n\nlikes_table = pq.read_table(\n '/task/data/train_likes.parquet',\n columns=['uid', 'item_id', 'timestamp', 'is_organic'],\n filters=[('timestamp', '>=', t_7d)]\n)\nlikes_df = likes_table.to_pandas()\norganic_likes = likes_df[likes_df.is_organic == 1]\n\nprint('7d before cutoff strong organic listens:', len(strong_listens), 'unique users:', strong_listens.uid.nunique(), 'unique items:', strong_listens.item_id.nunique())\nprint('7d before cutoff organic likes:', len(organic_likes), 'unique users:', organic_likes.uid.nunique(), 'unique items:', organic_likes.item_id.nunique())\n\"" } ### Tool output · 2026-09-29T05:53:12.422Z Output: 7d before cutoff strong organic listens: 428612 unique users: 6172 unique items: 86338 7d before cutoff organic likes: 17201 unique users: 3232 unique items: 11973 Process Group PGID: 1972 ## Step 26 · agent · 2026-09-29T05:53:22.837Z ### run_shell_command · 2026-09-29T05:53:22.837Z { "command": "PYTHONPATH=/task python3 -c \"\nimport pandas as pd\nimport pyarrow.parquet as pq\n\ncutoff = 24786800\nt_7d = cutoff - 7 * 86400\ncatalog = pd.read_parquet('/task/data/candidate_catalog.parquet')\ncat_items = set(catalog.item_id)\n\nlistens_table = pq.read_table(\n '/task/data/train_listens.parquet',\n columns=['uid', 'item_id', 'timestamp', 'played_ratio_pct', 'is_organic'],\n filters=[('timestamp', '>=', t_7d)]\n)\nlistens_df = listens_table.to_pandas()\nstrong_listens = listens_df[(listens_df.is_organic == 1) & (listens_df.played_ratio_pct >= 80) & listens_df.item_id.isin(cat_items)]\n\nlikes_table = pq.read_table(\n '/task/data/train_likes.parquet',\n columns=['uid', 'item_id', 'timestamp', 'is_organic'],\n filters=[('timestamp', '>=', t_7d)]\n)\nlikes_df = likes_table.to_pandas()\norganic_likes = likes_df[(likes_df.is_organic == 1) & likes_df.item_id.isin(cat_items)]\n\nprint('7d in-catalog strong organic listens:', len(strong_listens), 'unique users:', strong_listens.uid.nunique(), 'unique items:', strong_listens.item_id.nunique())\nprint('7d in-catalog organic likes:', len(organic_likes), 'unique users:', organic_likes.uid.nunique(), 'unique items:', organic_likes.item_id.nunique())\n\"", "description": "Check in-catalog strong organic interactions in 7 days before cutoff" } ### Tool output · 2026-09-29T05:53:24.888Z Output: 7d in-catalog strong organic listens: 305188 unique users: 5910 unique items: 25543 7d in-catalog organic likes: 11210 unique users: 2643 unique items: 6437 Process Group PGID: 1994 ## Step 27 · agent · 2026-09-29T05:53:28.286Z ### Recorded reasoning summary Analyzing Listener Growth My focus is on extrapolating current performance. I'm analyzing listen and like growth over 7-day periods and planning to assess sample availability for longer durations before the cutoff. ### run_shell_command · 2026-09-29T05:53:28.286Z { "command": "PYTHONPATH=/task python3 -c \"\nimport pandas as pd\nimport pyarrow.parquet as pq\n\ncutoff = 24786800\ncatalog = pd.read_parquet('/task/data/candidate_catalog.parquet')\ncat_items = set(catalog.item_id)\n\nfor days in [7, 14, 28]:\n t_start = cutoff - days * 86400\n listens_table = pq.read_table(\n '/task/data/train_listens.parquet',\n columns=['uid', 'item_id', 'timestamp', 'played_ratio_pct', 'is_organic'],\n filters=[('timestamp', '>=', t_start)]\n )\n listens_df = listens_table.to_pandas()\n strong = listens_df[(listens_df.is_organic == 1) & (listens_df.played_ratio_pct >= 80) & listens_df.item_id.isin(cat_items)]\n print(f'{days}d strong listens in cat:', len(strong), 'users:', strong.uid.nunique())\n\"", "description": "Check strong listens for 7, 14, 28 days" } ### Tool output · 2026-09-29T05:53:31.850Z Output: 7d strong listens in cat: 305188 users: 5910 14d strong listens in cat: 594362 users: 6711 28d strong listens in cat: 1170755 users: 7405 Process Group PGID: 2016 ## Step 28 · agent · 2026-09-29T05:53:37.687Z ### Recorded reasoning summary Testing PyTorch Speed I'm currently evaluating PyTorch training performance on this CPU, aiming to quantify how quickly we can process large datasets, specifically for sample sizes like 500,000 and 1,000,000. This is in relation to the observed 1.17 million listens from 7,405 users in the last 28 days. ### run_shell_command · 2026-09-29T05:53:37.687Z { "description": "Benchmark PyTorch training speed on CPU", "command": "python3 -c \"\nimport torch\nimport torch.nn as nn\nimport time\n\nprint('PyTorch version:', torch.__version__, 'threads:', torch.get_num_threads())\ntorch.set_num_threads(8)\n\nembed_dim = 64\nbatch_size = 512\nnum_items = 30001\n\nitem_emb = nn.Embedding(num_items, embed_dim)\nuser_mlp = nn.Sequential(\n nn.Linear(embed_dim, embed_dim),\n nn.ReLU(),\n nn.Linear(embed_dim, embed_dim)\n)\n\nopt = torch.optim.Adam(list(item_emb.parameters()) + list(user_mlp.parameters()), lr=1e-3)\ncrit = nn.CrossEntropyLoss()\n\n# Simulate a batch:\n# User history: B users, each has L=32 item ids, pooled\nL = 32\nuser_hist = torch.randint(0, num_items, (batch_size, L))\npos_items = torch.randint(0, num_items, (batch_size,))\n\nstart = time.time()\nn_batches = 100\nfor _ in range(n_batches):\n opt.zero_grad()\n # User tower: embed history items, mean pool, pass through MLP\n hist_vecs = item_emb(user_hist).mean(dim=1)\n u_vecs = user_mlp(hist_vecs) # (B, d)\n \n # Item tower: embed pos items\n v_vecs = item_emb(pos_items) # (B, d)\n \n # In-batch logits: (B, B)\n logits = torch.matmul(u_vecs, v_vecs.t())\n targets = torch.arange(batch_size)\n loss = crit(logits, targets)\n loss.backward()\n opt.step()\n\ndur = time.time() - start\ntotal_samples = n_batches * batch_size\nprint(f'Trained {total_samples} samples in {dur:.2f}s ({total_samples / dur:.1f} samples/sec)')\n\"" } ### Tool output · 2026-09-29T05:53:44.731Z Output: PyTorch version: 2.8.0+cpu threads: 4 Trained 51200 samples in 1.24s (41161.3 samples/sec) Process Group PGID: 2038 ## Step 29 · agent · 2026-09-29T05:53:56.532Z ### Recorded reasoning summary Processing Performance Improvements My recent focus has been on optimizing processing speeds. I've observed impressive results with the CPU achieving 41,000 samples per second, leading to roughly 12-second epochs for 500,000 samples. I'm now verifying these figures and investigating inference times, particularly for full catalog scoring across 8456 target users. ### run_shell_command · 2026-09-29T05:53:56.532Z { "command": "python3 -c \"\nimport torch\nimport time\n\ntorch.set_num_threads(8)\nU = torch.randn(8456, 64)\nV = torch.randn(30000, 64)\n\nstart = time.time()\nscores = torch.matmul(U, V.t())\n# top 100\ntopk_vals, topk_inds = torch.topk(scores, 100, dim=1)\ndur = time.time() - start\nprint(f'Full catalog MIPS + topk for 8456 users took {dur:.3f}s!')\n\"", "description": "Benchmark full catalog scoring and topk for all 8456 users" } ### Tool output · 2026-09-29T05:53:59.996Z Output: Full catalog MIPS + topk for 8456 users took 0.374s! Process Group PGID: 2062 ## Step 30 · agent · 2026-09-29T05:54:03.119Z ### Recorded reasoning summary Optimizing Ranker Performance I'm thrilled by the speed of the full catalog scoring and top-100 selection, completing in a mere 0.37 seconds. This efficiency will revolutionize our validation and testing. My current focus is on dissecting the factors contributing to the best ranker and identifying architectural elements, signals, and objectives that will significantly enhance graded NDCG@10. ### update_topic · 2026-09-29T05:54:03.119Z { "strategic_intent": "Analyze user history signals, design features for the two-tower model, and prepare the temporal backtest data.", "title": "Feature Engineering & Architecture Design", "summary": "Completed initial exploration: confirmed data alignment, candidate catalog of 30,000 items, 8456 target users, and fast PyTorch CPU performance (~41k samples/sec). Popularity baseline gave NDCG@10 of 0.01071. Now investigating user history representations, explicit preference states, temporal splitting, and two-tower architectures." } ### Tool output · 2026-09-29T05:54:03.122Z Current topic: "Feature Engineering & Architecture Design" Topic summary: Completed initial exploration: confirmed data alignment, candidate catalog of 30,000 items, 8456 target users, and fast PyTorch CPU performance (~41k samples/sec). Popularity baseline gave NDCG@10 of 0.01071. Now investigating user history representations, explicit preference states, temporal splitting, and two-tower architectures. Strategic Intent: Analyze user history signals, design features for the two-tower model, and prepare the temporal backtest data. ## Step 31 · agent · 2026-09-29T05:54:35.106Z ### Recorded reasoning summary Reviewing Validation Rules I'm currently focusing on understanding the specific constraints for building and tuning our validation ranker. My immediate task is to meticulously re-examine the rules, particularly those concerning the utilization of `train_*` data for history and `val_*` data for subsequent outcomes in this phase. ### Recorded reasoning summary Refining Candidate Selection My current focus is on how candidate item embeddings are generated and retrieved. I'm exploring the requirement for separate user and item towers, with the user tower deriving representation from interaction history, not just a user ID. This approach aligns with the need to decompose into independent vectors for efficient retrieval. ### Recorded reasoning summary Designing Item Representation I'm now considering the specifics of the item tower. My current thought is that each of the 30,000 catalog items requires a distinct representation. I'll be mapping each item ID to its corresponding embedding, which will be a key input for this tower. ### Recorded reasoning summary Precomputing Item Vectors I'm now focused on precomputing item vectors offline. I've mapped `item_id` to `item_idx` and confirmed the availability of item features like `positive_listens` and `popularity_rank`. I'll be feeding these features, along with item embeddings, into a small neural network to generate distinct vectors for each of the 30,000 catalog items, ensuring efficient storage and retrieval. ### Recorded reasoning summary Analyzing User Representation I am currently delving into the user tower's data sources. My focus is on leveraging interaction history, specifically 'liked' and 'disliked' items from `validation_preference_state`, alongside recent high-completion and organic listens, to accurately represent user taste. **Interpreting User Data** My current thought process is centered on interpreting the user's interaction history. I'm considering how explicit preferences like 'liked' and 'disliked' items, as well as strong/organic listens, can be effectively translated into a robust user representation. **Synthesizing User History** I am now synthesizing the user's interaction history. I'm piecing together explicit preferences and recent listening behaviors to construct a comprehensive understanding of their musical tastes. **Extracting User Signals** I am currently extracting key signals from user interaction data. I'm analyzing explicit likes, dislikes, and high-completion organic listens to build a nuanced user profile. ### Recorded reasoning summary Weighting Recent Interactions I'm now considering how to weight different user interactions. My current focus is on incorporating recency, giving more importance to listens within the last 7, 14, or 30 days, and also factoring in completion ratios to better reflect current user taste. ### Recorded reasoning summary Combining User Signals I am now exploring how to best combine the various user interaction signals. My current focus is on pooling liked items, recent listens with time-decay weights, and potentially dislikes. I'm considering concatenating these pooled representations or using learned weights for a final user MLP. ### Recorded reasoning summary Exploring Sequence Models I'm now considering sequence models for user representation. My current thought is to investigate using Multi-Head Self-Attention or GRUs over recent interactions, incorporating item embeddings, action types, and positional information. I'll compare the computational efficiency of these with attentive pooling for user vectors. ### Recorded reasoning summary Prioritizing User Likes I'm now focusing on the target objective and its implications. Given that organic likes (grade 3) are seven times more valuable than 80% listens (grade 1) in graded NDCG@10, I realize the critical importance of weighting positive samples accordingly during model training, potentially with a weight of three for grade 3 events. ### run_shell_command · 2026-09-29T05:54:35.106Z { "description": "Analyze grade distribution in validation truth", "command": "PYTHONPATH=/task python3 -c \"\nimport pandas as pd\nfrom ranking_evaluation import CandidateSet\nfrom evaluate_public import validation_truth\nfrom pathlib import Path\n\ndata_dir = Path('/task/data')\ntargets = set(pd.read_parquet(data_dir / 'target_users.parquet').uid.astype(int))\nelig = CandidateSet(data_dir / 'validation_eligible_candidates.npz')\ntruth = validation_truth(elig, targets)\n\nuids_with_g3 = [u for u, items in truth.items() if any(g == 3 for g in items.values())]\nuids_only_g1 = [u for u, items in truth.items() if all(g == 1 for g in items.values())]\nprint('Users with at least one grade 3:', len(uids_with_g3))\nprint('Users with only grade 1:', len(uids_only_g1))\n\"" } ### Tool output · 2026-09-29T05:54:39.255Z Output: Users with at least one grade 3: 1431 Users with only grade 1: 2832 Process Group PGID: 2092 ## Step 32 · agent · 2026-09-29T05:55:05.522Z ### Recorded reasoning summary Analyzing User Data Distribution I'm currently processing user data to understand the distribution of item grades. It appears a significant portion of users, 1431, possess at least one grade 3 item, while 2832 users exclusively own grade 1 items, summing to 4263 users with established ground truth. My next step is to leverage this understanding for training data construction. ### Recorded reasoning summary Exploring Validation Strategy I'm focusing on the public validation window, which spans 7 days. I need to adhere to the rule of building and tuning a ranker using only training data as history, and then refitting it on the combined training and validation data for the final model. ### Recorded reasoning summary Iterating Model Architecture My focus is on refining the model architecture by iterating on the validation benchmark. I'm specifically exploring how to best train a two-tower model using only the `train_*` data, considering how to define training instances with target items from recent positive interactions and user representations derived from prior history. ### Recorded reasoning summary Refining Interaction Windows I'm re-evaluating how to construct user history and target items for training. I'm considering different approaches, including using sequential interactions to define history and targets, similar to how sequence-to-item models operate. This will influence how I segment data for training. ### Recorded reasoning summary Recognizing Recommendation Paradigm I've realized my current approach aligns directly with standard sequential two-tower recommendation models like YouTube DNN, SASRec, and GRU4Rec. At test time, the user's history is all interactions up to the cutoff, and target items are all subsequent interactions within the 7-day validation window, aiming to rank any relevant item highly. ### run_shell_command · 2026-09-29T05:55:05.522Z { "description": "Check target users history counts for different time windows", "command": "PYTHONPATH=/task python3 -c \"\nimport pandas as pd\nimport pyarrow.parquet as pq\nimport numpy as np\n\ndata_dir = '/task/data'\ncutoff = 24786800\ntargets = pd.read_parquet(f'{data_dir}/target_users.parquet')\ntarget_uids = set(targets.uid)\n\n# Check likes\nlikes = pd.read_parquet(f'{data_dir}/train_likes.parquet')\nprint('Train likes for targets:', len(likes[likes.uid.isin(target_uids)]))\n\n# Check preference state\npref = pd.read_parquet(f'{data_dir}/validation_preference_state.parquet')\nprint('Val pref for targets:', len(pref[pref.uid.isin(target_uids)]))\n\n# Check listens in last 30, 60, 90 days\nfor days in [14, 30, 60, 90]:\n t_start = cutoff - days * 86400\n table = pq.read_table(\n f'{data_dir}/train_listens.parquet',\n columns=['uid', 'timestamp', 'played_ratio_pct', 'is_organic'],\n filters=[('timestamp', '>=', t_start)]\n )\n df = table.to_pandas()\n df_t = df[df.uid.isin(target_uids)]\n print(f'Last {days}d listens for targets: {len(df_t)}, uids: {df_t.uid.nunique()}')\n\"" } ### Tool output · 2026-09-29T05:55:11.300Z Output: Train likes for targets: 690968 Val pref for targets: 469124 Last 14d listens for targets: 2911931, uids: 7906 Last 30d listens for targets: 6113327, uids: 8237 Last 60d listens for targets: 11699019, uids: 8414 Last 90d listens for targets: 16743307, uids: 8456 Process Group PGID: 2112 ## Step 33 · agent · 2026-09-29T05:55:17.668Z ### Recorded reasoning summary Confirming User Engagement I've confirmed that all 8,456 target users have had listens in the last 90 days, with 8,237 active in the last 30. A significant 7,038 also have an active preference state. I'm now evaluating the `candidate_catalog.parquet` for item catalog mapping. ### Recorded reasoning summary Embedding Item Catalog I can embed all 30,000 items, mapping `item_id` to indices 1-30000 and unknown items to 0. Filtering user history to only include catalog items still leaves around 1,400 listens and 55 likes per user, which is substantial. ### Recorded reasoning summary Testing Taste Predictions I've confirmed users have ample history with catalog items. My current focus is on dissecting which interaction types best predict future discoveries, specifically analyzing organic listens and likes within the validation window. I'm empirically testing if liked and highly completed listens define taste, and will explore item similarity. ### run_shell_command · 2026-09-29T05:55:17.668Z { "command": "PYTHONPATH=/task python3 -c \"\nimport pandas as pd\nimport numpy as np\nfrom pathlib import Path\nfrom ranking_evaluation import CandidateSet\nfrom evaluate_public import validation_truth\n\ndata_dir = Path('/task/data')\ntargets = set(pd.read_parquet(data_dir / 'target_users.parquet').uid.astype(int))\nelig = CandidateSet(data_dir / 'validation_eligible_candidates.npz')\ntruth = validation_truth(elig, targets)\n\n# Let's inspect a few users in truth\nsample_uids = list(truth.keys())[:5]\nfor u in sample_uids:\n print(f'User {u}: truth items = {truth[u]}')\n\"", "description": "Sample user validation truth items" } ### Tool output · 2026-09-29T05:55:19.771Z Output: User 200: truth items = {7406680: 1, 694058: 1, 4082738: 1, 6079576: 1, 6006390: 1, 2644025: 1, 4897148: 1, 7930756: 1, 3744896: 1, 3763080: 1, 7481745: 1, 9233959: 1, 9166040: 1} User 800: truth items = {7179437: 1, 1607923: 1, 1816101: 1, 804416: 1, 2650514: 1, 4678741: 1, 2565336: 1, 6803177: 1, 6952637: 1, 4537506: 1, 2766576: 1, 9342289: 1, 9186305: 1, 308692: 1, 5559790: 1, 776584: 1, 388527: 1, 7400303: 1, 6551500: 1, 704566: 1, 843730: 1, 7326035: 1, 4702202: 1, 3531147: 1, 5433005: 1} User 900: truth items = {3673625: 1} User 1100: truth items = {4588549: 1, 635564: 1, 1613989: 1, 8681149: 1, 3910656: 1, 1030400: 1, 7387845: 1, 7340717: 1, 6146700: 1, 647839: 1, 7570645: 1, 3891369: 1, 3857830: 1, 5335567: 1, 8971417: 1, 368089: 1, 8653336: 1, 2051915: 1, 6318072: 1, 8078607: 1} User 1400: truth items = {6945311: 1, 2158715: 1, 2194514: 1, 3530629: 1, 2453912: 1, 7767752: 1, 4859530: 1, 3585411: 1, 4500834: 1, 4770069: 1, 3706360: 1, 6083906: 1, 1118882: 1, 8675053: 1, 5551069: 1, 6473581: 1, 2285672: 1, 5933848: 1, 7008219: 1, 5265819: 1, 975128: 1, 3970945: 1, 4345553: 1, 6901050: 1, 6901374: 1, 1851456: 1, 4678741: 1, 4897148: 1, 8420846: 3, 1327266: 1, 9233959: 1, 3203533: 1, 1407989: 1, 6417502: 1, 9016646: 1, 3763080: 1, 9378983: 1, 8669062: 1} Process Group PGID: 2142 ## Step 34 · agent · 2026-09-29T05:55:42.848Z ### Recorded reasoning summary Exploring User Engagement Patterns I'm currently analyzing user 1400's engagement, noting their possession of item 8420846 with a grade of 3, alongside numerous grade 1 items. My next step involves investigating how to architect a PyTorch Two-Tower Neural Network capable of precise decomposition into a user vector $u \in \mathbb{R}^d$. ### Recorded reasoning summary Designing Two-Tower Network I'm focusing on architecting a PyTorch Two-Tower Neural Network. My next step is to define the item tower, considering an item embedding table and available catalog features like `positive_listens` to generate item vectors $v_i \in \mathbb{R}^d$. ### Recorded reasoning summary Refining Item Tower Architecture I'm refining the item tower by incorporating continuous features like `organic_positive_listens` and `popularity_rank`. These will be normalized and projected before concatenating with item embeddings and passing through an MLP. The normalization of output item vectors is under active consideration. ### Recorded reasoning summary Adding Item Popularity Bias I'm considering how to incorporate item popularity directly into the user-item interaction score. A partitioned user vector allows learning both personalized similarity and an explicit popularity weight for items. I'll test both normalized and unnormalized approaches for the vectors. ### Recorded reasoning summary Processing User History Data I'm now integrating various user history signals into the user tower, including explicit likes, strong organic listens, all recent listens, and dislikes. I'm exploring the possibility of using item embedding lookups for items within a user's historical sequences. ### Recorded reasoning summary Deciding Embedding Sharing Strategy I'm weighing whether to share item embeddings between the user history and item towers or use separate ones. Sharing them offers elegance and regularization, while separate embeddings allow distinct representations of historical context versus item candidacy. I'm leaning towards sharing, with separate MLPs for each tower. **Determining History Item Aggregation** Now I need to decide how to aggregate items within each user history channel. I'm considering weighted pooling, but I still need to define the specific weights ($w_k$) for each item in a sequence or set, considering their importance and contribution. ### Recorded reasoning summary Aggregating History Items I'm deciding on the aggregation method for user history items, weighing weighted pooling against more complex attention mechanisms. Given the scale of 8,456 users and potentially 50-100 history items per user, weighted pooling appears most computationally feasible for individual history channels like likes, organic listens, and dislikes. ### Recorded reasoning summary Formulating User Representation I'm now formulating the user representation by concatenating various history signals and feature engineering. My next step involves passing this combined representation through a `user_mlp`, comprising `Linear -> LayerNorm -> GELU -> Dropout -> Linear`, to ultimately derive the user vector $u \in \mathbb{R}^d$, avoiding any user ID lookup and embracing a data-driven approach. ### Recorded reasoning summary Analyzing Training Regimes I'm currently analyzing training methodologies for the two-tower model, specifically focusing on future prediction. I'm exploring a temporal slice training approach, using a 7-day future window relative to a cutoff date for generating historical data and predictions. ### Recorded reasoning summary Evaluating Training Regimes I'm currently evaluating two distinct training regimes. Regime 1 focuses on future prediction with weighted target items across temporal slices, while Regime 2 adopts a continuous sequence/next-item prediction approach. I'm analyzing their respective strengths and complexities to determine the optimal path forward. ### run_shell_command · 2026-09-29T05:55:42.848Z { "description": "Measure time to read 28 days of train listens and likes", "command": "python3 -c \"\nimport pandas as pd\nimport pyarrow.parquet as pq\nimport numpy as np\nimport time\n\nstart = time.time()\ndata_dir = '/task/data'\ncutoff = 24786800\nw_len = 7 * 86400\n\n# Let's test reading 28 days of listens and likes\nt_min = cutoff - 28 * 86400\ncat = pd.read_parquet(f'{data_dir}/candidate_catalog.parquet')\ncat_items = set(cat.item_id)\ntargets = pd.read_parquet(f'{data_dir}/target_users.parquet')\ntarget_uids = set(targets.uid)\n\nlistens_table = pq.read_table(\n f'{data_dir}/train_listens.parquet',\n columns=['uid', 'item_id', 'timestamp', 'played_ratio_pct', 'is_organic'],\n filters=[('timestamp', '>=', t_min)]\n)\nlistens_df = listens_table.to_pandas()\nprint(f'Read {len(listens_df)} listens in {time.time() - start:.2f}s')\n\nlikes_table = pq.read_table(\n f'{data_dir}/train_likes.parquet',\n columns=['uid', 'item_id', 'timestamp', 'is_organic'],\n filters=[('timestamp', '>=', t_min)]\n)\nlikes_df = likes_table.to_pandas()\nprint(f'Read {len(likes_df)} likes in {time.time() - start:.2f}s')\n\"" } ### Tool output · 2026-09-29T05:55:44.971Z Output: Read 5736707 listens in 0.50s Read 113399 likes in 0.52s Process Group PGID: 2162 ## Step 35 · agent · 2026-09-29T05:57:40.676Z ### run_shell_command · 2026-09-29T05:57:40.676Z { "description": "Measure time to read 90 days of listens", "command": "python3 -c \"\nimport pyarrow.parquet as pq\nimport time\n\nstart = time.time()\nt_min = 24786800 - 90 * 86400\ntable = pq.read_table(\n '/task/data/train_listens.parquet',\n columns=['uid', 'item_id', 'timestamp', 'played_ratio_pct', 'is_organic'],\n filters=[('timestamp', '>=', t_min)]\n)\nprint(f'Read 90d {table.num_rows} listens in {time.time() - start:.2f}s')\n\"" } ### Tool output · 2026-09-29T05:57:42.721Z Output: Read 90d 16769274 listens in 1.47s Process Group PGID: 2196 ## Step 36 · agent · 2026-09-29T05:57:53.642Z ### Recorded reasoning summary Refining User History Representation I'm now focusing on how to best structure user history data up to a specific point in time, $T$. The sheer volume of listens processed so quickly suggests that data loading is no longer a bottleneck. The core challenge is now conceptual: defining a robust and efficient representation for this historical data. ### run_shell_command · 2026-09-29T05:57:53.642Z { "description": "Verify exact logic of preference state generation", "command": "python3 -c \"\nimport pandas as pd\nfrom pathlib import Path\n\ndata_dir = Path('/task/data')\nlikes = pd.read_parquet(data_dir / 'train_likes.parquet')\nunlikes = pd.read_parquet(data_dir / 'train_unlikes.parquet')\ndislikes = pd.read_parquet(data_dir / 'train_dislikes.parquet')\nundislikes = pd.read_parquet(data_dir / 'train_undislikes.parquet')\nvpref = pd.read_parquet(data_dir / 'validation_preference_state.parquet')\n\n# Combine likes and unlikes\nlikes['event'] = 1\nunlikes['event'] = 0\nall_like_events = pd.concat([likes[['uid', 'item_id', 'timestamp', 'event']], unlikes[['uid', 'item_id', 'timestamp', 'event']]]).sort_values('timestamp')\n# last event per (uid, item_id)\nlast_like = all_like_events.groupby(['uid', 'item_id']).last().reset_index()\nactive_likes = last_like[last_like.event == 1][['uid', 'item_id']]\nactive_likes['state'] = 'liked'\n\ndislikes['event'] = 1\nundislikes['event'] = 0\nall_dis_events = pd.concat([dislikes[['uid', 'item_id', 'timestamp', 'event']], undislikes[['uid', 'item_id', 'timestamp', 'event']]]).sort_values('timestamp')\nlast_dis = all_dis_events.groupby(['uid', 'item_id']).last().reset_index()\nactive_dis = last_dis[last_dis.event == 1][['uid', 'item_id']]\nactive_dis['state'] = 'disliked'\n\nsim_pref = pd.concat([active_likes[['uid', 'item_id', 'state']], active_dis[['uid', 'item_id', 'state']]])\n# Compare with vpref\nvpref_set = set(zip(vpref.uid, vpref.item_id, vpref.state))\nsim_set = set(zip(sim_pref.uid, sim_pref.item_id, sim_pref.state))\nprint('Simulated pref matches vpref:', vpref_set == sim_set)\nprint('vpref len:', len(vpref), 'sim len:', len(sim_pref))\n\"" } ### Tool output · 2026-09-29T05:57:56.905Z Output: Simulated pref matches vpref: False vpref len: 469124 sim len: 820525 Process Group PGID: 2218 ## Step 37 · agent · 2026-09-29T05:57:59.436Z ### run_shell_command · 2026-09-29T05:57:59.436Z { "description": "Check if vpref is filtered to targets", "command": "python3 -c \"\nimport pandas as pd\nfrom pathlib import Path\n\ndata_dir = Path('/task/data')\ntargets = set(pd.read_parquet(data_dir / 'target_users.parquet').uid)\nvpref = pd.read_parquet(data_dir / 'validation_preference_state.parquet')\nprint('vpref uids all in targets?', set(vpref.uid).issubset(targets))\n\"" } ### Tool output · 2026-09-29T05:58:01.071Z Output: vpref uids all in targets? True Process Group PGID: 2238 ## Step 38 · agent · 2026-09-29T05:58:17.488Z ### run_shell_command · 2026-09-29T05:58:17.488Z { "command": "python3 -c \"\nimport pandas as pd\nfrom pathlib import Path\n\ndata_dir = Path('/task/data')\ntargets = set(pd.read_parquet(data_dir / 'target_users.parquet').uid)\nlikes = pd.read_parquet(data_dir / 'train_likes.parquet')\nunlikes = pd.read_parquet(data_dir / 'train_unlikes.parquet')\ndislikes = pd.read_parquet(data_dir / 'train_dislikes.parquet')\nundislikes = pd.read_parquet(data_dir / 'train_undislikes.parquet')\nvpref = pd.read_parquet(data_dir / 'validation_preference_state.parquet')\n\nlikes = likes[likes.uid.isin(targets)].copy()\nunlikes = unlikes[unlikes.uid.isin(targets)].copy()\ndislikes = dislikes[dislikes.uid.isin(targets)].copy()\nundislikes = undislikes[undislikes.uid.isin(targets)].copy()\n\nlikes['event'] = 1\nunlikes['event'] = 0\nall_like_events = pd.concat([likes[['uid', 'item_id', 'timestamp', 'event']], unlikes[['uid', 'item_id', 'timestamp', 'event']]]).sort_values('timestamp')\nlast_like = all_like_events.groupby(['uid', 'item_id']).last().reset_index()\nactive_likes = last_like[last_like.event == 1][['uid', 'item_id']].copy()\nactive_likes['state'] = 'liked'\n\ndislikes['event'] = 1\nundislikes['event'] = 0\nall_dis_events = pd.concat([dislikes[['uid', 'item_id', 'timestamp', 'event']], undislikes[['uid', 'item_id', 'timestamp', 'event']]]).sort_values('timestamp')\nlast_dis = all_dis_events.groupby(['uid', 'item_id']).last().reset_index()\nactive_dis = last_dis[last_dis.event == 1][['uid', 'item_id']].copy()\nactive_dis['state'] = 'disliked'\n\nsim_pref = pd.concat([active_likes[['uid', 'item_id', 'state']], active_dis[['uid', 'item_id', 'state']]])\nvpref_set = set(zip(vpref.uid, vpref.item_id, vpref.state))\nsim_set = set(zip(sim_pref.uid, sim_pref.item_id, sim_pref.state))\nprint('Simulated pref matches vpref:', vpref_set == sim_set)\nprint('Difference count:', len(vpref_set ^ sim_set))\n\"", "description": "Compare filtered simulated pref with vpref" } ### Tool output · 2026-09-29T05:58:21.080Z Output: Simulated pref matches vpref: False Difference count: 228686 Process Group PGID: 2260 ## Step 39 · agent · 2026-09-29T05:58:25.381Z ### run_shell_command · 2026-09-29T05:58:25.381Z { "description": "Check if vpref is filtered to catalog items", "command": "python3 -c \"\nimport pandas as pd\nfrom pathlib import Path\n\ndata_dir = Path('/task/data')\ntargets = set(pd.read_parquet(data_dir / 'target_users.parquet').uid)\nvpref = pd.read_parquet(data_dir / 'validation_preference_state.parquet')\ncat = pd.read_parquet(data_dir / 'candidate_catalog.parquet')\ncat_items = set(cat.item_id)\n\nprint('vpref in catalog?', vpref.item_id.isin(cat_items).mean())\nprint('vpref len:', len(vpref))\n\"" } ### Tool output · 2026-09-29T05:58:27.009Z Output: vpref in catalog? 1.0 vpref len: 469124 Process Group PGID: 2277 ## Step 40 · agent · 2026-09-29T05:58:32.781Z ### run_shell_command · 2026-09-29T05:58:32.781Z { "command": "python3 -c \"\nimport pandas as pd\nfrom pathlib import Path\n\ndata_dir = Path('/task/data')\ntargets = set(pd.read_parquet(data_dir / 'target_users.parquet').uid)\ncat = set(pd.read_parquet(data_dir / 'candidate_catalog.parquet').item_id)\nlikes = pd.read_parquet(data_dir / 'train_likes.parquet')\nunlikes = pd.read_parquet(data_dir / 'train_unlikes.parquet')\ndislikes = pd.read_parquet(data_dir / 'train_dislikes.parquet')\nundislikes = pd.read_parquet(data_dir / 'train_undislikes.parquet')\nvpref = pd.read_parquet(data_dir / 'validation_preference_state.parquet')\n\nlikes = likes[likes.uid.isin(targets) & likes.item_id.isin(cat)].copy()\nunlikes = unlikes[unlikes.uid.isin(targets) & unlikes.item_id.isin(cat)].copy()\ndislikes = dislikes[dislikes.uid.isin(targets) & dislikes.item_id.isin(cat)].copy()\nundislikes = undislikes[undislikes.uid.isin(targets) & undislikes.item_id.isin(cat)].copy()\n\nlikes['event'] = 1\nunlikes['event'] = 0\nall_like_events = pd.concat([likes[['uid', 'item_id', 'timestamp', 'event']], unlikes[['uid', 'item_id', 'timestamp', 'event']]]).sort_values('timestamp')\nlast_like = all_like_events.groupby(['uid', 'item_id']).last().reset_index()\nactive_likes = last_like[last_like.event == 1][['uid', 'item_id']].copy()\nactive_likes['state'] = 'liked'\n\ndislikes['event'] = 1\nundislikes['event'] = 0\nall_dis_events = pd.concat([dislikes[['uid', 'item_id', 'timestamp', 'event']], undislikes[['uid', 'item_id', 'timestamp', 'event']]]).sort_values('timestamp')\nlast_dis = all_dis_events.groupby(['uid', 'item_id']).last().reset_index()\nactive_dis = last_dis[last_dis.event == 1][['uid', 'item_id']].copy()\nactive_dis['state'] = 'disliked'\n\nsim_pref = pd.concat([active_likes[['uid', 'item_id', 'state']], active_dis[['uid', 'item_id', 'state']]])\nvpref_set = set(zip(vpref.uid, vpref.item_id, vpref.state))\nsim_set = set(zip(sim_pref.uid, sim_pref.item_id, sim_pref.state))\nprint('Simulated pref matches vpref exactly:', vpref_set == sim_set)\n\"", "description": "Check exact match of simulated pref with catalog filtering" } ### Tool output · 2026-09-29T05:58:35.812Z Output: Simulated pref matches vpref exactly: False Process Group PGID: 2294 ## Step 41 · agent · 2026-09-29T05:59:07.289Z ### run_shell_command · 2026-09-29T05:59:07.289Z { "command": "python3 -c \"\nimport pandas as pd\nfrom pathlib import Path\n\ndata_dir = Path('/task/data')\ntargets = set(pd.read_parquet(data_dir / 'target_users.parquet').uid)\ncat = set(pd.read_parquet(data_dir / 'candidate_catalog.parquet').item_id)\nlikes = pd.read_parquet(data_dir / 'train_likes.parquet')\nunlikes = pd.read_parquet(data_dir / 'train_unlikes.parquet')\nvpref = pd.read_parquet(data_dir / 'validation_preference_state.parquet')\n\nvpref_liked = set(zip(vpref[vpref.state == 'liked'].uid, vpref[vpref.state == 'liked'].item_id))\n\nlikes = likes[likes.uid.isin(targets) & likes.item_id.isin(cat)].copy()\nunlikes = unlikes[unlikes.uid.isin(targets) & unlikes.item_id.isin(cat)].copy()\nlikes['event'] = 1\nunlikes['event'] = 0\nall_like_events = pd.concat([likes[['uid', 'item_id', 'timestamp', 'event']], unlikes[['uid', 'item_id', 'timestamp', 'event']]]).sort_values('timestamp')\nlast_like = all_like_events.groupby(['uid', 'item_id']).last().reset_index()\nactive_likes = set(zip(last_like[last_like.event == 1].uid, last_like[last_like.event == 1].item_id))\n\nprint('vpref liked len:', len(vpref_liked))\nprint('active likes len:', len(active_likes))\nprint('in vpref but not active:', len(vpref_liked - active_likes))\nprint('in active but not vpref:', len(active_likes - vpref_liked))\n\"", "description": "Inspect differences in liked items" } ### Tool output · 2026-09-29T05:59:10.155Z Output: vpref liked len: 418435 active likes len: 422925 in vpref but not active: 0 in active but not vpref: 4490 Process Group PGID: 2319 ## Step 42 · agent · 2026-09-29T05:59:19.591Z ### Recorded reasoning summary Refining Preference Logic I'm currently examining the relationship between preferences marked "in vpref" but not "active." It seems "vpref" is a narrower category within active likes, likely time-bound. I'm also reviewing validation and test preference states. ### Recorded reasoning summary Leveraging Ground Truth Exciting news! We have direct access to ground truth preference state files for validation and testing, eliminating the need for simulations. This allows us to precisely analyze candidate eligibility. I'm now shifting focus to exploring model architectures that can achieve high NDCG@10 scores, considering track similarity and user taste. ### Recorded reasoning summary Developing Candidate Models I'm exploring how to incorporate user taste vectors, track quality, and recency into candidate generation. My current focus is on testing a simple co-occurrence/embedding model, specifically training a Two-Tower model on tracks found together in sessions or user histories. I'm analyzing standard two-tower architectures and how they process user and item inputs for retrieval. ### update_topic · 2026-09-29T05:59:19.591Z { "summary": "Formulated the two-tower model architecture: User Tower aggregates recent interaction embeddings (likes, recent high-completion listens) and passes through a user MLP; Item Tower embeds catalog items through an item MLP. Now building an end-to-end prototype on the validation split and evaluating against the public evaluator.", "title": "Prototype & Validation Experimentation", "strategic_intent": "Build and evaluate a clean baseline two-tower neural network on the validation window." } ### Tool output · 2026-09-29T05:59:19.594Z Current topic: "Prototype & Validation Experimentation" Topic summary: Formulated the two-tower model architecture: User Tower aggregates recent interaction embeddings (likes, recent high-completion listens) and passes through a user MLP; Item Tower embeds catalog items through an item MLP. Now building an end-to-end prototype on the validation split and evaluating against the public evaluator. Strategic Intent: Build and evaluate a clean baseline two-tower neural network on the validation window. ## Step 43 · agent · 2026-09-29T05:59:47.189Z ### Recorded reasoning summary Designing Prototype Data I'm focusing on selecting the right data for prototype training. My current thoughts center on the `train_*` datasets and specifically examining events within the training data, beginning with `train_likes` after the cutoff of $T_{\text{train\_cutoff}} = 24786800$. ### Recorded reasoning summary Refining Training Windows I'm now considering `train_listens` and `validation_preference_state`. My thoughts are exploring how to define training instances based on user interaction sequences within a 60-day window prior to the cutoff. I'm contemplating using the last 14 days as a target window, or training on sessions across the entire 60 days, focusing on temporally close listens and strong positive signals like organic likes. ### Recorded reasoning summary Defining Training Windows My current focus is on defining distinct training windows, namely A, B, and C, each progressively earlier than the last. I am establishing clear boundaries for target items (organic likes and strong listens) and user history, ensuring all user interactions are strictly prior to the respective window's start. I'm also thinking about the input to the user tower at evaluation time. ### Recorded reasoning summary Training Sequence-to-Item I'm reconsidering session co-occurrence and recent interactions for training. My thoughts are now exploring a sequence-to-item approach, similar to YouTube DNN or SASRec, where each positive interaction becomes a training example. This allows me to leverage the rich history prior to each target item for embedding generation, potentially yielding millions of examples within the last 30 days of training data. ### Recorded reasoning summary Evaluating History Construction I'm assessing the efficiency of constructing user histories for training. My thoughts suggest that temporal window batching, rather than dynamic lookups per interaction, significantly optimizes history generation for large datasets, making it feasible for millions of examples. ### Recorded reasoning summary Optimizing History Computation I've devised a highly efficient method for generating user histories across multiple training windows. By fixing user history at the start of each distinct window, I drastically reduce redundant computations, yielding millions of training pairs with minimal processing time. I'm now proceeding to verify this mathematically and programmatically. ### Recorded reasoning summary Considering Discovery I'm questioning how discovery is handled given historical interaction filtering. My thoughts are focusing on ensuring the model can recommend new items, as recent validation showed zero repeat interactions in ground truth, highlighting a potential gap in my current approach. ### run_shell_command · 2026-09-29T05:59:47.189Z { "command": "python3 -c \"\nimport pandas as pd\nimport pyarrow.parquet as pq\nimport numpy as np\n\ncutoff = 24786800\nt_7d = cutoff - 7 * 86400\ncatalog = pd.read_parquet('/task/data/candidate_catalog.parquet')\ncat_items = set(catalog.item_id)\n\n# Window 0: [cutoff - 7d, cutoff]\nlistens = pq.read_table('/task/data/train_listens.parquet', columns=['uid', 'item_id', 'timestamp', 'played_ratio_pct', 'is_organic'], filters=[('timestamp', '>=', t_7d)]).to_pandas()\nstrong_target = listens[(listens.is_organic == 1) & (listens.played_ratio_pct >= 80) & listens.item_id.isin(cat_items)]\n\n# History before t_7d\nhist_listens = pq.read_table('/task/data/train_listens.parquet', columns=['uid', 'item_id', 'timestamp'], filters=[('timestamp', '>=', t_7d - 30 * 86400), ('timestamp', '<', t_7d)]).to_pandas()\nhist_pairs = set(zip(hist_listens.uid, hist_listens.item_id))\n\ntarget_pairs = list(zip(strong_target.uid, strong_target.item_id))\nrepeat_count = sum(p in hist_pairs for p in target_pairs)\nprint('Total target pairs in window:', len(target_pairs))\nprint('Repeats from past 30d:', repeat_count, f'({repeat_count/len(target_pairs):.2%})')\nprint('Novel items in window:', len(target_pairs) - repeat_count, f'({1 - repeat_count/len(target_pairs):.2%})')\n\"", "description": "Check novel vs repeat items in 7d train window" } ### Tool output · 2026-09-29T05:59:53.317Z Output: Total target pairs in window: 305188 Repeats from past 30d: 201980 (66.18%) Novel items in window: 103208 (33.82%) Process Group PGID: 2336 ## Step 44 · agent · 2026-09-29T06:00:33.986Z ### Recorded reasoning summary Analyzing Novelty Patterns I'm observing that a significant portion, over 100,000, of target pairs within the 7-day training window are entirely novel listening items. This contrasts with the roughly 200,000 repeat items. Interestingly, all candidates considered in the test window are inherently novel due to pre-filtering. ### Recorded reasoning summary Integrating Novelty Data I'm considering how best to leverage both novel and repeat listening data. It seems including repeats aids in learning item-item similarity and user taste, while novel listens help with discovery. Both aspects appear crucial for a robust recommendation model. ### Recorded reasoning summary Adjusting Logits for Popularity I'm recalibrating the logits to account for catalog popularity, applying the logQ correction as standard in retrieval models. This adjustment should directly optimize for true relevance during inference by mitigating the over-sampling of popular items as negatives, ensuring a fairer comparison. ### Recorded reasoning summary Sampling Negatives Effectively I'm now focusing on sampling negative items from the catalog, considering both uniform and popularity-based approaches. My aim is to ensure the model learns from a diverse set of negatives, encompassing both popular and less-frequented items, during training. **Determining Embedding Dimensionality** I'm exploring the optimal embedding dimensionality ($d$) for my user and item representations. Both 64 and 128 dimensions appear feasible, offering acceptable computation times and memory footprints given the catalog size. I'll proceed with analysis to select the best fit. ### Recorded reasoning summary Aggregating User History I'm now consolidating various user history signals. This includes explicit likes and dislikes, alongside recent strong and general listens, each with carefully considered weighting schemes to capture diverse user engagement patterns effectively. ### Recorded reasoning summary Processing User Features I am now detailing how the User Tower processes continuous features. This involves calculating weighted pooled embeddings for various history channels and concatenating them with projected continuous features before passing them through the User MLP. ### Recorded reasoning summary Defining Item Features I am now defining the item tower's process, incorporating item embeddings with projected catalog features. The concatenated representation then passes through an MLP, optionally normalized, to produce the final item representation. ### list_directory · 2026-09-29T06:00:33.986Z { "dir_path": "/app/solution" } ### Tool output · 2026-09-29T06:00:33.990Z Directory /app/solution is empty. ## Step 45 · agent · 2026-09-29T06:00:44.302Z ### Recorded reasoning summary Generating Baseline Submission My immediate focus is on creating a robust baseline submission in `submission.parquet`. This ensures I always have a validated starting point, crucial for tracking progress and preserving successful iterations. ### run_shell_command · 2026-09-29T06:00:44.302Z { "description": "Create initial valid fallback submission and verify schema", "command": "PYTHONPATH=/task python3 -c \"\nimport pandas as pd\nimport numpy as np\nfrom pathlib import Path\nfrom ranking_evaluation import validate_ranking, TOP_N\n\ndata_dir = Path('/task/data')\ntargets_df = pd.read_parquet(data_dir / 'target_users.parquet')\ntargets = set(targets_df.uid.astype(int))\n\ntest_npz = np.load(data_dir / 'test_eligible_candidates.npz')\npacked = test_npz['packed_eligible']\nitem_ids = test_npz['item_ids']\nuids = test_npz['uids']\n\nrecords = []\nfor i, uid in enumerate(uids):\n mask = np.unpackbits(packed[i], count=len(item_ids), bitorder='little').astype(bool)\n eligible_items = item_ids[mask][:100]\n for r, itm in enumerate(eligible_items, 1):\n records.append((uid, itm, r))\n\ndf = pd.DataFrame(records, columns=['uid', 'item_id', 'rank'])\nvalidated = validate_ranking(df, targets)\nout_path = Path('/app/solution/submission.parquet')\nvalidated.to_parquet(out_path)\nprint(f'Successfully wrote fallback submission to {out_path} with {len(validated)} rows!')\n\"" } ### Tool output · 2026-09-29T06:00:47.550Z Output: Successfully wrote fallback submission to /app/solution/submission.parquet with 845600 rows! Process Group PGID: 2364 ## Step 46 · agent · 2026-09-29T06:00:50.448Z ### Recorded reasoning summary Verifying Candidate Eligibility I've successfully generated an initial submission file at `/app/solution/submission.parquet`. My next step is to meticulously check if every entry within this submission adheres to the candidate eligibility criteria outlined in `test_eligible_candidates.npz`. ### run_shell_command · 2026-09-29T06:00:50.448Z { "description": "Verify all items in fallback submission are eligible", "command": "PYTHONPATH=/task python3 -c \"\nimport pandas as pd\nfrom ranking_evaluation import CandidateSet\nfrom pathlib import Path\n\nelig = CandidateSet(Path('/task/data/test_eligible_candidates.npz'))\nsub = pd.read_parquet('/app/solution/submission.parquet')\n\nall_ok = all(elig.is_eligible(uid, item) for uid, item in sub[['uid', 'item_id']].itertuples(index=False, name=None))\nprint('All items eligible:', all_ok)\n\"" } ### Tool output · 2026-09-29T06:00:54.120Z Output: All items eligible: True Process Group PGID: 2380 ## Step 47 · agent · 2026-09-29T06:01:52.348Z ### Recorded reasoning summary Refining Strategy for NDCG I've confirmed the fallback submission is structurally sound. My current focus is on enhancing performance beyond the popularity baseline to maximize NDCG@10. I'm now analyzing the baseline's metrics to identify avenues for improvement. ### run_shell_command · 2026-09-29T06:01:52.348Z { "description": "Analyze popularity rank of ground truth items", "command": "PYTHONPATH=/task python3 -c \"\nimport pandas as pd\nimport numpy as np\nfrom pathlib import Path\nfrom ranking_evaluation import CandidateSet\nfrom evaluate_public import validation_truth\n\ndata_dir = Path('/task/data')\ntargets = set(pd.read_parquet(data_dir / 'target_users.parquet').uid.astype(int))\nelig = CandidateSet(data_dir / 'validation_eligible_candidates.npz')\ntruth = validation_truth(elig, targets)\ncatalog = pd.read_parquet(data_dir / 'candidate_catalog.parquet')\n\n# Check popularity of truth items:\ncat_ranks = dict(zip(catalog.item_id, catalog.popularity_rank))\ntruth_ranks = [cat_ranks.get(itm, 99999) for u, itms in truth.items() for itm in itms]\nprint('Truth item popularity rank: median', np.median(truth_ranks), 'mean', np.mean(truth_ranks), '25th', np.percentile(truth_ranks, 25), '75th', np.percentile(truth_ranks, 75))\n\n# For grade 3 vs grade 1:\ng3_ranks = [cat_ranks.get(itm, 99999) for u, itms in truth.items() for itm, g in itms.items() if g == 3]\ng1_ranks = [cat_ranks.get(itm, 99999) for u, itms in truth.items() for itm, g in itms.items() if g == 1]\nprint('Grade 3 item popularity rank: median', np.median(g3_ranks), 'mean', np.mean(g3_ranks))\nprint('Grade 1 item popularity rank: median', np.median(g1_ranks), 'mean', np.mean(g1_ranks))\n\"" } ### Tool output · 2026-09-29T06:01:54.465Z Output: Truth item popularity rank: median 5622.0 mean 8463.087743916969 25th 1733.0 75th 13374.0 Grade 3 item popularity rank: median 6371.0 mean 8891.975322617162 Grade 1 item popularity rank: median 5545.0 mean 8406.28765891101 Process Group PGID: 2403 ## Step 48 · agent · 2026-09-29T06:07:48.753Z ### Recorded reasoning summary Analyzing Popularity Distributions I'm currently examining the popularity rank distributions of truth items. It appears the median rank sits around 5622, with the 25th percentile at 1733 and the 75th at 13,374, which is quite a spread. The baseline popularity metric, limited to the top 100 items, seems to be a very small fraction of the overall distribution. ### Recorded reasoning summary Exploring Two-Tower Logic I'm thinking about how a two-tower model learns personalized tastes, especially for mid-popularity items. The key seems to be embedding items based on shared acoustic features and user appeal, while the user tower aggregates listened tracks to create a taste profile. This allows for personalized discovery beyond just top-ranked items. ### Recorded reasoning summary Refining Similarity Signals I'm considering the strongest signals for item similarity and user preference, focusing on co-listening patterns within user histories and direct future listening predictions. My current exploration involves evaluating how best to train the two-tower model, specifically whether an end-to-end approach for both user and item towers, using batch processing and cross-entropy loss, offers the most effective training. ### Recorded reasoning summary Optimizing Negative Sampling I'm currently exploring the impact of batch size on negative sampling, specifically testing a batch size of 512 against in-batch negatives. I'm also considering augmenting this with 1024 sampled negatives from the catalog per batch, creating a candidate pool of 1536 items for each user vector. This approach allows direct discrimination against a much larger set of potential items, and I'm verifying the computational feasibility of this strategy. ### Recorded reasoning summary Defining User Input I'm clarifying the composition of the user tower's input. This involves defining `liked_items`, `strong_listens` (organic listens with >= 80% played ratio recently), `recent_listens`, and `disliked_items` from various data sources, using recent history and applying recency decay where appropriate. I'm also planning for zero-padding if a user has fewer than the specified number of items. ### Recorded reasoning summary Incorporating Item Features I'm now considering how to enrich the item tower with catalog features like positive listens and organic ratios. My current thought is to concatenate these features with the item embeddings before feeding them into the item MLP, which will also incorporate additional item-specific information. This approach should allow for a more nuanced understanding of item characteristics. ### Recorded reasoning summary Normalizing Embeddings I'm focusing on L2 normalization for both item and user embeddings. This ensures that the dot product between them directly represents cosine similarity, which is a more intuitive measure of relatedness. The user tower's input is being refined by concatenating pooled item embeddings (from likes, strong, and recent listens) with user statistics and then passing it through an MLP. ### run_shell_command · 2026-09-29T06:07:48.753Z { "description": "Test data loading time for train likes and listens", "command": "python3 -c \"\nimport pandas as pd\nimport pyarrow.parquet as pq\nimport numpy as np\nimport time\n\nstart = time.time()\ndata_dir = '/task/data'\ncutoff = 24786800\nt_7d = cutoff - 7 * 86400\n\ncatalog = pd.read_parquet(f'{data_dir}/candidate_catalog.parquet')\nitem_id_to_idx = {id_: idx for idx, id_ in enumerate(catalog.item_id, 1)}\ntargets = pd.read_parquet(f'{data_dir}/target_users.parquet')\ntarget_uids = set(targets.uid)\n\n# Read likes before cutoff\nlikes = pd.read_parquet(f'{data_dir}/train_likes.parquet')\nlikes = likes[likes.uid.isin(target_uids) & (likes.timestamp < cutoff)]\nprint(f'Likes: {len(likes)} in {time.time() - start:.2f}s')\n\n# Read listens in last 60d\nt_60d = cutoff - 60 * 86400\nlistens = pq.read_table(\n f'{data_dir}/train_listens.parquet',\n columns=['uid', 'item_id', 'timestamp', 'played_ratio_pct', 'is_organic'],\n filters=[('timestamp', '>=', t_60d)]\n).to_pandas()\nlistens = listens[listens.uid.isin(target_uids)]\nprint(f'Listens: {len(listens)} in {time.time() - start:.2f}s')\n\"" } ### Tool output · 2026-09-29T06:07:51.696Z Output: Likes: 690968 in 0.13s Listens: 11699019 in 1.37s Process Group PGID: 2459 ## Step 49 · agent · 2026-09-29T06:08:14.805Z ### Recorded reasoning summary Analyzing User Behavior Patterns I'm currently deep in the process of dissecting user engagement metrics, specifically looking at listen counts and likes. The immediate focus is on developing an efficient method to construct user history tensors and corresponding training target pairs, a crucial step for model development. ### Recorded reasoning summary Formulating Training Data I'm refining the data preparation pipeline. My current focus involves defining positive pairs for training, particularly by weighting organic likes and strong organic listens within a specific future time window. This is essential for building accurate user history tensors. ### Recorded reasoning summary Exploring Multiple Cutoffs I'm now considering leveraging multiple time cutoffs to construct user histories and identify positive targets. This approach would involve generating distinct datasets from different historical windows, aiming to capture more robust patterns. I'm also planning a Python test to validate the efficiency of this multi-cutoff strategy. ### run_shell_command · 2026-09-29T06:08:14.805Z { "command": "python3 -c \"\nimport pandas as pd\nimport pyarrow.parquet as pq\nimport numpy as np\nimport time\n\nstart = time.time()\ndata_dir = '/task/data'\ncutoff = 24786800\nt_7d = cutoff - 7 * 86400\n\ncatalog = pd.read_parquet(f'{data_dir}/candidate_catalog.parquet')\nitem_id_to_idx = {id_: idx for idx, id_ in enumerate(catalog.item_id, 1)}\ntargets_df = pd.read_parquet(f'{data_dir}/target_users.parquet')\nuids = targets_df.uid.to_numpy()\nuid_to_idx = {uid: idx for idx, uid in enumerate(uids)}\nN_users = len(uids)\n\n# Let's test extracting history at t_7d for all users\nt_min = t_7d - 60 * 86400\nlistens = pq.read_table(\n f'{data_dir}/train_listens.parquet',\n columns=['uid', 'item_id', 'timestamp', 'played_ratio_pct', 'is_organic'],\n filters=[('timestamp', '>=', t_min), ('timestamp', '<', t_7d)]\n).to_pandas()\nlistens = listens[listens.uid.isin(uid_to_idx)]\nlistens['item_idx'] = listens.item_id.map(item_id_to_idx)\nlistens = listens.dropna(subset=['item_idx'])\nlistens['item_idx'] = listens['item_idx'].astype(np.int64)\nprint(f'Filtered catalog listens before t_7d: {len(listens)} in {time.time() - start:.2f}s')\n\n# Target events in [t_7d, cutoff]\ntarget_listens = pq.read_table(\n f'{data_dir}/train_listens.parquet',\n columns=['uid', 'item_id', 'timestamp', 'played_ratio_pct', 'is_organic'],\n filters=[('timestamp', '>=', t_7d), ('timestamp', '<', cutoff)]\n).to_pandas()\ntarget_strong = target_listens[target_listens.uid.isin(uid_to_idx) & (target_listens.is_organic == 1) & (target_listens.played_ratio_pct >= 80)]\ntarget_strong['item_idx'] = target_strong.item_id.map(item_id_to_idx)\ntarget_strong = target_strong.dropna(subset=['item_idx'])\n\ntarget_likes = pd.read_parquet(f'{data_dir}/train_likes.parquet')\ntarget_likes = target_likes[target_likes.uid.isin(uid_to_idx) & (target_likes.timestamp >= t_7d) & (target_likes.timestamp < cutoff) & (target_likes.is_organic == 1)]\ntarget_likes['item_idx'] = target_likes.item_id.map(item_id_to_idx)\ntarget_likes = target_likes.dropna(subset=['item_idx'])\n\nprint(f'Target strong listens: {len(target_strong)}, target organic likes: {len(target_likes)}')\n\"", "description": "Test extracting history and targets for window 1" } ### Tool output · 2026-09-29T06:08:19.189Z Output: :39: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy Filtered catalog listens before t_7d: 8267821 in 2.24s Target strong listens: 304885, target organic likes: 7774 Process Group PGID: 2481 ## Step 50 · agent · 2026-09-29T06:10:30.286Z ### Recorded reasoning summary Processing User Histories I'm currently focused on extracting key user historical data. Specifically, I'm identifying `liked_items` to build individual user profiles, aiming to capture their most frequent interactions. ### run_shell_command · 2026-09-29T06:10:30.286Z { "command": "python3 -c \"\nimport pandas as pd\nimport numpy as np\nimport time\n\nstart = time.time()\n# Simulate with 8456 users\nN_users = 8456\nK = 64\n\n# Test building from a dataframe with 1M rows using pandas groupby or numpy ops\ndf = pd.DataFrame({\n 'uid_idx': np.random.randint(0, N_users, size=1000000),\n 'item_idx': np.random.randint(1, 30001, size=1000000),\n 'weight': np.random.rand(1000000).astype(np.float32)\n})\n\n# Fast numpy extraction:\n# Sort by uid_idx\ndf = df.sort_values('uid_idx')\nuid_arr = df['uid_idx'].to_numpy()\nitem_arr = df['item_idx'].to_numpy()\nweight_arr = df['weight'].to_numpy()\n\n# Find boundaries\nchange_pts = np.flatnonzero(np.diff(uid_arr)) + 1\nstarts = np.r_[0, change_pts]\nends = np.r_[change_pts, len(uid_arr)]\nunique_uids = uid_arr[starts]\n\nout_items = np.zeros((N_users, K), dtype=np.int64)\nout_weights = np.zeros((N_users, K), dtype=np.float32)\n\nfor u, s, e in zip(unique_uids, starts, ends):\n # take last K items\n items = item_arr[max(s, e - K):e]\n weights = weight_arr[max(s, e - K):e]\n out_items[u, :len(items)] = items\n out_weights[u, :len(weights)] = weights\n\nprint(f'Processed 1M rows for {N_users} users into (N, {K}) in {time.time() - start:.3f}s!')\n\"", "description": "Benchmark numpy group extraction speed" } ### Tool output · 2026-09-29T06:10:31.914Z Output: Processed 1M rows for 8456 users into (N, 64) in 0.120s! Process Group PGID: 2515 ## Step 51 · agent · 2026-09-29T06:11:26.044Z ### Recorded reasoning summary Exploring Two-Tower Models I'm impressed by the 0.120-second execution. My focus is now on training a two-tower model for music discovery. Specifically, I'm analyzing key architectural aspects, particularly the embedding dimension '$d$'. ### Recorded reasoning summary Testing Embedding Dimensions I'm currently evaluating embedding dimensions of 64 and 128 for the user tower. My immediate focus is on how the "likes," "strong," "recent," and "dislikes" features, along with summary statistics, will be processed and projected within the neural network architecture. ### Recorded reasoning summary Processing User Features I'm refining the pooling mechanisms for my user tower. The current focus involves how the weighted aggregation of "likes," "strong," and "recent" interactions, alongside "dislikes" and aggregated statistics, will be projected to generate effective user embeddings for the model. ### Recorded reasoning summary Integrating Item Features I'm integrating catalog features into the item tower. Specifically, I'm processing normalized catalog features through a projection layer before concatenating them with item embeddings and passing them through the MLP for final embedding generation. ### Recorded reasoning summary Refining Two-Tower Scoring I'm investigating how to incorporate a temperature parameter $\tau$ into the training objective for my two-tower model. This involves calculating user and positive item embeddings for a batch and exploring how to use these with weights for a more nuanced scoring mechanism. ### Recorded reasoning summary Learning Temperature Parameters I'm exploring the critical role of temperature $\tau$ in my two-tower model's retrieval objective. The idea of making $\tau$ a learnable parameter, akin to CLIP's `logit_scale`, is particularly exciting. I’m now building a prototype script to test this approach and assess its impact on validation NDCG@10. ### write_file · 2026-09-29T06:11:26.044Z { "content": "#!/usr/bin/env python3\n\"\"\"Prototype two-tower neural network ranker for validation experimentation.\"\"\"\nfrom __future__ import annotations\n\nimport math\nimport time\nfrom pathlib import Path\n\nimport numpy as np\nimport pandas as pd\nimport pyarrow.parquet as pq\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\nfrom ranking_evaluation import CandidateSet, validate_ranking, TOP_N\nfrom evaluate_public import validation_truth, score\n\nDATA_DIR = Path('/task/data')\nSOLUTION_DIR = Path('/app/solution')\n\n\ndef get_catalog_data():\n cat = pd.read_parquet(DATA_DIR / 'candidate_catalog.parquet')\n item_ids = cat.item_id.to_numpy()\n item_id_to_idx = {int(id_): idx for idx, id_ in enumerate(item_ids, 1)}\n \n # Item features: log1p(pos_listens), log1p(organic_listens), log1p(pop_rank), organic_ratio\n pos = np.log1p(cat.positive_listens.to_numpy(dtype=np.float32))\n org = np.log1p(cat.organic_positive_listens.to_numpy(dtype=np.float32))\n pop = np.log1p(cat.popularity_rank.to_numpy(dtype=np.float32))\n ratio = (cat.organic_positive_listens / (cat.positive_listens + 1.0)).to_numpy(dtype=np.float32)\n \n # Normalize features\n feats = np.stack([\n (pos - pos.mean()) / (pos.std() + 1e-6),\n (org - org.mean()) / (org.std() + 1e-6),\n (pop - pop.mean()) / (pop.std() + 1e-6),\n (ratio - ratio.mean()) / (ratio.std() + 1e-6),\n ], axis=1)\n \n # 0 is padding/unknown\n feat_tensor = np.zeros((len(item_ids) + 1, 4), dtype=np.float32)\n feat_tensor[1:] = feats\n \n return item_ids, item_id_to_idx, torch.from_numpy(feat_tensor)\n\n\ndef extract_user_histories(cutoff_ts: int, item_id_to_idx: dict[int, int], uid_to_idx: dict[int, int], N_users: int, is_val: bool = False):\n \"\"\"Extract user history tensors at cutoff_ts.\"\"\"\n K = 64\n likes_items = np.zeros((N_users, K), dtype=np.int64)\n strong_items = np.zeros((N_users, K), dtype=np.int64)\n strong_weights = np.zeros((N_users, K), dtype=np.float32)\n recent_items = np.zeros((N_users, K), dtype=np.int64)\n recent_weights = np.zeros((N_users, K), dtype=np.float32)\n dislikes_items = np.zeros((N_users, 32), dtype=np.int64)\n user_stats = np.zeros((N_users, 4), dtype=np.float32)\n \n # 1. Likes\n train_likes = pd.read_parquet(DATA_DIR / 'train_likes.parquet')\n likes_df = train_likes[(train_likes.timestamp < cutoff_ts) & (train_likes.uid.isin(uid_to_idx))].copy()\n if is_val:\n # Also include validation_preference_state liked\n pref = pd.read_parquet(DATA_DIR / 'validation_preference_state.parquet')\n liked_pref = pref[(pref.state == 'liked') & (pref.uid.isin(uid_to_idx))].copy()\n liked_pref['timestamp'] = cutoff_ts - 1\n likes_df = pd.concat([likes_df[['uid', 'item_id', 'timestamp']], liked_pref[['uid', 'item_id', 'timestamp']]])\n \n likes_df['uid_idx'] = likes_df.uid.map(uid_to_idx)\n likes_df['item_idx'] = likes_df.item_id.map(item_id_to_idx)\n likes_df = likes_df.dropna(subset=['uid_idx', 'item_idx']).sort_values('timestamp')\n likes_df['uid_idx'] = likes_df.uid_idx.astype(np.int64)\n likes_df['item_idx'] = likes_df.item_idx.astype(np.int64)\n \n # Group by uid_idx\n for u, g in likes_df.groupby('uid_idx'):\n itms = g.item_idx.to_numpy()\n k_take = min(len(itms), K)\n likes_items[u, :k_take] = itms[-k_take:]\n user_stats[u, 0] = np.log1p(len(itms))\n \n # 2. Listens in last 45 days\n t_min = cutoff_ts - 45 * 86400\n listens_table = pq.read_table(\n DATA_DIR / 'train_listens.parquet',\n columns=['uid', 'item_id', 'timestamp', 'played_ratio_pct', 'is_organic'],\n filters=[('timestamp', '>=', t_min), ('timestamp', '<', cutoff_ts)]\n )\n listens = listens_table.to_pandas()\n listens = listens[listens.uid.isin(uid_to_idx)]\n listens['uid_idx'] = listens.uid.map(uid_to_idx).astype(np.int64)\n listens['item_idx'] = listens.item_id.map(item_id_to_idx)\n listens = listens.dropna(subset=['item_idx'])\n listens['item_idx'] = listens.item_idx.astype(np.int64)\n listens = listens.sort_values('timestamp')\n \n # Strong organic listens (last 45 days, >= 80%, is_organic=1)\n strong = listens[(listens.is_organic == 1) & (listens.played_ratio_pct >= 80)]\n decay_scale = 14 * 86400\n for u, g in strong.groupby('uid_idx'):\n itms = g.item_idx.to_numpy()\n ts = g.timestamp.to_numpy()\n w = np.exp(-(cutoff_ts - ts) / decay_scale)\n k_take = min(len(itms), K)\n strong_items[u, :k_take] = itms[-k_take:]\n strong_weights[u, :k_take] = w[-k_take:]\n user_stats[u, 1] = np.log1p(len(itms))\n \n # Recent general listens (last 14 days)\n t_14d = cutoff_ts - 14 * 86400\n recent = listens[listens.timestamp >= t_14d]\n for u, g in recent.groupby('uid_idx'):\n itms = g.item_idx.to_numpy()\n ts = g.timestamp.to_numpy()\n ratios = g.played_ratio_pct.to_numpy() / 100.0\n w = ratios * np.exp(-(cutoff_ts - ts) / (7 * 86400))\n k_take = min(len(itms), K)\n recent_items[u, :k_take] = itms[-k_take:]\n recent_weights[u, :k_take] = w[-k_take:]\n user_stats[u, 2] = np.log1p(len(itms))\n \n # Dislikes\n train_dislikes = pd.read_parquet(DATA_DIR / 'train_dislikes.parquet')\n dis_df = train_dislikes[(train_dislikes.timestamp < cutoff_ts) & (train_dislikes.uid.isin(uid_to_idx))].copy()\n if is_val:\n pref = pd.read_parquet(DATA_DIR / 'validation_preference_state.parquet')\n dis_pref = pref[(pref.state == 'disliked') & (pref.uid.isin(uid_to_idx))].copy()\n dis_pref['timestamp'] = cutoff_ts - 1\n dis_df = pd.concat([dis_df[['uid', 'item_id', 'timestamp']], dis_pref[['uid', 'item_id', 'timestamp']]])\n dis_df['uid_idx'] = dis_df.uid.map(uid_to_idx)\n dis_df['item_idx'] = dis_df.item_id.map(item_id_to_idx)\n dis_df = dis_df.dropna(subset=['uid_idx', 'item_idx']).sort_values('timestamp')\n dis_df['uid_idx'] = dis_df.uid_idx.astype(np.int64)\n dis_df['item_idx'] = dis_df.item_idx.astype(np.int64)\n for u, g in dis_df.groupby('uid_idx'):\n itms = g.item_idx.to_numpy()\n k_take = min(len(itms), 32)\n dislikes_items[u, :k_take] = itms[-k_take:]\n user_stats[u, 3] = np.log1p(len(itms))\n\n # Normalize user stats\n for col in range(4):\n s_std = user_stats[:, col].std()\n if s_std > 1e-6:\n user_stats[:, col] = (user_stats[:, col] - user_stats[:, col].mean()) / s_std\n\n return {\n 'likes': torch.from_numpy(likes_items),\n 'strong': torch.from_numpy(strong_items),\n 'strong_w': torch.from_numpy(strong_weights),\n 'recent': torch.from_numpy(recent_items),\n 'recent_w': torch.from_numpy(recent_weights),\n 'dislikes': torch.from_numpy(dislikes_items),\n 'stats': torch.from_numpy(user_stats),\n }\n\n\nclass UserTower(nn.Module):\n def __init__(self, item_embeddings: nn.Embedding, d_in: int, d_hidden: int, d_out: int):\n super().__init__()\n self.item_emb = item_embeddings\n self.stats_proj = nn.Linear(4, 16)\n in_dim = 4 * d_in + 16\n self.mlp = nn.Sequential(\n nn.Linear(in_dim, d_hidden),\n nn.LayerNorm(d_hidden),\n nn.GELU(),\n nn.Dropout(0.1),\n nn.Linear(d_hidden, d_out),\n )\n\n def forward(self, likes, strong, strong_w, recent, recent_w, dislikes, stats):\n # likes pool\n mask_l = (likes > 0).float().unsqueeze(-1)\n emb_l = self.item_emb(likes) * mask_l\n h_l = emb_l.sum(dim=1) / (mask_l.sum(dim=1) + 1e-6)\n\n # strong pool\n mask_s = ((strong > 0).float() * strong_w).unsqueeze(-1)\n emb_s = self.item_emb(strong) * mask_s\n h_s = emb_s.sum(dim=1) / (mask_s.sum(dim=1) + 1e-6)\n\n # recent pool\n mask_r = ((recent > 0).float() * recent_w).unsqueeze(-1)\n emb_r = self.item_emb(recent) * mask_r\n h_r = emb_r.sum(dim=1) / (mask_r.sum(dim=1) + 1e-6)\n\n # dislikes pool\n mask_d = (dislikes > 0).float().unsqueeze(-1)\n emb_d = self.item_emb(dislikes) * mask_d\n h_d = emb_d.sum(dim=1) / (mask_d.sum(dim=1) + 1e-6)\n\n h_stats = F.gelu(self.stats_proj(stats))\n feat = torch.cat([h_l, h_s, h_r, h_d, h_stats], dim=-1)\n u = self.mlp(feat)\n return F.normalize(u, p=2, dim=-1)\n\n\nclass ItemTower(nn.Module):\n def __init__(self, item_embeddings: nn.Embedding, catalog_feats: torch.Tensor, d_in: int, d_hidden: int, d_out: int):\n super().__init__()\n self.item_emb = item_embeddings\n self.register_buffer('catalog_feats', catalog_feats)\n self.feat_proj = nn.Linear(4, 16)\n self.mlp = nn.Sequential(\n nn.Linear(d_in + 16, d_hidden),\n nn.LayerNorm(d_hidden),\n nn.GELU(),\n nn.Linear(d_hidden, d_out),\n )\n\n def forward(self, item_ids):\n emb = self.item_emb(item_ids)\n feats = self.catalog_feats[item_ids]\n h_feats = F.gelu(self.feat_proj(feats))\n x = torch.cat([emb, h_feats], dim=-1)\n v = self.mlp(x)\n return F.normalize(v, p=2, dim=-1)\n\n\ndef build_training_targets(start_ts: int, end_ts: int, uid_to_idx: dict[int, int], item_id_to_idx: dict[int, int]):\n \"\"\"Collect positive training pairs in [start_ts, end_ts].\"\"\"\n # 1. Organic likes\n likes_table = pq.read_table(\n DATA_DIR / 'train_likes.parquet',\n columns=['uid', 'item_id', 'timestamp', 'is_organic'],\n filters=[('timestamp', '>=', start_ts), ('timestamp', '<', end_ts)]\n )\n likes_df = likes_table.to_pandas()\n likes_df = likes_df[(likes_df.is_organic == 1) & (likes_df.uid.isin(uid_to_idx))]\n likes_df['uid_idx'] = likes_df.uid.map(uid_to_idx)\n likes_df['item_idx'] = likes_df.item_id.map(item_id_to_idx)\n likes_df = likes_df.dropna(subset=['item_idx'])\n likes_df['weight'] = 7.0 # Grade 3 gain = 2^3 - 1 = 7.0\n \n # 2. Strong organic listens\n listens_table = pq.read_table(\n DATA_DIR / 'train_listens.parquet',\n columns=['uid', 'item_id', 'timestamp', 'played_ratio_pct', 'is_organic'],\n filters=[('timestamp', '>=', start_ts), ('timestamp', '<', end_ts)]\n )\n listens_df = listens_table.to_pandas()\n strong = listens_df[(listens_df.is_organic == 1) & (listens_df.played_ratio_pct >= 80) & (listens_df.uid.isin(uid_to_idx))]\n strong['uid_idx'] = strong.uid.map(uid_to_idx)\n strong['item_idx'] = strong.item_id.map(item_id_to_idx)\n strong = strong.dropna(subset=['item_idx'])\n strong['weight'] = 1.0 # Grade 1 gain = 2^1 - 1 = 1.0\n \n # Combine; if pair exists in both, keep max weight (7.0)\n comb = pd.concat([likes_df[['uid_idx', 'item_idx', 'weight']], strong[['uid_idx', 'item_idx', 'weight']]])\n comb = comb.groupby(['uid_idx', 'item_idx'], as_index=False)['weight'].max()\n \n uid_idxs = comb.uid_idx.to_numpy(dtype=np.int64)\n item_idxs = comb.item_idx.to_numpy(dtype=np.int64)\n weights = comb.weight.to_numpy(dtype=np.float32)\n return uid_idxs, item_idxs, weights\n\n\ndef train_and_eval():\n print(\"Loading catalog and target users...\")\n item_ids, item_id_to_idx, catalog_feats = get_catalog_data()\n targets_df = pd.read_parquet(DATA_DIR / 'target_users.parquet')\n uids = targets_df.uid.to_numpy()\n uid_to_idx = {int(uid): idx for idx, uid in enumerate(uids)}\n N_users = len(uids)\n num_items = len(item_ids) + 1 # 30001\n \n cutoff_val = 24786800\n t_7d = cutoff_val - 7 * 86400\n t_14d = cutoff_val - 14 * 86400\n \n print(\"Extracting validation user histories at cutoff...\")\n val_hist = extract_user_histories(cutoff_val, item_id_to_idx, uid_to_idx, N_users, is_val=True)\n \n print(\"Extracting training window 1 histories at cutoff - 7d...\")\n train_hist_1 = extract_user_histories(t_7d, item_id_to_idx, uid_to_idx, N_users, is_val=False)\n u_idx_1, itm_idx_1, w_1 = build_training_targets(t_7d, cutoff_val, uid_to_idx, item_id_to_idx)\n print(f\"Window 1 training samples: {len(u_idx_1)}\")\n\n print(\"Extracting training window 2 histories at cutoff - 14d...\")\n train_hist_2 = extract_user_histories(t_14d, item_id_to_idx, uid_to_idx, N_users, is_val=False)\n u_idx_2, itm_idx_2, w_2 = build_training_targets(t_14d, t_7d, uid_to_idx, item_id_to_idx)\n print(f\"Window 2 training samples: {len(u_idx_2)}\")\n \n # Model parameters\n d_emb = 64\n d_hidden = 128\n d_out = 64\n \n torch.manual_seed(42)\n np.random.seed(42)\n \n shared_emb = nn.Embedding(num_items, d_emb, padding_idx=0)\n nn.init.normal_(shared_emb.weight, std=0.02)\n with torch.no_grad():\n shared_emb.weight[0].fill_(0.0)\n \n user_tower = UserTower(shared_emb, d_emb, d_hidden, d_out)\n item_tower = ItemTower(shared_emb, catalog_feats, d_emb, d_hidden, d_out)\n \n # Learnable logit scale (CLIP-style) initialized to 1 / 0.07 ~ 14.28\n logit_scale = nn.Parameter(torch.tensor(math.log(1.0 / 0.07), dtype=torch.float32))\n \n params = list(shared_emb.parameters()) + list(user_tower.parameters()) + list(item_tower.parameters()) + [logit_scale]\n optimizer = torch.optim.AdamW(params, lr=1e-3, weight_decay=1e-4)\n \n # Prepare training dataset by pooling both windows\n # Window flag: 0 for window 1, 1 for window 2\n win_flag = np.concatenate([np.zeros(len(u_idx_1), dtype=np.int32), np.ones(len(u_idx_2), dtype=np.int32)])\n all_u = np.concatenate([u_idx_1, u_idx_2])\n all_itm = np.concatenate([itm_idx_1, itm_idx_2])\n all_w = np.concatenate([w_1, w_2])\n \n N_samples = len(all_u)\n print(f\"Total training samples across 2 windows: {N_samples}\")\n \n batch_size = 512\n epochs = 4\n \n # Ground truth validation for evaluation\n val_elig = CandidateSet(DATA_DIR / 'validation_eligible_candidates.npz')\n graded_val = validation_truth(val_elig, set(uids))\n val_packed = np.load(DATA_DIR / 'validation_eligible_candidates.npz')['packed_eligible']\n val_catalog_items = np.load(DATA_DIR / 'validation_eligible_candidates.npz')['item_ids']\n \n def evaluate_model():\n user_tower.eval()\n item_tower.eval()\n with torch.no_grad():\n # Precompute all 30000 item embeddings\n all_cat_ids = torch.arange(1, num_items, dtype=torch.int64)\n V = item_tower(all_cat_ids) # (30000, d_out)\n \n # Compute user vectors for all 8456 target users\n # Batch users to conserve memory\n user_vecs_list = []\n u_batch_size = 1024\n for s in range(0, N_users, u_batch_size):\n e = min(s + u_batch_size, N_users)\n u_batch = user_tower(\n val_hist['likes'][s:e],\n val_hist['strong'][s:e],\n val_hist['strong_w'][s:e],\n val_hist['recent'][s:e],\n val_hist['recent_w'][s:e],\n val_hist['dislikes'][s:e],\n val_hist['stats'][s:e],\n )\n user_vecs_list.append(u_batch)\n U = torch.cat(user_vecs_list, dim=0) # (8456, d_out)\n \n # Scores: (8456, 30000)\n scores = torch.matmul(U, V.t()).numpy()\n \n # Filter by candidate eligibility\n # unpackbits:\n bits = np.unpackbits(val_packed, axis=1, count=30000, bitorder='little').astype(bool)\n scores[~bits] = -1e9\n \n # Top 100\n ranked = {}\n for row, uid in enumerate(uids):\n top_cols = np.argpartition(scores[row], -100)[-100:]\n # sort top 100 descending\n top_cols = top_cols[np.argsort(-scores[row][top_cols])]\n ranked[int(uid)] = list(map(int, val_catalog_items[top_cols]))\n \n res = score(ranked, graded_val)\n return res\n\n print(\"\\nInitial zero-shot evaluation...\")\n res = evaluate_model()\n print(\"Initial score:\", res)\n\n for epoch in range(1, epochs + 1):\n t0 = time.time()\n user_tower.train()\n item_tower.train()\n \n perm = np.random.permutation(N_samples)\n total_loss = 0.0\n n_batches = 0\n \n for b_start in range(0, N_samples, batch_size):\n b_end = min(b_start + batch_size, N_samples)\n if b_end - b_start < 32:\n continue\n idx = perm[b_start:b_end]\n \n b_u = all_u[idx]\n b_itm = torch.from_numpy(all_itm[idx])\n b_w = torch.from_numpy(all_w[idx])\n b_win = win_flag[idx]\n \n # Separate by window to slice history\n mask_w1 = (b_win == 0)\n mask_w2 = (b_win == 1)\n \n u_vecs = torch.zeros((len(idx), d_out), dtype=torch.float32)\n if np.any(mask_w1):\n idx_w1 = np.where(mask_w1)[0]\n u_w1 = b_u[idx_w1]\n u_vecs[idx_w1] = user_tower(\n train_hist_1['likes'][u_w1],\n train_hist_1['strong'][u_w1],\n train_hist_1['strong_w'][u_w1],\n train_hist_1['recent'][u_w1],\n train_hist_1['recent_w'][u_w1],\n train_hist_1['dislikes'][u_w1],\n train_hist_1['stats'][u_w1],\n )\n if np.any(mask_w2):\n idx_w2 = np.where(mask_w2)[0]\n u_w2 = b_u[idx_w2]\n u_vecs[idx_w2] = user_tower(\n train_hist_2['likes'][u_w2],\n train_hist_2['strong'][u_w2],\n train_hist_2['strong_w'][u_w2],\n train_hist_2['recent'][u_w2],\n train_hist_2['recent_w'][u_w2],\n train_hist_2['dislikes'][u_w2],\n train_hist_2['stats'][u_w2],\n )\n \n v_vecs = item_tower(b_itm)\n \n scale = logit_scale.exp().clamp(max=100.0)\n # In-batch logits: (B, B)\n logits = torch.matmul(u_vecs, v_vecs.t()) * scale\n \n targets = torch.arange(len(idx), dtype=torch.int64)\n # Weighted cross entropy\n ce = F.cross_entropy(logits, targets, reduction='none')\n loss = (ce * b_w).mean()\n \n optimizer.zero_grad()\n loss.backward()\n torch.nn.utils.clip_grad_norm_(params, max_norm=1.0)\n optimizer.step()\n \n total_loss += loss.item()\n n_batches += 1\n \n dur = time.time() - t0\n avg_loss = total_loss / max(n_batches, 1)\n scale_val = logit_scale.exp().item()\n print(f\"Epoch {epoch:02d} completed in {dur:.1f}s | Loss: {avg_loss:.4f} | Scale: {scale_val:.2f}\")\n \n val_res = evaluate_model()\n print(f\"Validation score after epoch {epoch}: {val_res}\")\n\n\nif __name__ == '__main__':\n train_and_eval()\n", "file_path": "/app/solution/train_prototype.py" } ### Tool output · 2026-09-29T06:11:26.066Z Successfully created and wrote to new file: /app/solution/train_prototype.py. Here is the updated code: #!/usr/bin/env python3 """Prototype two-tower neural network ranker for validation experimentation.""" from __future__ import annotations import math import time from pathlib import Path import numpy as np import pandas as pd import pyarrow.parquet as pq import torch import torch.nn as nn import torch.nn.functional as F from ranking_evaluation import CandidateSet, validate_ranking, TOP_N from evaluate_public import validation_truth, score DATA_DIR = Path('/task/data') SOLUTION_DIR = Path('/app/solution') def get_catalog_data(): cat = pd.read_parquet(DATA_DIR / 'candidate_catalog.parquet') item_ids = cat.item_id.to_numpy() item_id_to_idx = {int(id_): idx for idx, id_ in enumerate(item_ids, 1)} # Item features: log1p(pos_listens), log1p(organic_listens), log1p(pop_rank), organic_ratio pos = np.log1p(cat.positive_listens.to_numpy(dtype=np.float32)) org = np.log1p(cat.organic_positive_listens.to_numpy(dtype=np.float32)) pop = np.log1p(cat.popularity_rank.to_numpy(dtype=np.float32)) ratio = (cat.organic_positive_listens / (cat.positive_listens + 1.0)).to_numpy(dtype=np.float32) # Normalize features feats = np.stack([ (pos - pos.mean()) / (pos.std() + 1e-6), (org - org.mean()) / (org.std() + 1e-6), (pop - pop.mean()) / (pop.std() + 1e-6), (ratio - ratio.mean()) / (ratio.std() + 1e-6), ], axis=1) # 0 is padding/unknown feat_tensor = np.zeros((len(item_ids) + 1, 4), dtype=np.float32) feat_tensor[1:] = feats return item_ids, item_id_to_idx, torch.from_numpy(feat_tensor) def extract_user_histories(cutoff_ts: int, item_id_to_idx: dict[int, int], uid_to_idx: dict[int, int], N_users: int, is_val: bool = False): """Extract user history tensors at cutoff_ts.""" K = 64 likes_items = np.zeros((N_users, K), dtype=np.int64) strong_items = np.zeros((N_users, K), dtype=np.int64) strong_weights = np.zeros((N_users, K), dtype=np.float32) recent_items = np.zeros((N_users, K), dtype=np.int64) recent_weights = np.zeros((N_users, K), dtype=np.float32) dislikes_items = np.zeros((N_users, 32), dtype=np.int64) user_stats = np.zeros((N_users, 4), dtype=np.float32) # 1. Likes train_likes = pd.read_parquet(DATA_DIR / 'train_likes.parquet') likes_df = train_likes[(train_likes.timestamp < cutoff_ts) & (train_likes.uid.isin(uid_to_idx))].copy() if is_val: # Also include validation_preference_state liked pref = pd.read_parquet(DATA_DIR / 'validation_preference_state.parquet') liked_pref = pref[(pref.state == 'liked') & (pref.uid.isin(uid_to_idx))].copy() liked_pref['timestamp'] = cutoff_ts - 1 likes_df = pd.concat([likes_df[['uid', 'item_id', 'timestamp']], liked_pref[['uid', 'item_id', 'timestamp']]]) likes_df['uid_idx'] = likes_df.uid.map(uid_to_idx) likes_df['item_idx'] = likes_df.item_id.map(item_id_to_idx) likes_df = likes_df.dropna(subset=['uid_idx', 'item_idx']).sort_values('timestamp') likes_df['uid_idx'] = likes_df.uid_idx.astype(np.int64) likes_df['item_idx'] = likes_df.item_idx.astype(np.int64) # Group by uid_idx for u, g in likes_df.groupby('uid_idx'): itms = g.item_idx.to_numpy() k_take = min(len(itms), K) likes_items[u, :k_take] = itms[-k_take:] user_stats[u, 0] = np.log1p(len(itms)) # 2. Listens in last 45 days t_min = cutoff_ts - 45 * 86400 listens_table = pq.read_table( DATA_DIR / 'train_listens.parquet', columns=['uid', 'item_id', 'timestamp', 'played_ratio_pct', 'is_organic'], filters=[('timestamp', '>=', t_min), ('timestamp', '<', cutoff_ts)] ) listens = listens_table.to_pandas() listens = listens[listens.uid.isin(uid_to_idx)] listens['uid_idx'] = listens.uid.map(uid_to_idx).astype(np.int64) listens['item_idx'] = listens.item_id.map(item_id_to_idx) listens = listens.dropna(subset=['item_idx']) listens['item_idx'] = listens.item_idx.astype(np.int64) listens = listens.sort_values('timestamp') # Strong organic listens (last 45 days, >= 80%, is_organic=1) strong = listens[(listens.is_organic == 1) & (listens.played_ratio_pct >= 80)] decay_scale = 14 * 86400 for u, g in strong.groupby('uid_idx'): itms = g.item_idx.to_numpy() ts = g.timestamp.to_numpy() w = np.exp(-(cutoff_ts - ts) / decay_scale) k_take = min(len(itms), K) strong_items[u, :k_take] = itms[-k_take:] strong_weights[u, :k_take] = w[-k_take:] user_stats[u, 1] = np.log1p(len(itms)) # Recent general listens (last 14 days) t_14d = cutoff_ts - 14 * 86400 recent = listens[listens.timestamp >= t_14d] for u, g in recent.groupby('uid_idx'): itms = g.item_idx.to_numpy() ts = g.timestamp.to_numpy() ratios = g.played_ratio_pct.to_numpy() / 100.0 w = ratios * np.exp(-(cutoff_ts - ts) / (7 * 86400)) k_take = min(len(itms), K) recent_items[u, :k_take] = itms[-k_take:] recent_weights[u, :k_take] = w[-k_take:] user_stats[u, 2] = np.log1p(len(itms)) # Dislikes train_dislikes = pd.read_parquet(DATA_DIR / 'train_dislikes.parquet') dis_df = train_dislikes[(train_dislikes.timestamp < cutoff_ts) & (train_dislikes.uid.isin(uid_to_idx))].copy() if is_val: pref = pd.read_parquet(DATA_DIR / 'validation_preference_state.parquet') dis_pref = pref[(pref.state == 'disliked') & (pref.uid.isin(uid_to_idx))].copy() dis_pref['timestamp'] = cutoff_ts - 1 dis_df = pd.concat([dis_df[['uid', 'item_id', 'timestamp']], dis_pref[['uid', 'item_id', 'timestamp']]]) dis_df['uid_idx'] = dis_df.uid.map(uid_to_idx) dis_df['item_idx'] = dis_df.item_id.map(item_id_to_idx) dis_df = dis_df.dropna(subset=['uid_idx', 'item_idx']).sort_values('timestamp') dis_df['uid_idx'] = dis_df.uid_idx.astype(np.int64) dis_df['item_idx'] = dis_df.item_idx.astype(np.int64) for u, g in dis_df.groupby('uid_idx'): itms = g.item_idx.to_numpy() k_take = min(len(itms), 32) dislikes_items[u, :k_take] = itms[-k_take:] user_stats[u, 3] = np.log1p(len(itms)) # Normalize user stats for col in range(4): s_std = user_stats[:, col].std() if s_std > 1e-6: user_stats[:, col] = (user_stats[:, col] - user_stats[:, col].mean()) / s_std return { 'likes': torch.from_numpy(likes_items), 'strong': torch.from_numpy(strong_items), 'strong_w': torch.from_numpy(strong_weights), 'recent': torch.from_numpy(recent_items), 'recent_w': torch.from_numpy(recent_weights), 'dislikes': torch.from_numpy(dislikes_items), 'stats': torch.from_numpy(user_stats), } class UserTower(nn.Module): def __init__(self, item_embeddings: nn.Embedding, d_in: int, d_hidden: int, d_out: int): super().__init__() self.item_emb = item_embeddings self.stats_proj = nn.Linear(4, 16) in_dim = 4 * d_in + 16 self.mlp = nn.Sequential( nn.Linear(in_dim, d_hidden), nn.LayerNorm(d_hidden), nn.GELU(), nn.Dropout(0.1), nn.Linear(d_hidden, d_out), ) def forward(self, likes, strong, strong_w, recent, recent_w, dislikes, stats): # likes pool mask_l = (likes > 0).float().unsqueeze(-1) emb_l = self.item_emb(likes) * mask_l h_l = emb_l.sum(dim=1) / (mask_l.sum(dim=1) + 1e-6) # strong pool mask_s = ((strong > 0).float() * strong_w).unsqueeze(-1) emb_s = self.item_emb(strong) * mask_s h_s = emb_s.sum(dim=1) / (mask_s.sum(dim=1) + 1e-6) # recent pool mask_r = ((recent > 0).float() * recent_w).unsqueeze(-1) emb_r = self.item_emb(recent) * mask_r h_r = emb_r.sum(dim=1) / (mask_r.sum(dim=1) + 1e-6) # dislikes pool mask_d = (dislikes > 0).float().unsqueeze(-1) emb_d = self.item_emb(dislikes) * mask_d h_d = emb_d.sum(dim=1) / (mask_d.sum(dim=1) + 1e-6) h_stats = F.gelu(self.stats_proj(stats)) feat = torch.cat([h_l, h_s, h_r, h_d, h_stats], dim=-1) u = self.mlp(feat) return F.normalize(u, p=2, dim=-1) class ItemTower(nn.Module): def __init__(self, item_embeddings: nn.Embedding, catalog_feats: torch.Tensor, d_in: int, d_hidden: int, d_out: int): super().__init__() self.item_emb = item_embeddings self.register_buffer('catalog_feats', catalog_feats) self.feat_proj = nn.Linear(4, 16) self.mlp = nn.Sequential( nn.Linear(d_in + 16, d_hidden), nn.LayerNorm(d_hidden), nn.GELU(), nn.Linear(d_hidden, d_out), ) def forward(self, item_ids): emb = self.item_emb(item_ids) feats = self.catalog_feats[item_ids] h_feats = F.gelu(self.feat_proj(feats)) x = torch.cat([emb, h_feats], dim=-1) v = self.mlp(x) return F.normalize(v, p=2, dim=-1) def build_training_targets(start_ts: int, end_ts: int, uid_to_idx: dict[int, int], item_id_to_idx: dict[int, int]): """Collect positive training pairs in [start_ts, end_ts].""" # 1. Organic likes likes_table = pq.read_table( DATA_DIR / 'train_likes.parquet', columns=['uid', 'item_id', 'timestamp', 'is_organic'], filters=[('timestamp', '>=', start_ts), ('timestamp', '<', end_ts)] ) likes_df = likes_table.to_pandas() likes_df = likes_df[(likes_df.is_organic == 1) & (likes_df.uid.isin(uid_to_idx))] likes_df['uid_idx'] = likes_df.uid.map(uid_to_idx) likes_df['item_idx'] = likes_df.item_id.map(item_id_to_idx) likes_df = likes_df.dropna(subset=['item_idx']) likes_df['weight'] = 7.0 # Grade 3 gain = 2^3 - 1 = 7.0 # 2. Strong organic listens listens_table = pq.read_table( DATA_DIR / 'train_listens.parquet', columns=['uid', 'item_id', 'timestamp', 'played_ratio_pct', 'is_organic'], filters=[('timestamp', '>=', start_ts), ('timestamp', '<', end_ts)] ) listens_df = listens_table.to_pandas() strong = listens_df[(listens_df.is_organic == 1) & (listens_df.played_ratio_pct >= 80) & (listens_df.uid.isin(uid_to_idx))] strong['uid_idx'] = strong.uid.map(uid_to_idx) strong['item_idx'] = strong.item_id.map(item_id_to_idx) strong = strong.dropna(subset=['item_idx']) strong['weight'] = 1.0 # Grade 1 gain = 2^1 - 1 = 1.0 # Combine; if pair exists in both, keep max weight (7.0) comb = pd.concat([likes_df[['uid_idx', 'item_idx', 'weight']], strong[['uid_idx', 'item_idx', 'weight']]]) comb = comb.groupby(['uid_idx', 'item_idx'], as_index=False)['weight'].max() uid_idxs = comb.uid_idx.to_numpy(dtype=np.int64) item_idxs = comb.item_idx.to_numpy(dtype=np.int64) weights = comb.weight.to_numpy(dtype=np.float32) return uid_idxs, item_idxs, weights def train_and_eval(): print("Loading catalog and target users...") item_ids, item_id_to_idx, catalog_feats = get_catalog_data() targets_df = pd.read_parquet(DATA_DIR / 'target_users.parquet') uids = targets_df.uid.to_numpy() uid_to_idx = {int(uid): idx for idx, uid in enumerate(uids)} N_users = len(uids) num_items = len(item_ids) + 1 # 30001 cutoff_val = 24786800 t_7d = cutoff_val - 7 * 86400 t_14d = cutoff_val - 14 * 86400 print("Extracting validation user histories at cutoff...") val_hist = extract_user_histories(cutoff_val, item_id_to_idx, uid_to_idx, N_users, is_val=True) print("Extracting training window 1 histories at cutoff - 7d...") train_hist_1 = extract_user_histories(t_7d, item_id_to_idx, uid_to_idx, N_users, is_val=False) u_idx_1, itm_idx_1, w_1 = build_training_targets(t_7d, cutoff_val, uid_to_idx, item_id_to_idx) print(f"Window 1 training samples: {len(u_idx_1)}") print("Extracting training window 2 histories at cutoff - 14d...") train_hist_2 = extract_user_histories(t_14d, item_id_to_idx, uid_to_idx, N_users, is_val=False) u_idx_2, itm_idx_2, w_2 = build_training_targets(t_14d, t_7d, uid_to_idx, item_id_to_idx) print(f"Window 2 training samples: {len(u_idx_2)}") # Model parameters d_emb = 64 d_hidden = 128 d_out = 64 torch.manual_seed(42) np.random.seed(42) shared_emb = nn.Embedding(num_items, d_emb, padding_idx=0) nn.init.normal_(shared_emb.weight, std=0.02) with torch.no_grad(): shared_emb.weight[0].fill_(0.0) user_tower = UserTower(shared_emb, d_emb, d_hidden, d_out) item_tower = ItemTower(shared_emb, catalog_feats, d_emb, d_hidden, d_out) # Learnable logit scale (CLIP-style) initialized to 1 / 0.07 ~ 14.28 logit_scale = nn.Parameter(torch.tensor(math.log(1.0 / 0.07), dtype=torch.float32)) params = list(shared_emb.parameters()) + list(user_tower.parameters()) + list(item_tower.parameters()) + [logit_scale] optimizer = torch.optim.AdamW(params, lr=1e-3, weight_decay=1e-4) # Prepare training dataset by pooling both windows # Window flag: 0 for window 1, 1 for window 2 win_flag = np.concatenate([np.zeros(len(u_idx_1), dtype=np.int32), np.ones(len(u_idx_2), dtype=np.int32)]) all_u = np.concatenate([u_idx_1, u_idx_2]) all_itm = np.concatenate([itm_idx_1, itm_idx_2]) all_w = np.concatenate([w_1, w_2]) N_samples = len(all_u) print(f"Total training samples across 2 windows: {N_samples}") batch_size = 512 epochs = 4 # Ground truth validation for evaluation val_elig = CandidateSet(DATA_DIR / 'validation_eligible_candidates.npz') graded_val = validation_truth(val_elig, set(uids)) val_packed = np.load(DATA_DIR / 'validation_eligible_candidates.npz')['packed_eligible'] val_catalog_items = np.load(DATA_DIR / 'validation_eligible_candidates.npz')['item_ids'] def evaluate_model(): user_tower.eval() item_tower.eval() with torch.no_grad(): # Precompute all 30000 item embeddings all_cat_ids = torch.arange(1, num_items, dtype=torch.int64) V = item_tower(all_cat_ids) # (30000, d_out) # Compute user vectors for all 8456 target users # Batch users to conserve memory user_vecs_list = [] u_batch_size = 1024 for s in range(0, N_users, u_batch_size): e = min(s + u_batch_size, N_users) u_batch = user_tower( val_hist['likes'][s:e], val_hist['strong'][s:e], val_hist['strong_w'][s:e], val_hist['recent'][s:e], val_hist['recent_w'][s:e], val_hist['dislikes'][s:e], val_hist['stats'][s:e], ) user_vecs_list.append(u_batch) U = torch.cat(user_vecs_list, dim=0) # (8456, d_out) # Scores: (8456, 30000) scores = torch.matmul(U, V.t()).numpy() # Filter by candidate eligibility # unpackbits: bits = np.unpackbits(val_packed, axis=1, count=30000, bitorder='little').astype(bool) scores[~bits] = -1e9 # Top 100 ranked = {} for row, uid in enumerate(uids): top_cols = np.argpartition(scores[row], -100)[-100:] # sort top 100 descending top_cols = top_cols[np.argsort(-scores[row][top_cols])] ranked[int(uid)] = list(map(int, val_catalog_items[top_cols])) res = score(ranked, graded_val) return res print("\nInitial zero-shot evaluation...") res = evaluate_model() print("Initial score:", res) for epoch in range(1, epochs + 1): t0 = time.time() user_tower.train() item_tower.train() perm = np.random.permutation(N_samples) total_loss = 0.0 n_batches = 0 for b_start in range(0, N_samples, batch_size): b_end = min(b_start + batch_size, N_samples) if b_end - b_start < 32: continue idx = perm[b_start:b_end] b_u = all_u[idx] b_itm = torch.from_numpy(all_itm[idx]) b_w = torch.from_numpy(all_w[idx]) b_win = win_flag[idx] # Separate by window to slice history mask_w1 = (b_win == 0) mask_w2 = (b_win == 1) u_vecs = torch.zeros((len(idx), d_out), dtype=torch.float32) if np.any(mask_w1): idx_w1 = np.where(mask_w1)[0] u_w1 = b_u[idx_w1] u_vecs[idx_w1] = user_tower( train_hist_1['likes'][u_w1], train_hist_1['strong'][u_w1], train_hist_1['strong_w'][u_w1], train_hist_1['recent'][u_w1], train_hist_1['recent_w'][u_w1], train_hist_1['dislikes'][u_w1], train_hist_1['stats'][u_w1], ) if np.any(mask_w2): idx_w2 = np.where(mask_w2)[0] u_w2 = b_u[idx_w2] u_vecs[idx_w2] = user_tower( train_hist_2['likes'][u_w2], train_hist_2['strong'][u_w2], train_hist_2['strong_w'][u_w2], train_hist_2['recent'][u_w2], train_hist_2['recent_w'][u_w2], train_hist_2['dislikes'][u_w2], train_hist_2['stats'][u_w2], ) v_vecs = item_tower(b_itm) scale = logit_scale.exp().clamp(max=100.0) # In-batch logits: (B, B) logits = torch.matmul(u_vecs, v_vecs.t()) * scale targets = torch.arange(len(idx), dtype=torch.int64) # Weighted cross entropy ce = F.cross_entropy(logits, targets, reduction='none') loss = (ce * b_w).mean() optimizer.zero_grad() loss.backward() torch.nn.utils.clip_grad_norm_(params, max_norm=1.0) optimizer.step() total_loss += loss.item() n_batches += 1 dur = time.time() - t0 avg_loss = total_loss / max(n_batches, 1) scale_val = logit_scale.exp().item() print(f"Epoch {epoch:02d} completed in {dur:.1f}s | Loss: {avg_loss:.4f} | Scale: {scale_val:.2f}") val_res = evaluate_model() print(f"Validation score after epoch {epoch}: {val_res}") if __name__ == '__main__': train_and_eval() ## Step 52 · agent · 2026-09-29T06:11:29.473Z ### run_shell_command · 2026-09-29T06:11:29.473Z { "command": "PYTHONPATH=/task python3 /app/solution/train_prototype.py", "description": "Run prototype two-tower training and evaluation" } ### Tool output · 2026-09-29T06:13:35.967Z Output: /app/solution/train_prototype.py:104: RuntimeWarning: overflow encountered in exp w = np.exp(-(cutoff_ts - ts) / decay_scale) /app/solution/train_prototype.py:117: RuntimeWarning: overflow encountered in exp w = ratios * np.exp(-(cutoff_ts - ts) / (7 * 86400)) /app/solution/train_prototype.py:117: RuntimeWarning: invalid value encountered in multiply w = ratios * np.exp(-(cutoff_ts - ts) / (7 * 86400)) /app/solution/train_prototype.py:104: RuntimeWarning: overflow encountered in exp w = np.exp(-(cutoff_ts - ts) / decay_scale) /app/solution/train_prototype.py:117: RuntimeWarning: overflow encountered in exp w = ratios * np.exp(-(cutoff_ts - ts) / (7 * 86400)) /app/solution/train_prototype.py:117: RuntimeWarning: invalid value encountered in multiply w = ratios * np.exp(-(cutoff_ts - ts) / (7 * 86400)) /app/solution/train_prototype.py:245: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy strong['uid_idx'] = strong.uid.map(uid_to_idx) /app/solution/train_prototype.py:246: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy strong['item_idx'] = strong.item_id.map(item_id_to_idx) /app/solution/train_prototype.py:104: RuntimeWarning: overflow encountered in exp w = np.exp(-(cutoff_ts - ts) / decay_scale) /app/solution/train_prototype.py:117: RuntimeWarning: overflow encountered in exp w = ratios * np.exp(-(cutoff_ts - ts) / (7 * 86400)) /app/solution/train_prototype.py:117: RuntimeWarning: invalid value encountered in multiply w = ratios * np.exp(-(cutoff_ts - ts) / (7 * 86400)) /app/solution/train_prototype.py:245: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy strong['uid_idx'] = strong.uid.map(uid_to_idx) /app/solution/train_prototype.py:246: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy strong['item_idx'] = strong.item_id.map(item_id_to_idx) /opt/task-venv/lib/python3.12/site-packages/torch/_compile.py:53: UserWarning: optimizer contains a parameter group with duplicate parameters; in future, this will cause an error; see github.com/pytorch/pytorch/issues/40967 for more information return disable_fn(*args, **kwargs) Loading catalog and target users... Extracting validation user histories at cutoff... Extracting training window 1 histories at cutoff - 7d... Window 1 training samples: 176193 Extracting training window 2 histories at cutoff - 14d... Window 2 training samples: 171289 Total training samples across 2 windows: 347482 Initial zero-shot evaluation... Initial score: {'quality_ndcg@10': 0.000196, 'quality_users': 4263} Epoch 01 completed in 26.3s | Loss: nan | Scale: nan Validation score after epoch 1: {'quality_ndcg@10': 0.000196, 'quality_users': 4263} Epoch 02 completed in 23.1s | Loss: nan | Scale: nan Validation score after epoch 2: {'quality_ndcg@10': 0.000196, 'quality_users': 4263} Epoch 03 completed in 24.2s | Loss: nan | Scale: nan Validation score after epoch 3: {'quality_ndcg@10': 0.000196, 'quality_users': 4263} Epoch 04 completed in 23.3s | Loss: nan | Scale: nan Validation score after epoch 4: {'quality_ndcg@10': 0.000196, 'quality_users': 4263} Process Group PGID: 2532 ## Step 53 · agent · 2026-09-29T06:15:27.294Z ### Recorded reasoning summary Analyzing Prototype Warnings I'm currently investigating a prototype run's warnings, specifically an overflow in `np.exp`. My focus is on understanding why `cutoff_ts - ts` is causing this, given that `ts` is a uint32. ### Recorded reasoning summary Resolving Data Underflow I've identified that unsigned integer subtraction is causing `cutoff_ts - ts` to underflow, leading to massive positive values. This then causes overflows or NaNs in `np.exp`. I'm implementing clipping and converting to float64 for safe subtraction to prevent this. I'm also addressing duplicate parameter assignments in the towers. ### run_shell_command · 2026-09-29T06:15:27.294Z { "description": "Test fixed timestamp difference calculation", "command": "python3 -c \"\nimport numpy as np\ncutoff = 24786800\nts = np.array([24000000, 24786800], dtype=np.uint32)\ndiff = np.maximum(0.0, float(cutoff) - ts.astype(np.float64))\nw = np.exp(-diff / (14 * 86400))\nprint('Weights:', w, 'has nan?', np.isnan(w).any())\n\"" } ### Tool output · 2026-09-29T06:15:27.629Z Output: Weights: [0.52180414 1. ] has nan? False Process Group PGID: 2581 ## Step 54 · agent · 2026-09-29T06:15:43.778Z ### Recorded reasoning summary Investigating NaN Gradients I've confirmed the data is clean with no NaNs present. My current focus is on pinpointing the source of the NaN gradients within the `scale` calculation, which appears to originate from `logits`. I'm now examining the interplay between `user_tower` and `item_tower` forward passes, specifically how they handle in-batch negatives in `train_prototype.py`. ### replace · 2026-09-29T06:15:43.778Z { "new_string": " # Strong organic listens (last 45 days, >= 80%, is_organic=1)\n strong = listens[(listens.is_organic == 1) & (listens.played_ratio_pct >= 80)]\n decay_scale = 14.0 * 86400.0\n for u, g in strong.groupby('uid_idx'):\n itms = g.item_idx.to_numpy()\n ts = g.timestamp.to_numpy(dtype=np.float64)\n diff = np.maximum(0.0, float(cutoff_ts) - ts)\n w = np.exp(-diff / decay_scale).astype(np.float32)\n k_take = min(len(itms), K)\n strong_items[u, :k_take] = itms[-k_take:]\n strong_weights[u, :k_take] = w[-k_take:]\n user_stats[u, 1] = np.log1p(len(itms))\n \n # Recent general listens (last 14 days)\n t_14d = cutoff_ts - 14 * 86400\n recent = listens[listens.timestamp >= t_14d]\n for u, g in recent.groupby('uid_idx'):\n itms = g.item_idx.to_numpy()\n ts = g.timestamp.to_numpy(dtype=np.float64)\n ratios = (g.played_ratio_pct.to_numpy(dtype=np.float64) / 100.0).astype(np.float32)\n diff = np.maximum(0.0, float(cutoff_ts) - ts)\n w = ratios * np.exp(-diff / (7.0 * 86400.0)).astype(np.float32)\n k_take = min(len(itms), K)\n recent_items[u, :k_take] = itms[-k_take:]\n recent_weights[u, :k_take] = w[-k_take:]\n user_stats[u, 2] = np.log1p(len(itms))", "allow_multiple": false, "file_path": "/app/solution/train_prototype.py", "old_string": " # Strong organic listens (last 45 days, >= 80%, is_organic=1)\n strong = listens[(listens.is_organic == 1) & (listens.played_ratio_pct >= 80)]\n decay_scale = 14 * 86400\n for u, g in strong.groupby('uid_idx'):\n itms = g.item_idx.to_numpy()\n ts = g.timestamp.to_numpy()\n w = np.exp(-(cutoff_ts - ts) / decay_scale)\n k_take = min(len(itms), K)\n strong_items[u, :k_take] = itms[-k_take:]\n strong_weights[u, :k_take] = w[-k_take:]\n user_stats[u, 1] = np.log1p(len(itms))\n \n # Recent general listens (last 14 days)\n t_14d = cutoff_ts - 14 * 86400\n recent = listens[listens.timestamp >= t_14d]\n for u, g in recent.groupby('uid_idx'):\n itms = g.item_idx.to_numpy()\n ts = g.timestamp.to_numpy()\n ratios = g.played_ratio_pct.to_numpy() / 100.0\n w = ratios * np.exp(-(cutoff_ts - ts) / (7 * 86400))\n k_take = min(len(itms), K)\n recent_items[u, :k_take] = itms[-k_take:]\n recent_weights[u, :k_take] = w[-k_take:]\n user_stats[u, 2] = np.log1p(len(itms))", "instruction": "Fix unsigned timestamp overflow, prevent NaN, ensure distinct users per batch, and add catalog negative samples." } ### Tool output · 2026-09-29T06:15:43.792Z Successfully modified file: /app/solution/train_prototype.py (1 replacements). Here is the updated code: ... listens['item_idx'] = listens.item_idx.astype(np.int64) listens = listens.sort_values('timestamp') # Strong organic listens (last 45 days, >= 80%, is_organic=1) strong = listens[(listens.is_organic == 1) & (listens.played_ratio_pct >= 80)] decay_scale = 14.0 * 86400.0 for u, g in strong.groupby('uid_idx'): itms = g.item_idx.to_numpy() ts = g.timestamp.to_numpy(dtype=np.float64) diff = np.maximum(0.0, float(cutoff_ts) - ts) w = np.exp(-diff / decay_scale).astype(np.float32) k_take = min(len(itms), K) strong_items[u, :k_take] = itms[-k_take:] strong_weights[u, :k_take] = w[-k_take:] user_stats[u, 1] = np.log1p(len(itms)) # Recent general listens (last 14 days) t_14d = cutoff_ts - 14 * 86400 recent = listens[listens.timestamp >= t_14d] for u, g in recent.groupby('uid_idx'): itms = g.item_idx.to_numpy() ts = g.timestamp.to_numpy(dtype=np.float64) ratios = (g.played_ratio_pct.to_numpy(dtype=np.float64) / 100.0).astype(np.float32) diff = np.maximum(0.0, float(cutoff_ts) - ts) w = ratios * np.exp(-diff / (7.0 * 86400.0)).astype(np.float32) k_take = min(len(itms), K) recent_items[u, :k_take] = itms[-k_take:] recent_weights[u, :k_take] = w[-k_take:] user_stats[u, 2] = np.log1p(len(itms)) ... ## Step 55 · agent · 2026-09-29T06:15:49.876Z ### read_file · 2026-09-29T06:15:49.876Z { "file_path": "/app/solution/train_prototype.py", "start_line": 280, "end_line": 360 } ### Tool output · 2026-09-29T06:15:49.888Z IMPORTANT: The file content has been truncated. Status: Showing lines 280-360 of 458 total lines. Action: To read more of the file, you can use the 'start_line' and 'end_line' parameters in a subsequent 'read_file' call. For example, to read the next section of the file, use start_line: 361. --- FILE CONTENT (truncated) --- u_idx_1, itm_idx_1, w_1 = build_training_targets(t_7d, cutoff_val, uid_to_idx, item_id_to_idx) print(f"Window 1 training samples: {len(u_idx_1)}") print("Extracting training window 2 histories at cutoff - 14d...") train_hist_2 = extract_user_histories(t_14d, item_id_to_idx, uid_to_idx, N_users, is_val=False) u_idx_2, itm_idx_2, w_2 = build_training_targets(t_14d, t_7d, uid_to_idx, item_id_to_idx) print(f"Window 2 training samples: {len(u_idx_2)}") # Model parameters d_emb = 64 d_hidden = 128 d_out = 64 torch.manual_seed(42) np.random.seed(42) shared_emb = nn.Embedding(num_items, d_emb, padding_idx=0) nn.init.normal_(shared_emb.weight, std=0.02) with torch.no_grad(): shared_emb.weight[0].fill_(0.0) user_tower = UserTower(shared_emb, d_emb, d_hidden, d_out) item_tower = ItemTower(shared_emb, catalog_feats, d_emb, d_hidden, d_out) # Learnable logit scale (CLIP-style) initialized to 1 / 0.07 ~ 14.28 logit_scale = nn.Parameter(torch.tensor(math.log(1.0 / 0.07), dtype=torch.float32)) params = list(shared_emb.parameters()) + list(user_tower.parameters()) + list(item_tower.parameters()) + [logit_scale] optimizer = torch.optim.AdamW(params, lr=1e-3, weight_decay=1e-4) # Prepare training dataset by pooling both windows # Window flag: 0 for window 1, 1 for window 2 win_flag = np.concatenate([np.zeros(len(u_idx_1), dtype=np.int32), np.ones(len(u_idx_2), dtype=np.int32)]) all_u = np.concatenate([u_idx_1, u_idx_2]) all_itm = np.concatenate([itm_idx_1, itm_idx_2]) all_w = np.concatenate([w_1, w_2]) N_samples = len(all_u) print(f"Total training samples across 2 windows: {N_samples}") batch_size = 512 epochs = 4 # Ground truth validation for evaluation val_elig = CandidateSet(DATA_DIR / 'validation_eligible_candidates.npz') graded_val = validation_truth(val_elig, set(uids)) val_packed = np.load(DATA_DIR / 'validation_eligible_candidates.npz')['packed_eligible'] val_catalog_items = np.load(DATA_DIR / 'validation_eligible_candidates.npz')['item_ids'] def evaluate_model(): user_tower.eval() item_tower.eval() with torch.no_grad(): # Precompute all 30000 item embeddings all_cat_ids = torch.arange(1, num_items, dtype=torch.int64) V = item_tower(all_cat_ids) # (30000, d_out) # Compute user vectors for all 8456 target users # Batch users to conserve memory user_vecs_list = [] u_batch_size = 1024 for s in range(0, N_users, u_batch_size): e = min(s + u_batch_size, N_users) u_batch = user_tower( val_hist['likes'][s:e], val_hist['strong'][s:e], val_hist['strong_w'][s:e], val_hist['recent'][s:e], val_hist['recent_w'][s:e], val_hist['dislikes'][s:e], val_hist['stats'][s:e], ) user_vecs_list.append(u_batch) U = torch.cat(user_vecs_list, dim=0) # (8456, d_out) # Scores: (8456, 30000) scores = torch.matmul(U, V.t()).numpy() # Filter by candidate eligibility # unpackbits: bits = np.unpackbits(val_packed, axis=1, count=30000, bitorder='little').astype(bool) ## Step 56 · agent · 2026-09-29T06:15:53.381Z ### read_file · 2026-09-29T06:15:53.381Z { "start_line": 360, "file_path": "/app/solution/train_prototype.py", "end_line": 435 } ### Tool output · 2026-09-29T06:15:53.389Z IMPORTANT: The file content has been truncated. Status: Showing lines 360-435 of 458 total lines. Action: To read more of the file, you can use the 'start_line' and 'end_line' parameters in a subsequent 'read_file' call. For example, to read the next section of the file, use start_line: 436. --- FILE CONTENT (truncated) --- bits = np.unpackbits(val_packed, axis=1, count=30000, bitorder='little').astype(bool) scores[~bits] = -1e9 # Top 100 ranked = {} for row, uid in enumerate(uids): top_cols = np.argpartition(scores[row], -100)[-100:] # sort top 100 descending top_cols = top_cols[np.argsort(-scores[row][top_cols])] ranked[int(uid)] = list(map(int, val_catalog_items[top_cols])) res = score(ranked, graded_val) return res print("\nInitial zero-shot evaluation...") res = evaluate_model() print("Initial score:", res) for epoch in range(1, epochs + 1): t0 = time.time() user_tower.train() item_tower.train() perm = np.random.permutation(N_samples) total_loss = 0.0 n_batches = 0 for b_start in range(0, N_samples, batch_size): b_end = min(b_start + batch_size, N_samples) if b_end - b_start < 32: continue idx = perm[b_start:b_end] b_u = all_u[idx] b_itm = torch.from_numpy(all_itm[idx]) b_w = torch.from_numpy(all_w[idx]) b_win = win_flag[idx] # Separate by window to slice history mask_w1 = (b_win == 0) mask_w2 = (b_win == 1) u_vecs = torch.zeros((len(idx), d_out), dtype=torch.float32) if np.any(mask_w1): idx_w1 = np.where(mask_w1)[0] u_w1 = b_u[idx_w1] u_vecs[idx_w1] = user_tower( train_hist_1['likes'][u_w1], train_hist_1['strong'][u_w1], train_hist_1['strong_w'][u_w1], train_hist_1['recent'][u_w1], train_hist_1['recent_w'][u_w1], train_hist_1['dislikes'][u_w1], train_hist_1['stats'][u_w1], ) if np.any(mask_w2): idx_w2 = np.where(mask_w2)[0] u_w2 = b_u[idx_w2] u_vecs[idx_w2] = user_tower( train_hist_2['likes'][u_w2], train_hist_2['strong'][u_w2], train_hist_2['strong_w'][u_w2], train_hist_2['recent'][u_w2], train_hist_2['recent_w'][u_w2], train_hist_2['dislikes'][u_w2], train_hist_2['stats'][u_w2], ) v_vecs = item_tower(b_itm) scale = logit_scale.exp().clamp(max=100.0) # In-batch logits: (B, B) logits = torch.matmul(u_vecs, v_vecs.t()) * scale targets = torch.arange(len(idx), dtype=torch.int64) # Weighted cross entropy ## Step 57 · agent · 2026-09-29T06:16:03.365Z ### Recorded reasoning summary Refining Parameter List I'm currently reviewing the parameter list for inclusion. I've confirmed that `user_tower` already incorporates `shared_emb`. My next step is to verify if `item_tower` introduces any additional parameters beyond `shared_emb` to ensure a comprehensive and accurate collection. ### replace · 2026-09-29T06:16:03.365Z { "new_string": " # 1. Organic likes\n likes_table = pq.read_table(\n DATA_DIR / 'train_likes.parquet',\n columns=['uid', 'item_id', 'timestamp', 'is_organic'],\n filters=[('timestamp', '>=', start_ts), ('timestamp', '<', end_ts)]\n )\n likes_df = likes_table.to_pandas()\n likes_df = likes_df[(likes_df.is_organic == 1) & (likes_df.uid.isin(uid_to_idx))].copy()\n likes_df['uid_idx'] = likes_df.uid.map(uid_to_idx)\n likes_df['item_idx'] = likes_df.item_id.map(item_id_to_idx)\n likes_df = likes_df.dropna(subset=['item_idx'])\n likes_df['weight'] = 7.0 # Grade 3 gain = 2^3 - 1 = 7.0\n \n # 2. Strong organic listens\n listens_table = pq.read_table(\n DATA_DIR / 'train_listens.parquet',\n columns=['uid', 'item_id', 'timestamp', 'played_ratio_pct', 'is_organic'],\n filters=[('timestamp', '>=', start_ts), ('timestamp', '<', end_ts)]\n )\n listens_df = listens_table.to_pandas()\n strong = listens_df[(listens_df.is_organic == 1) & (listens_df.played_ratio_pct >= 80) & (listens_df.uid.isin(uid_to_idx))].copy()\n strong['uid_idx'] = strong.uid.map(uid_to_idx)\n strong['item_idx'] = strong.item_id.map(item_id_to_idx)\n strong = strong.dropna(subset=['item_idx'])\n strong['weight'] = 1.0 # Grade 1 gain = 2^1 - 1 = 1.0", "file_path": "/app/solution/train_prototype.py", "allow_multiple": false, "old_string": " # 1. Organic likes\n likes_table = pq.read_table(\n DATA_DIR / 'train_likes.parquet',\n columns=['uid', 'item_id', 'timestamp', 'is_organic'],\n filters=[('timestamp', '>=', start_ts), ('timestamp', '<', end_ts)]\n )\n likes_df = likes_table.to_pandas()\n likes_df = likes_df[(likes_df.is_organic == 1) & (likes_df.uid.isin(uid_to_idx))]\n likes_df['uid_idx'] = likes_df.uid.map(uid_to_idx)\n likes_df['item_idx'] = likes_df.item_id.map(item_id_to_idx)\n likes_df = likes_df.dropna(subset=['item_idx'])\n likes_df['weight'] = 7.0 # Grade 3 gain = 2^3 - 1 = 7.0\n \n # 2. Strong organic listens\n listens_table = pq.read_table(\n DATA_DIR / 'train_listens.parquet',\n columns=['uid', 'item_id', 'timestamp', 'played_ratio_pct', 'is_organic'],\n filters=[('timestamp', '>=', start_ts), ('timestamp', '<', end_ts)]\n )\n listens_df = listens_table.to_pandas()\n strong = listens_df[(listens_df.is_organic == 1) & (listens_df.played_ratio_pct >= 80) & (listens_df.uid.isin(uid_to_idx))]\n strong['uid_idx'] = strong.uid.map(uid_to_idx)\n strong['item_idx'] = strong.item_id.map(item_id_to_idx)\n strong = strong.dropna(subset=['item_idx'])\n strong['weight'] = 1.0 # Grade 1 gain = 2^1 - 1 = 1.0", "instruction": "Fix optimizer duplicate parameters and pandas copy warnings" } ### Tool output · 2026-09-29T06:16:03.378Z Successfully modified file: /app/solution/train_prototype.py (1 replacements). Here is the updated code: ... DATA_DIR / 'train_likes.parquet', columns=['uid', 'item_id', 'timestamp', 'is_organic'], filters=[('timestamp', '>=', start_ts), ('timestamp', '<', end_ts)] ) likes_df = likes_table.to_pandas() likes_df = likes_df[(likes_df.is_organic == 1) & (likes_df.uid.isin(uid_to_idx))].copy() likes_df['uid_idx'] = likes_df.uid.map(uid_to_idx) likes_df['item_idx'] = likes_df.item_id.map(item_id_to_idx) likes_df = likes_df.dropna(subset=['item_idx']) likes_df['weight'] = 7.0 # Grade 3 gain = 2^3 - 1 = 7.0 ... DATA_DIR / 'train_listens.parquet', columns=['uid', 'item_id', 'timestamp', 'played_ratio_pct', 'is_organic'], filters=[('timestamp', '>=', start_ts), ('timestamp', '<', end_ts)] ) listens_df = listens_table.to_pandas() strong = listens_df[(listens_df.is_organic == 1) & (listens_df.played_ratio_pct >= 80) & (listens_df.uid.isin(uid_to_idx))].copy() strong['uid_idx'] = strong.uid.map(uid_to_idx) strong['item_idx'] = strong.item_id.map(item_id_to_idx) strong = strong.dropna(subset=['item_idx']) strong['weight'] = 1.0 # Grade 1 gain = 2^1 - 1 = 1.0 ... ## Step 58 · agent · 2026-09-29T06:16:07.107Z ### replace · 2026-09-29T06:16:07.107Z { "instruction": "Fix params list to avoid duplicate shared_emb", "allow_multiple": false, "new_string": " params = list(user_tower.parameters()) + list(item_tower.feat_proj.parameters()) + list(item_tower.mlp.parameters()) + [logit_scale]\n optimizer = torch.optim.AdamW(params, lr=1e-3, weight_decay=1e-4)", "old_string": " params = list(shared_emb.parameters()) + list(user_tower.parameters()) + list(item_tower.parameters()) + [logit_scale]\n optimizer = torch.optim.AdamW(params, lr=1e-3, weight_decay=1e-4)", "file_path": "/app/solution/train_prototype.py" } ### Tool output · 2026-09-29T06:16:07.120Z Successfully modified file: /app/solution/train_prototype.py (1 replacements). Here is the updated code: ... item_tower = ItemTower(shared_emb, catalog_feats, d_emb, d_hidden, d_out) # Learnable logit scale (CLIP-style) initialized to 1 / 0.07 ~ 14.28 logit_scale = nn.Parameter(torch.tensor(math.log(1.0 / 0.07), dtype=torch.float32)) params = list(user_tower.parameters()) + list(item_tower.feat_proj.parameters()) + list(item_tower.mlp.parameters()) + [logit_scale] optimizer = torch.optim.AdamW(params, lr=1e-3, weight_decay=1e-4) # Prepare training dataset by pooling both windows # Window flag: 0 for window 1, 1 for window 2 win_flag = np.concatenate([np.zeros(len(u_idx_1), dtype=np.int32), np.ones(len(u_idx_2), dtype=np.int32)]) ... ## Step 59 · agent · 2026-09-29T06:16:10.338Z ### run_shell_command · 2026-09-29T06:16:10.338Z { "description": "Run prototype training script", "command": "PYTHONPATH=/task python3 /app/solution/train_prototype.py" } ### Tool output · 2026-09-29T06:17:54.524Z Output: Loading catalog and target users... Extracting validation user histories at cutoff... Extracting training window 1 histories at cutoff - 7d... Window 1 training samples: 176193 Extracting training window 2 histories at cutoff - 14d... Window 2 training samples: 171289 Total training samples across 2 windows: 347482 Initial zero-shot evaluation... Initial score: {'quality_ndcg@10': 0.000416, 'quality_users': 4263} Epoch 01 completed in 17.1s | Loss: 7.2516 | Scale: 13.36 Validation score after epoch 1: {'quality_ndcg@10': 0.004754, 'quality_users': 4263} Epoch 02 completed in 18.1s | Loss: 6.5804 | Scale: 13.80 Validation score after epoch 2: {'quality_ndcg@10': 0.006032, 'quality_users': 4263} Epoch 03 completed in 18.6s | Loss: 6.0869 | Scale: 14.96 Validation score after epoch 3: {'quality_ndcg@10': 0.004829, 'quality_users': 4263} Epoch 04 completed in 20.2s | Loss: 5.7046 | Scale: 16.59 Validation score after epoch 4: {'quality_ndcg@10': 0.005476, 'quality_users': 4263} Process Group PGID: 2597 ## Step 60 · agent · 2026-09-29T06:18:41.776Z ### Recorded reasoning summary Analyzing Current Performance The initial prototype trained exceptionally well, hitting a remarkable 0.006 in just epoch 2. My focus is now on dissecting this success; I'm diving deep into error analysis to understand precisely why this outcome is so promising compared to the popularity baseline of 0.0107, and strategizing how to push these metrics even higher. ### Recorded reasoning summary Investigating Item Embeddings I'm currently examining the specific items my prototype is ranking, curious about their obscurity and if they lean too heavily towards niche content. My investigation into the item tower's feature engineering revealed that popularity features are L2-normalized, which prevents them from directly scaling the dot product score. This is a crucial detail I need to consider further. ### Recorded reasoning summary Incorporating Item Bias I've realized that item bias can indeed be incorporated into a two-tower model by simply extending the user and item vectors. This allows for modeling inherent item quality and potentially even user sensitivity to popularity, significantly enriching the scoring mechanism. ### run_shell_command · 2026-09-29T06:18:41.776Z { "command": "python3 -c \"\nimport numpy as np\nimport pandas as pd\nimport torch\nfrom pathlib import Path\nfrom ranking_evaluation import CandidateSet, score_rankings\nfrom evaluate_public import validation_truth\n\ndata_dir = Path('/task/data')\ntargets = set(pd.read_parquet(data_dir / 'target_users.parquet').uid.astype(int))\nelig = CandidateSet(data_dir / 'validation_eligible_candidates.npz')\ntruth = validation_truth(elig, targets)\ncatalog = pd.read_parquet(data_dir / 'candidate_catalog.parquet')\n\n# Let's inspect the correlation between organic_positive_listens and validation ground truth\npop_scores = np.log1p(catalog.organic_positive_listens.to_numpy(dtype=np.float32))\n\npacked = np.load(data_dir / 'validation_eligible_candidates.npz')['packed_eligible']\ncat_items = np.load(data_dir / 'validation_eligible_candidates.npz')['item_ids']\nuids = np.load(data_dir / 'validation_eligible_candidates.npz')['uids']\n\n# Let's evaluate purely pop_scores:\nranked = {}\nbits = np.unpackbits(packed, axis=1, count=30000, bitorder='little').astype(bool)\nfor row, uid in enumerate(uids):\n s = pop_scores.copy()\n s[~bits[row]] = -1e9\n top = np.argpartition(s, -100)[-100:]\n top = top[np.argsort(-s[top])]\n ranked[int(uid)] = list(map(int, cat_items[top]))\n\nprint('Pure organic popularity score:', score_rankings(ranked, truth))\n\"", "description": "Evaluate pure organic popularity score" } ### Tool output · 2026-09-29T06:18:45.740Z Output: Traceback (most recent call last): File "", line 6, in ModuleNotFoundError: No module named 'ranking_evaluation' Exit Code: 1 Process Group PGID: 2634 ## Step 61 · agent · 2026-09-29T06:19:33.031Z ### run_shell_command · 2026-09-29T06:19:33.031Z { "description": "Evaluate organic popularity score with PYTHONPATH", "command": "PYTHONPATH=/task python3 -c \"\nimport numpy as np\nimport pandas as pd\nimport torch\nfrom pathlib import Path\nfrom ranking_evaluation import CandidateSet, score_rankings\nfrom evaluate_public import validation_truth\n\ndata_dir = Path('/task/data')\ntargets = set(pd.read_parquet(data_dir / 'target_users.parquet').uid.astype(int))\nelig = CandidateSet(data_dir / 'validation_eligible_candidates.npz')\ntruth = validation_truth(elig, targets)\ncatalog = pd.read_parquet(data_dir / 'candidate_catalog.parquet')\n\npop_scores = np.log1p(catalog.organic_positive_listens.to_numpy(dtype=np.float32))\n\npacked = np.load(data_dir / 'validation_eligible_candidates.npz')['packed_eligible']\ncat_items = np.load(data_dir / 'validation_eligible_candidates.npz')['item_ids']\nuids = np.load(data_dir / 'validation_eligible_candidates.npz')['uids']\n\nranked = {}\nbits = np.unpackbits(packed, axis=1, count=30000, bitorder='little').astype(bool)\nfor row, uid in enumerate(uids):\n s = pop_scores.copy()\n s[~bits[row]] = -1e9\n top = np.argpartition(s, -100)[-100:]\n top = top[np.argsort(-s[top])]\n ranked[int(uid)] = list(map(int, cat_items[top]))\n\nprint('Pure organic popularity score:', score_rankings(ranked, truth))\n\"" } ### Tool output · 2026-09-29T06:19:40.516Z Output: Pure organic popularity score: {'quality_ndcg@10': 0.010578, 'quality_users': 4263} Process Group PGID: 2651 ## Step 62 · agent · 2026-09-29T06:20:27.760Z ### Recorded reasoning summary Analyzing Model Performance Discrepancy I'm digging into the performance gap between the neural two-tower model (0.006) and the popularity baseline (0.0105). My initial focus is on how in-batch contrastive learning, specifically without logQ correction or item bias, might be influencing results by disproportionately benefiting high-frequency items. ### run_shell_command · 2026-09-29T06:20:27.760Z { "description": "Check predicted items", "command": "python3 -c \"\n# Let's inspect what items the prototype predicted\n# In prototype:\n\"" } ### Tool output · 2026-09-29T06:20:27.795Z Output: (empty) Process Group PGID: 2677 ## Step 63 · agent · 2026-09-29T06:20:46.372Z ### Recorded reasoning summary Refining Logit Calculations I'm currently exploring adjustments to my logit calculations. Specifically, I'm evaluating the impact of incorporating marginal probabilities, like $(\text{catalog.positive\_listens} / \text{total})^{0.75}$, as a LogQ correction. This involves subtracting the log of these probabilities from in-batch logits. Additionally, I'm considering the integration of learnable item biases into the model. ### Recorded reasoning summary Incorporating Item Biases I'm now focusing on how to integrate learnable item biases and scalar weights. My thought is to augment the user and item vectors by one dimension, where the item's bias term and the user's scalar weight would reside. This approach should allow the dot product to naturally encompass these new parameters, aligning with the requirement of independently precomputable item vectors and efficient retrieval. ### run_shell_command · 2026-09-29T06:20:46.372Z { "command": "python3 -c \"\nimport pandas as pd\ndata_dir = '/task/data'\nlikes = pd.read_parquet(f'{data_dir}/train_likes.parquet')\npref = pd.read_parquet(f'{data_dir}/validation_preference_state.parquet')\npref_liked = pref[pref.state == 'liked']\n\nlikes_pairs = set(zip(likes.uid, likes.item_id))\npref_pairs = set(zip(pref_liked.uid, pref_liked.item_id))\nprint('Total pref liked:', len(pref_pairs))\nprint('Pref liked in train_likes:', len(pref_pairs.intersection(likes_pairs)))\n\"", "description": "Check overlap of pref liked and train_likes" } ### Tool output · 2026-09-29T06:20:49.344Z Output: Total pref liked: 418435 Pref liked in train_likes: 418435 Process Group PGID: 2680 ## Step 64 · agent · 2026-09-29T06:21:09.225Z ### Recorded reasoning summary Processing User Likes All `pref_liked` items have been successfully incorporated into `train_likes`. My current focus is on investigating the discrepancy with `train_hist_1` to understand why it was missing numerous likes. ### Recorded reasoning summary Investigating Embedding Initialization I've identified a crucial detail: `shared_emb` is initialized with a normal distribution, which I hadn't fully accounted for when considering how item embeddings are aggregated. This warrants closer examination of how the sum of these embeddings is behaving. ### Recorded reasoning summary Refining Embedding Aggregation I've realized the average of 64 random item embeddings, each with a std of 0.02, results in an input to the user MLP with a std of only 0.0025. This, combined with the minimal training steps per item and gradient attenuation via average pooling, suggests the embeddings might not be learning effectively. ### Recorded reasoning summary Addressing Gradient Attenuation I've discovered that mean pooling over 64 items attenuates gradients by a factor of 64, drastically reducing the effective learning rate for individual item embeddings. This explains why they barely move from initialization, a known challenge in two-tower models. I am now exploring production solutions like Item2Vec pre-training. ### Recorded reasoning summary Exploring Alternative Embedding Methods I'm re-evaluating Item2Vec and Word2Vec. My understanding is they don't decompose a global user-item matrix, scaling well for production. This offers a promising alternative to address the gradient attenuation issues I've been observing. ### Recorded reasoning summary Exploring Contrastive Loss I'm considering item contrastive loss and pre-training item embeddings with Word2Vec/Item2Vec on listening sessions. Investigating library availability, I see PyTorch is present but gensim is not. I'll explore training Item2Vec within PyTorch or other strategies like higher embedding layer learning rates with sampled softmax loss and attention mechanisms. ### run_shell_command · 2026-09-29T06:21:09.225Z { "description": "Check last listens and likes before cutoff", "command": "PYTHONPATH=/task python3 -c \"\nimport pandas as pd\nimport pyarrow.parquet as pq\nfrom pathlib import Path\nfrom ranking_evaluation import CandidateSet\nfrom evaluate_public import validation_truth\n\ndata_dir = Path('/task/data')\ntargets = set(pd.read_parquet(data_dir / 'target_users.parquet').uid.astype(int))\nelig = CandidateSet(data_dir / 'validation_eligible_candidates.npz')\ntruth = validation_truth(elig, targets)\n\ncutoff = 24786800\n# Get last 5 listens before cutoff for each user\nlistens_table = pq.read_table(\n data_dir / 'train_listens.parquet',\n columns=['uid', 'item_id', 'timestamp'],\n filters=[('timestamp', '>=', cutoff - 7 * 86400), ('timestamp', '<', cutoff)]\n)\nlistens = listens_table.to_pandas()\nlistens = listens[listens.uid.isin(targets)].sort_values('timestamp')\n\nlast_listens = listens.groupby('uid').tail(5)\nprint('Users with listens in last 7d:', last_listens.uid.nunique())\n\n# Check likes before cutoff\nlikes = pd.read_parquet(data_dir / 'train_likes.parquet')\nlikes = likes[likes.uid.isin(targets) & (likes.timestamp < cutoff)].sort_values('timestamp')\nlast_likes = likes.groupby('uid').tail(5)\nprint('Users with likes before cutoff:', last_likes.uid.nunique())\n\"" } ### Tool output · 2026-09-29T06:21:12.201Z Output: Users with listens in last 7d: 7369 Users with likes before cutoff: 7071 Process Group PGID: 2705 ## Step 65 · agent · 2026-09-29T06:24:50.670Z ### Recorded reasoning summary Improving Item Embeddings I'm focusing on how to train the Item and User Towers to generate semantically rich embeddings. My current exploration is into what constitutes effective item embeddings specifically within the context of music recommendation. ### Recorded reasoning summary Refining Co-occurrence Signals I'm now focusing on leveraging co-occurrence data to strengthen item embeddings. Specifically, I'm investigating how to effectively construct positive transition pairs from listening histories to inform the training of the two-tower model, considering the substantial catalog size. ### Recorded reasoning summary Testing Popularity Influence I'm now exploring the immediate impact of incorporating popularity bias into a similarity model to see if it can outperform the baseline. I plan to conduct a quick experiment using Python to assess this. ### run_shell_command · 2026-09-29T06:24:50.670Z { "command": "PYTHONPATH=/task python3 -c \"\nimport pandas as pd\nimport numpy as np\nimport pyarrow.parquet as pq\nfrom pathlib import Path\nfrom ranking_evaluation import CandidateSet, score_rankings\nfrom evaluate_public import validation_truth\n\ndata_dir = Path('/task/data')\ntargets = set(pd.read_parquet(data_dir / 'target_users.parquet').uid.astype(int))\nelig = CandidateSet(data_dir / 'validation_eligible_candidates.npz')\ntruth = validation_truth(elig, targets)\ncatalog = pd.read_parquet(data_dir / 'candidate_catalog.parquet')\n\n# Popularity score\npop = np.log1p(catalog.organic_positive_listens.to_numpy(dtype=np.float32))\npop = (pop - pop.mean()) / pop.std()\n\n# Let's inspect NDCG with pure pop:\npacked = np.load(data_dir / 'validation_eligible_candidates.npz')['packed_eligible']\ncat_items = np.load(data_dir / 'validation_eligible_candidates.npz')['item_ids']\nuids = np.load(data_dir / 'validation_eligible_candidates.npz')['uids']\nbits = np.unpackbits(packed, axis=1, count=30000, bitorder='little').astype(bool)\n\nranked = {}\nfor row, uid in enumerate(uids):\n s = pop.copy()\n s[~bits[row]] = -1e9\n top = np.argpartition(s, -100)[-100:]\n top = top[np.argsort(-s[top])]\n ranked[int(uid)] = list(map(int, cat_items[top]))\n\nprint('Popularity baseline NDCG@10:', score_rankings(ranked, truth))\n\"", "description": "Check normalized popularity baseline" } ### Tool output · 2026-09-29T06:24:54.109Z Output: Popularity baseline NDCG@10: {'quality_ndcg@10': 0.010578, 'quality_users': 4263} Process Group PGID: 2745 ## Step 66 · agent · 2026-09-29T06:25:25.170Z ### Recorded reasoning summary Testing Personalized Signals I'm exploring how incorporating personalized item co-occurrence and track similarity into the popularity baseline might elevate NDCG scores. The current focus is on designing and executing tests that quantify the impact of these combined signals. ### run_shell_command · 2026-09-29T06:25:25.170Z { "command": "PYTHONPATH=/task python3 -c \"\nimport pandas as pd\nimport numpy as np\nimport pyarrow.parquet as pq\nfrom scipy import sparse\nfrom pathlib import Path\nfrom ranking_evaluation import CandidateSet, score_rankings\nfrom evaluate_public import validation_truth\n\ndata_dir = Path('/task/data')\ntargets = set(pd.read_parquet(data_dir / 'target_users.parquet').uid.astype(int))\nelig = CandidateSet(data_dir / 'validation_eligible_candidates.npz')\ntruth = validation_truth(elig, targets)\ncatalog = pd.read_parquet(data_dir / 'candidate_catalog.parquet')\nitem_id_to_idx = {id_: idx for idx, id_ in enumerate(catalog.item_id)}\n\ncutoff = 24786800\nt_start = cutoff - 14 * 86400\nlistens = pq.read_table(\n data_dir / 'train_listens.parquet',\n columns=['uid', 'item_id'],\n filters=[('timestamp', '>=', t_start), ('timestamp', '<', cutoff), ('is_organic', '==', 1), ('played_ratio_pct', '>=', 80)]\n).to_pandas()\nlistens = listens[listens.item_id.isin(item_id_to_idx) & listens.uid.isin(targets)]\nlistens['item_idx'] = listens.item_id.map(item_id_to_idx)\n\n# Unique (uid, item_idx)\npairs = listens[['uid', 'item_idx']].drop_duplicates()\nprint('Unique pairs in 14d:', len(pairs))\n\"", "description": "Inspect co-occurrence pairs" } ### Tool output · 2026-09-29T06:25:28.342Z Output: Unique pairs in 14d: 293109 Process Group PGID: 2771 ## Step 67 · agent · 2026-09-29T06:25:35.498Z ### run_shell_command · 2026-09-29T06:25:35.498Z { "command": "PYTHONPATH=/task python3 -c \"\nimport pandas as pd\nimport numpy as np\nimport pyarrow.parquet as pq\nfrom scipy import sparse\nfrom pathlib import Path\nfrom ranking_evaluation import CandidateSet, score_rankings\nfrom evaluate_public import validation_truth\n\ndata_dir = Path('/task/data')\ntargets = set(pd.read_parquet(data_dir / 'target_users.parquet').uid.astype(int))\nelig = CandidateSet(data_dir / 'validation_eligible_candidates.npz')\ntruth = validation_truth(elig, targets)\ncatalog = pd.read_parquet(data_dir / 'candidate_catalog.parquet')\nitem_id_to_idx = {id_: idx for idx, id_ in enumerate(catalog.item_id)}\n\ncutoff = 24786800\nt_start = cutoff - 14 * 86400\nlistens = pq.read_table(\n data_dir / 'train_listens.parquet',\n columns=['uid', 'item_id'],\n filters=[('timestamp', '>=', t_start), ('timestamp', '<', cutoff), ('is_organic', '==', 1), ('played_ratio_pct', '>=', 80)]\n).to_pandas()\nlistens = listens[listens.item_id.isin(item_id_to_idx) & listens.uid.isin(targets)]\nlistens['item_idx'] = listens.item_id.map(item_id_to_idx)\n\n# Map users to rows\nuids = np.load(data_dir / 'validation_eligible_candidates.npz')['uids']\nuid_to_row = {uid: r for r, uid in enumerate(uids)}\nlistens['row'] = listens.uid.map(uid_to_row)\nlistens = listens.dropna(subset=['row'])\n\nrow_indices = listens['row'].to_numpy(dtype=int)\ncol_indices = listens['item_idx'].to_numpy(dtype=int)\ndata = np.ones(len(listens), dtype=np.float32)\n\nX = sparse.csr_matrix((data, (row_indices, col_indices)), shape=(len(uids), 30000))\n# Item co-occurrence: C = X.T @ X\nC = X.T @ X\nC.setdiag(0)\n# Item-item cosine similarity\nnorms = np.sqrt(C.diagonal() + 1e-6)\n# User scores: S = X @ C\nuser_sim = X @ C # shape (8456, 30000)\n\npacked = np.load(data_dir / 'validation_eligible_candidates.npz')['packed_eligible']\ncat_items = np.load(data_dir / 'validation_eligible_candidates.npz')['item_ids']\nbits = np.unpackbits(packed, axis=1, count=30000, bitorder='little').astype(bool)\n\npop = np.log1p(catalog.organic_positive_listens.to_numpy(dtype=np.float32))\n\nfor alpha in [0.0, 0.01, 0.05, 0.1, 0.5, 1.0]:\n ranked = {}\n for r, uid in enumerate(uids):\n s_row = user_sim[r].toarray().ravel()\n # combine with popularity\n if s_row.max() > 0:\n s_row = s_row / s_row.max()\n score_val = s_row + alpha * (pop / pop.max())\n score_val[~bits[r]] = -1e9\n top = np.argpartition(score_val, -100)[-100:]\n top = top[np.argsort(-score_val[top])]\n ranked[int(uid)] = list(map(int, cat_items[top]))\n print(f'alpha={alpha}: {score_rankings(ranked, truth)}')\n\"", "description": "Test item-item co-occurrence combined with popularity" } ### Tool output · 2026-09-29T06:25:55.801Z Output: alpha=0.0: {'quality_ndcg@10': 0.022866, 'quality_users': 4263} alpha=0.01: {'quality_ndcg@10': 0.024769, 'quality_users': 4263} alpha=0.05: {'quality_ndcg@10': 0.024902, 'quality_users': 4263} alpha=0.1: {'quality_ndcg@10': 0.024751, 'quality_users': 4263} alpha=0.5: {'quality_ndcg@10': 0.024086, 'quality_users': 4263} alpha=1.0: {'quality_ndcg@10': 0.022589, 'quality_users': 4263} Process Group PGID: 2800 ## Step 68 · agent · 2026-09-29T06:26:17.483Z ### Recorded reasoning summary Achieving Significant Improvement I'm thrilled to report a substantial leap in performance! My quality NDCG@10 metric has hit 0.024902 with `alpha=0.05`, which is an astounding 2.3x improvement over the popularity baseline of 0.01071, even with only 14 days of listener data. This validates the direction of the two-tower neural network approach for the 'Made for You' recommendation task. ### Recorded reasoning summary Exploring Co-occurrence Significance I'm currently digging into the efficacy of co-occurrence $XC$ within the two-tower architecture. My recent focus has been on understanding precisely why this element proved so effective, particularly given the constraints of limited listener data and the need for scalable production. ### Recorded reasoning summary Unveiling the Mechanism I've uncovered a fascinating equivalence: the co-occurrence term $C_{i,j}$ effectively becomes the sum of similarities between a user's historical items and a candidate item when the user vector is the sum of its history item embeddings. This is precisely how two-tower models operate! ### Recorded reasoning summary Leveraging Sequential Transitions I'm now focusing on training the item embeddings and two towers directly in PyTorch. The key insight is to leverage positive item-item transitions from track sequences and user-item interactions. By streaming sequence pairs like $(i, j)$ for tracks listened to within a session, I can implement an in-batch contrastive or negative sampling loss. This approach avoids constructing a global user-item matrix, akin to Word2Vec or BERT4Rec, and allows for efficient mini-batch stochastic gradient descent. ### Recorded reasoning summary Refining User Representation I'm now examining how user history aggregation impacts performance. Previously, averaging all 64 history items diluted the signal from more recent or frequent listens. I'm exploring methods to better represent users, especially considering limited training data and the need to capture nuanced preferences for improved recommendation quality. ### Recorded reasoning summary Incorporating Popularity Bias I'm now investigating the direct integration of item popularity bias into the two-tower embeddings. My latest thoughts explore how to append a scalar to the normalized semantic embeddings, allowing popularity to directly influence the model's predictions. This is a direct follow-up to the observation that popularity boost was not possible with L2 normalization. ### Recorded reasoning summary Injecting Popularity I've discovered a way to integrate item popularity directly into the user tower's output. By reformulating the dot product, I can represent the combined personalized similarity and popularity as an inner product of $(d+1)$-dimensional vectors, allowing popularity to be learned as a parameter. ### run_shell_command · 2026-09-29T06:26:17.483Z { "command": "PYTHONPATH=/task python3 -c \"\nimport pandas as pd\nimport numpy as np\nimport pyarrow.parquet as pq\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nimport time\nfrom pathlib import Path\nfrom ranking_evaluation import CandidateSet, score_rankings\nfrom evaluate_public import validation_truth\n\ndata_dir = Path('/task/data')\ntargets = set(pd.read_parquet(data_dir / 'target_users.parquet').uid.astype(int))\nelig = CandidateSet(data_dir / 'validation_eligible_candidates.npz')\ntruth = validation_truth(elig, targets)\ncatalog = pd.read_parquet(data_dir / 'candidate_catalog.parquet')\nitem_id_to_idx = {id_: idx for idx, id_ in enumerate(catalog.item_id, 1)}\nN_items = len(item_id_to_idx) + 1 # 30001\n\ncutoff = 24786800\nt_start = cutoff - 30 * 86400\nlistens = pq.read_table(\n data_dir / 'train_listens.parquet',\n columns=['uid', 'item_id', 'timestamp'],\n filters=[('timestamp', '>=', t_start), ('timestamp', '<', cutoff), ('is_organic', '==', 1), ('played_ratio_pct', '>=', 80)]\n).to_pandas()\nlistens = listens[listens.item_id.isin(item_id_to_idx) & listens.uid.isin(targets)].sort_values(['uid', 'timestamp'])\nlistens['item_idx'] = listens.item_id.map(item_id_to_idx).astype(int)\n\n# Extract transitions (i_t, i_{t+1}) for each user\nprint('Total strong listens in 30d:', len(listens))\nlistens['next_item_idx'] = listens.groupby('uid')['item_idx'].shift(-1)\ntransitions = listens.dropna(subset=['next_item_idx'])\ntransitions['next_item_idx'] = transitions['next_item_idx'].astype(int)\nprint('Total transitions:', len(transitions))\n\"", "description": "Extract sequential transitions in 30d listens" } ### Tool output · 2026-09-29T06:26:22.932Z Output: :35: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy Total strong listens in 30d: 1250037 Total transitions: 1242736 Process Group PGID: 2829 ## Step 69 · agent · 2026-09-29T06:26:37.817Z ### Recorded reasoning summary Analyzing Sequential Transitions I'm focusing on the sheer volume of sequential transitions within a month's strong listening data, noting over a million possibilities. My current investigation involves exploring the impact of short-term listening windows, specifically $W=5$ tracks within the same session. The goal is to ascertain if training item embeddings on these transitions can reveal authentic musical similarities, and I'm now setting up PyTorch for this experimentation. ### Recorded reasoning summary Exploring Two-Tower Architectures I'm considering how item and user towers can be integrated. My current thinking is to train the user tower to predict the next track based on a sequence of recent listens, mirroring sequential recommendation models. I'm also exploring augmenting the user tower with liked tracks to capture both long-term taste and current context. ### Recorded reasoning summary Generating Sequential Pairs I'm now focused on the feasibility of generating training pairs from the sequential interaction data. I'm testing Python scripts to efficiently extract history and target item sequences, specifically aiming to create over a million (history, target) pairs from the 30 days of listening logs. ### run_shell_command · 2026-09-29T06:26:37.817Z { "description": "Benchmark sequential training pairs creation", "command": "PYTHONPATH=/task python3 -c \"\nimport pandas as pd\nimport numpy as np\nimport pyarrow.parquet as pq\nimport time\nfrom pathlib import Path\n\nstart = time.time()\ndata_dir = Path('/task/data')\ntargets = set(pd.read_parquet(data_dir / 'target_users.parquet').uid.astype(int))\ncatalog = pd.read_parquet(data_dir / 'candidate_catalog.parquet')\nitem_id_to_idx = {id_: idx for idx, id_ in enumerate(catalog.item_id, 1)}\n\ncutoff = 24786800\nt_start = cutoff - 30 * 86400\nlistens = pq.read_table(\n data_dir / 'train_listens.parquet',\n columns=['uid', 'item_id', 'timestamp'],\n filters=[('timestamp', '>=', t_start), ('timestamp', '<', cutoff), ('is_organic', '==', 1), ('played_ratio_pct', '>=', 80)]\n).to_pandas()\nlistens = listens[listens.item_id.isin(item_id_to_idx) & listens.uid.isin(targets)].sort_values(['uid', 'timestamp'])\nlistens['item_idx'] = listens.item_id.map(item_id_to_idx).astype(int)\n\n# Group user sequences:\nuser_sequences = listens.groupby('uid')['item_idx'].apply(list).to_dict()\nprint(f'Extracted sequences for {len(user_sequences)} users in {time.time() - start:.2f}s')\n\n# Create (history_items, target_item) training examples\n# For each user, sample up to 50 target items along their sequence\nhist_list = []\ntarget_list = []\nuid_list = []\nmax_hist_len = 20\n\nfor uid, seq in user_sequences.items():\n if len(seq) < 2:\n continue\n # Take every 2nd or 3rd step to avoid huge redundancy, or take up to 40 steps\n step = max(1, len(seq) // 40)\n for t in range(1, len(seq), step):\n h = seq[max(0, t - max_hist_len):t]\n hist_list.append(h)\n target_list.append(seq[t])\n uid_list.append(uid)\n\nprint(f'Created {len(target_list)} training examples in {time.time() - start:.2f}s')\n\"" } ### Tool output · 2026-09-29T06:26:40.926Z Output: Extracted sequences for 7301 users in 1.09s Created 273840 training examples in 1.43s Process Group PGID: 2857 ## Step 70 · agent · 2026-09-29T06:27:27.713Z ### Recorded reasoning summary Generating Training Data I've successfully generated over 273,000 training examples in under two seconds. I'm exploring incorporating "likes" as target items with a weight of 7.0 and testing different lookback windows of 60 and 90 days to create up to 500,000 examples. ### Recorded reasoning summary Exploring User Aggregation I'm now focusing on how to best aggregate user sequences within the User Tower. My current thoughts involve exploring positional/recency weighting, incorporating explicit likes and dislikes, and considering the very last item and user statistics to inform the final user embedding. ### Recorded reasoning summary Developing Item Tower I am detailing the Item Tower's input and architecture, including item embeddings, catalog features, and an MLP. I am also investigating how to incorporate item popularity bias through a learned or tuned parameter, $\beta$, which balances personalization with popularity, resulting in $d+1$ dimensional vectors for precise dot product calculations. ### write_file · 2026-09-29T06:27:27.713Z { "content": "#!/usr/bin/env python3\n\"\"\"Sequential Two-Tower Neural Network Ranker for discovery shelf.\"\"\"\nfrom __future__ import annotations\n\nimport math\nimport time\nfrom pathlib import Path\n\nimport numpy as np\nimport pandas as pd\nimport pyarrow.parquet as pq\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\nfrom ranking_evaluation import CandidateSet, validate_ranking, TOP_N\nfrom evaluate_public import validation_truth, score\n\nDATA_DIR = Path('/task/data')\nSOLUTION_DIR = Path('/app/solution')\n\n\ndef get_catalog_features():\n cat = pd.read_parquet(DATA_DIR / 'candidate_catalog.parquet')\n item_ids = cat.item_id.to_numpy()\n item_id_to_idx = {int(id_): idx for idx, id_ in enumerate(item_ids, 1)}\n \n pos = np.log1p(cat.positive_listens.to_numpy(dtype=np.float32))\n org = np.log1p(cat.organic_positive_listens.to_numpy(dtype=np.float32))\n pop = np.log1p(cat.popularity_rank.to_numpy(dtype=np.float32))\n ratio = (cat.organic_positive_listens / (cat.positive_listens + 1.0)).to_numpy(dtype=np.float32)\n \n # Normalized popularity prior in [0, 1]\n norm_pop = (org - org.min()) / (org.max() - org.min() + 1e-6)\n \n feats = np.stack([\n (pos - pos.mean()) / (pos.std() + 1e-6),\n (org - org.mean()) / (org.std() + 1e-6),\n (pop - pop.mean()) / (pop.std() + 1e-6),\n (ratio - ratio.mean()) / (ratio.std() + 1e-6),\n ], axis=1)\n \n feat_tensor = np.zeros((len(item_ids) + 1, 4), dtype=np.float32)\n feat_tensor[1:] = feats\n \n pop_tensor = np.zeros(len(item_ids) + 1, dtype=np.float32)\n pop_tensor[1:] = norm_pop\n \n return item_ids, item_id_to_idx, torch.from_numpy(feat_tensor), torch.from_numpy(pop_tensor)\n\n\ndef load_user_likes_and_dislikes(cutoff_ts: int, item_id_to_idx: dict[int, int], uid_to_idx: dict[int, int], N_users: int, is_val: bool = False):\n \"\"\"Load user likes and dislikes for long-term preference.\"\"\"\n K_like = 64\n K_dis = 32\n likes_arr = np.zeros((N_users, K_like), dtype=np.int64)\n dislikes_arr = np.zeros((N_users, K_dis), dtype=np.int64)\n stats_arr = np.zeros((N_users, 2), dtype=np.float32)\n \n train_likes = pd.read_parquet(DATA_DIR / 'train_likes.parquet')\n likes_df = train_likes[(train_likes.timestamp < cutoff_ts) & (train_likes.uid.isin(uid_to_idx))].copy()\n if is_val:\n pref = pd.read_parquet(DATA_DIR / 'validation_preference_state.parquet')\n liked_pref = pref[(pref.state == 'liked') & (pref.uid.isin(uid_to_idx))].copy()\n liked_pref['timestamp'] = cutoff_ts - 1\n likes_df = pd.concat([likes_df[['uid', 'item_id', 'timestamp']], liked_pref[['uid', 'item_id', 'timestamp']]])\n \n likes_df['uid_idx'] = likes_df.uid.map(uid_to_idx)\n likes_df['item_idx'] = likes_df.item_id.map(item_id_to_idx)\n likes_df = likes_df.dropna(subset=['uid_idx', 'item_idx']).sort_values('timestamp')\n likes_df['uid_idx'] = likes_df.uid_idx.astype(np.int64)\n likes_df['item_idx'] = likes_df.item_idx.astype(np.int64)\n \n for u, g in likes_df.groupby('uid_idx'):\n itms = g.item_idx.to_numpy()\n k_take = min(len(itms), K_like)\n likes_arr[u, :k_take] = itms[-k_take:]\n stats_arr[u, 0] = np.log1p(len(itms))\n\n train_dis = pd.read_parquet(DATA_DIR / 'train_dislikes.parquet')\n dis_df = train_dis[(train_dis.timestamp < cutoff_ts) & (train_dis.uid.isin(uid_to_idx))].copy()\n if is_val:\n pref = pd.read_parquet(DATA_DIR / 'validation_preference_state.parquet')\n dis_pref = pref[(pref.state == 'disliked') & (pref.uid.isin(uid_to_idx))].copy()\n dis_pref['timestamp'] = cutoff_ts - 1\n dis_df = pd.concat([dis_df[['uid', 'item_id', 'timestamp']], dis_pref[['uid', 'item_id', 'timestamp']]])\n dis_df['uid_idx'] = dis_df.uid.map(uid_to_idx)\n dis_df['item_idx'] = dis_df.item_id.map(item_id_to_idx)\n dis_df = dis_df.dropna(subset=['uid_idx', 'item_idx']).sort_values('timestamp')\n dis_df['uid_idx'] = dis_df.uid_idx.astype(np.int64)\n dis_df['item_idx'] = dis_df.item_idx.astype(np.int64)\n \n for u, g in dis_df.groupby('uid_idx'):\n itms = g.item_idx.to_numpy()\n k_take = min(len(itms), K_dis)\n dislikes_arr[u, :k_take] = itms[-k_take:]\n stats_arr[u, 1] = np.log1p(len(itms))\n \n return torch.from_numpy(likes_arr), torch.from_numpy(dislikes_arr), torch.from_numpy(stats_arr)\n\n\nclass UserTower(nn.Module):\n def __init__(self, item_embeddings: nn.Embedding, d_in: int, d_hidden: int, d_out: int):\n super().__init__()\n self.item_emb = item_embeddings\n self.stats_proj = nn.Linear(2, 16)\n # Channels: last_item (d_in), seq_pool (d_in), likes_pool (d_in), dislikes_pool (d_in), stats (16)\n in_dim = 4 * d_in + 16\n self.mlp = nn.Sequential(\n nn.Linear(in_dim, d_hidden),\n nn.LayerNorm(d_hidden),\n nn.GELU(),\n nn.Dropout(0.1),\n nn.Linear(d_hidden, d_out),\n )\n # Learnable popularity balance weight\n self.pop_weight = nn.Parameter(torch.tensor(0.15, dtype=torch.float32))\n\n def forward(self, last_items, seq_items, seq_weights, likes, dislikes, stats):\n # 1. Last item embedding\n mask_last = (last_items > 0).float().unsqueeze(-1)\n h_last = self.item_emb(last_items) * mask_last\n\n # 2. Sequence pool\n mask_seq = ((seq_items > 0).float() * seq_weights).unsqueeze(-1)\n emb_seq = self.item_emb(seq_items) * mask_seq\n h_seq = emb_seq.sum(dim=1) / (mask_seq.sum(dim=1) + 1e-6)\n\n # 3. Likes pool\n mask_l = (likes > 0).float().unsqueeze(-1)\n emb_l = self.item_emb(likes) * mask_l\n h_l = emb_l.sum(dim=1) / (mask_l.sum(dim=1) + 1e-6)\n\n # 4. Dislikes pool\n mask_d = (dislikes > 0).float().unsqueeze(-1)\n emb_d = self.item_emb(dislikes) * mask_d\n h_d = emb_d.sum(dim=1) / (mask_d.sum(dim=1) + 1e-6)\n\n # 5. Stats\n h_stats = F.gelu(self.stats_proj(stats))\n\n feat = torch.cat([h_last, h_seq, h_l, h_d, h_stats], dim=-1)\n u_base = self.mlp(feat)\n u_norm = F.normalize(u_base, p=2, dim=-1)\n\n # Concatenate popularity coordinate:\n # beta = torch.sigmoid(self.pop_weight)\n beta = self.pop_weight.clamp(0.01, 0.8)\n scale_sem = torch.sqrt(1.0 - beta**2)\n u_final = torch.cat([u_norm * scale_sem, beta.expand(len(u_norm), 1)], dim=-1)\n return u_final\n\n\nclass ItemTower(nn.Module):\n def __init__(self, item_embeddings: nn.Embedding, catalog_feats: torch.Tensor, pop_scores: torch.Tensor, d_in: int, d_hidden: int, d_out: int):\n super().__init__()\n self.item_emb = item_embeddings\n self.register_buffer('catalog_feats', catalog_feats)\n self.register_buffer('pop_scores', pop_scores)\n self.feat_proj = nn.Linear(4, 16)\n self.mlp = nn.Sequential(\n nn.Linear(d_in + 16, d_hidden),\n nn.LayerNorm(d_hidden),\n nn.GELU(),\n nn.Linear(d_hidden, d_out),\n )\n\n def forward(self, item_ids, pop_weight_param: torch.Tensor):\n emb = self.item_emb(item_ids)\n feats = self.catalog_feats[item_ids]\n h_feats = F.gelu(self.feat_proj(feats))\n x = torch.cat([emb, h_feats], dim=-1)\n v_base = self.mlp(x)\n v_norm = F.normalize(v_base, p=2, dim=-1)\n\n # Append popularity coordinate\n beta = pop_weight_param.clamp(0.01, 0.8)\n scale_sem = torch.sqrt(1.0 - beta**2)\n pop_val = self.pop_scores[item_ids].unsqueeze(-1)\n v_final = torch.cat([v_norm * scale_sem, beta * pop_val], dim=-1)\n return v_final\n\n\ndef run_experiment():\n print(\"Loading data...\")\n item_ids, item_id_to_idx, catalog_feats, pop_scores = get_catalog_features()\n targets_df = pd.read_parquet(DATA_DIR / 'target_users.parquet')\n uids = targets_df.uid.to_numpy()\n uid_to_idx = {int(uid): idx for idx, uid in enumerate(uids)}\n N_users = len(uids)\n num_items = len(item_ids) + 1 # 30001\n \n cutoff_val = 24786800\n \n # 1. Validation user history at cutoff\n print(\"Loading validation user history...\")\n val_likes, val_dislikes, val_stats = load_user_likes_and_dislikes(cutoff_val, item_id_to_idx, uid_to_idx, N_users, is_val=True)\n \n # Load recent listens for validation users (last 30 days)\n t_30d = cutoff_val - 30 * 86400\n val_listens = pq.read_table(\n DATA_DIR / 'train_listens.parquet',\n columns=['uid', 'item_id', 'timestamp', 'played_ratio_pct'],\n filters=[('timestamp', '>=', t_30d), ('timestamp', '<', cutoff_val)]\n ).to_pandas()\n val_listens = val_listens[val_listens.uid.isin(uid_to_idx) & val_listens.item_id.isin(item_id_to_idx)].sort_values(['uid', 'timestamp'])\n val_listens['uid_idx'] = val_listens.uid.map(uid_to_idx).astype(int)\n val_listens['item_idx'] = val_listens.item_id.map(item_id_to_idx).astype(int)\n \n K_seq = 32\n val_last_items = np.zeros(N_users, dtype=np.int64)\n val_seq_items = np.zeros((N_users, K_seq), dtype=np.int64)\n val_seq_weights = np.zeros((N_users, K_seq), dtype=np.float32)\n \n for u, g in val_listens.groupby('uid_idx'):\n itms = g.item_idx.to_numpy()\n ts = g.timestamp.to_numpy(dtype=np.float64)\n diff = np.maximum(0.0, float(cutoff_val) - ts)\n w = np.exp(-diff / (7.0 * 86400.0)).astype(np.float32)\n val_last_items[u] = itms[-1]\n k_take = min(len(itms), K_seq)\n val_seq_items[u, :k_take] = itms[-k_take:]\n val_seq_weights[u, :k_take] = w[-k_take:]\n\n val_inputs = {\n 'last_items': torch.from_numpy(val_last_items),\n 'seq_items': torch.from_numpy(val_seq_items),\n 'seq_weights': torch.from_numpy(val_seq_weights),\n 'likes': val_likes,\n 'dislikes': val_dislikes,\n 'stats': val_stats,\n }\n\n # 2. Extract sequential training examples from 60 days of train listens\n print(\"Extracting sequential training data...\")\n t_60d = cutoff_val - 60 * 86400\n train_listens = pq.read_table(\n DATA_DIR / 'train_listens.parquet',\n columns=['uid', 'item_id', 'timestamp'],\n filters=[('timestamp', '>=', t_60d), ('timestamp', '<', cutoff_val), ('played_ratio_pct', '>=', 50)]\n ).to_pandas()\n train_listens = train_listens[train_listens.uid.isin(uid_to_idx) & train_listens.item_id.isin(item_id_to_idx)].sort_values(['uid', 'timestamp'])\n train_listens['uid_idx'] = train_listens.uid.map(uid_to_idx).astype(int)\n train_listens['item_idx'] = train_listens.item_id.map(item_id_to_idx).astype(int)\n \n user_seqs = train_listens.groupby('uid_idx')['item_idx'].apply(list).to_dict()\n \n # Also load likes for each user\n train_likes = pd.read_parquet(DATA_DIR / 'train_likes.parquet')\n train_likes = train_likes[train_likes.uid.isin(uid_to_idx) & train_likes.item_id.isin(item_id_to_idx) & (train_likes.timestamp < cutoff_val)]\n train_likes['uid_idx'] = train_likes.uid.map(uid_to_idx).astype(int)\n train_likes['item_idx'] = train_likes.item_id.map(item_id_to_idx).astype(int)\n user_liked_dict = train_likes.groupby('uid_idx')['item_idx'].apply(list).to_dict()\n\n train_last = []\n train_seq = []\n train_w = []\n train_u = []\n train_target = []\n train_weights = []\n \n rec_weights = np.exp(-np.arange(K_seq)[::-1] * 0.1).astype(np.float32)\n\n for u, seq in user_seqs.items():\n if len(seq) < 2:\n continue\n step = max(1, len(seq) // 30)\n for t in range(1, len(seq), step):\n target_itm = seq[t]\n h = seq[max(0, t - K_seq):t]\n last_itm = h[-1]\n pad_seq = np.zeros(K_seq, dtype=np.int64)\n pad_w = np.zeros(K_seq, dtype=np.float32)\n pad_seq[:len(h)] = h\n pad_w[:len(h)] = rec_weights[-len(h):]\n \n train_last.append(last_itm)\n train_seq.append(pad_seq)\n train_w.append(pad_w)\n train_u.append(u)\n train_target.append(target_itm)\n train_weights.append(1.0)\n \n # Add liked items as targets\n if u in user_liked_dict:\n liked_items = user_liked_dict[u]\n for itm in liked_items[-10:]:\n # Use current end of sequence as context\n h = seq[-K_seq:]\n last_itm = h[-1]\n pad_seq = np.zeros(K_seq, dtype=np.int64)\n pad_w = np.zeros(K_seq, dtype=np.float32)\n pad_seq[:len(h)] = h\n pad_w[:len(h)] = rec_weights[-len(h):]\n \n train_last.append(last_itm)\n train_seq.append(pad_seq)\n train_w.append(pad_w)\n train_u.append(u)\n train_target.append(itm)\n train_weights.append(7.0)\n\n train_last = np.array(train_last, dtype=np.int64)\n train_seq = np.stack(train_seq, axis=0)\n train_w = np.stack(train_w, axis=0)\n train_u = np.array(train_u, dtype=np.int64)\n train_target = np.array(train_target, dtype=np.int64)\n train_weights = np.array(train_weights, dtype=np.float32)\n \n N_train = len(train_target)\n print(f\"Total training pairs: {N_train}\")\n\n # Model architecture\n d_emb = 64\n d_hidden = 128\n d_out = 63 # + 1 for pop = 64\n \n shared_emb = nn.Embedding(num_items, d_emb, padding_idx=0)\n nn.init.normal_(shared_emb.weight, std=0.05)\n with torch.no_grad():\n shared_emb.weight[0].fill_(0.0)\n\n user_tower = UserTower(shared_emb, d_emb, d_hidden, d_out)\n item_tower = ItemTower(shared_emb, catalog_feats, pop_scores, d_emb, d_hidden, d_out)\n \n logit_scale = nn.Parameter(torch.tensor(math.log(1.0 / 0.05), dtype=torch.float32))\n \n params = list(user_tower.parameters()) + list(item_tower.feat_proj.parameters()) + list(item_tower.mlp.parameters()) + [logit_scale]\n optimizer = torch.optim.AdamW(params, lr=1e-3, weight_decay=1e-4)\n\n # Validation ground truth\n val_elig = CandidateSet(DATA_DIR / 'validation_eligible_candidates.npz')\n graded_val = validation_truth(val_elig, set(uids))\n val_packed = np.load(DATA_DIR / 'validation_eligible_candidates.npz')['packed_eligible']\n val_catalog_items = np.load(DATA_DIR / 'validation_eligible_candidates.npz')['item_ids']\n bits = np.unpackbits(val_packed, axis=1, count=30000, bitorder='little').astype(bool)\n\n def evaluate_ranker():\n user_tower.eval()\n item_tower.eval()\n with torch.no_grad():\n all_cat_ids = torch.arange(1, num_items, dtype=torch.int64)\n V = item_tower(all_cat_ids, user_tower.pop_weight) # (30000, d_out + 1)\n \n user_vecs_list = []\n u_bs = 1024\n for s in range(0, N_users, u_bs):\n e = min(s + u_bs, N_users)\n u_batch = user_tower(\n val_inputs['last_items'][s:e],\n val_inputs['seq_items'][s:e],\n val_inputs['seq_weights'][s:e],\n val_inputs['likes'][s:e],\n val_inputs['dislikes'][s:e],\n val_inputs['stats'][s:e],\n )\n user_vecs_list.append(u_batch)\n U = torch.cat(user_vecs_list, dim=0) # (8456, d_out + 1)\n \n scores = torch.matmul(U, V.t()).numpy()\n scores[~bits] = -1e9\n \n ranked = {}\n for row, uid in enumerate(uids):\n top_cols = np.argpartition(scores[row], -100)[-100:]\n top_cols = top_cols[np.argsort(-scores[row][top_cols])]\n ranked[int(uid)] = list(map(int, val_catalog_items[top_cols]))\n \n return score(ranked, graded_val)\n\n print(\"\\nInitial zero-shot evaluation...\")\n print(evaluate_ranker())\n\n batch_size = 512\n epochs = 5\n \n for epoch in range(1, epochs + 1):\n t0 = time.time()\n user_tower.train()\n item_tower.train()\n \n perm = np.random.permutation(N_train)\n total_loss = 0.0\n n_b = 0\n \n for b_start in range(0, N_train, batch_size):\n b_end = min(b_start + batch_size, N_train)\n if b_end - b_start < 64:\n continue\n idx = perm[b_start:b_end]\n \n u_sub = train_u[idx]\n last_sub = torch.from_numpy(train_last[idx])\n seq_sub = torch.from_numpy(train_seq[idx])\n w_sub = torch.from_numpy(train_w[idx])\n likes_sub = val_likes[u_sub]\n dislikes_sub = val_dislikes[u_sub]\n stats_sub = val_stats[u_sub]\n \n b_target = torch.from_numpy(train_target[idx])\n b_sample_w = torch.from_numpy(train_weights[idx])\n \n u_vecs = user_tower(last_sub, seq_sub, w_sub, likes_sub, dislikes_sub, stats_sub)\n v_vecs = item_tower(b_target, user_tower.pop_weight)\n \n scale = logit_scale.exp().clamp(max=100.0)\n logits = torch.matmul(u_vecs, v_vecs.t()) * scale\n \n targets = torch.arange(len(idx), dtype=torch.int64)\n ce = F.cross_entropy(logits, targets, reduction='none')\n loss = (ce * b_sample_w).mean()\n \n optimizer.zero_grad()\n loss.backward()\n torch.nn.utils.clip_grad_norm_(params, max_norm=1.0)\n optimizer.step()\n \n total_loss += loss.item()\n n_b += 1\n \n dur = time.time() - t0\n print(f\"Epoch {epoch:02d} | Time: {dur:.1f}s | Loss: {total_loss/n_b:.4f} | PopWeight: {user_tower.pop_weight.item():.4f}\")\n eval_res = evaluate_ranker()\n print(f\"Validation Score: {eval_res}\")\n\n\nif __name__ == '__main__':\n run_experiment()\n", "file_path": "/app/solution/train_seq_two_tower.py" } ### Tool output · 2026-09-29T06:27:27.727Z Successfully created and wrote to new file: /app/solution/train_seq_two_tower.py. Here is the updated code: #!/usr/bin/env python3 """Sequential Two-Tower Neural Network Ranker for discovery shelf.""" from __future__ import annotations import math import time from pathlib import Path import numpy as np import pandas as pd import pyarrow.parquet as pq import torch import torch.nn as nn import torch.nn.functional as F from ranking_evaluation import CandidateSet, validate_ranking, TOP_N from evaluate_public import validation_truth, score DATA_DIR = Path('/task/data') SOLUTION_DIR = Path('/app/solution') def get_catalog_features(): cat = pd.read_parquet(DATA_DIR / 'candidate_catalog.parquet') item_ids = cat.item_id.to_numpy() item_id_to_idx = {int(id_): idx for idx, id_ in enumerate(item_ids, 1)} pos = np.log1p(cat.positive_listens.to_numpy(dtype=np.float32)) org = np.log1p(cat.organic_positive_listens.to_numpy(dtype=np.float32)) pop = np.log1p(cat.popularity_rank.to_numpy(dtype=np.float32)) ratio = (cat.organic_positive_listens / (cat.positive_listens + 1.0)).to_numpy(dtype=np.float32) # Normalized popularity prior in [0, 1] norm_pop = (org - org.min()) / (org.max() - org.min() + 1e-6) feats = np.stack([ (pos - pos.mean()) / (pos.std() + 1e-6), (org - org.mean()) / (org.std() + 1e-6), (pop - pop.mean()) / (pop.std() + 1e-6), (ratio - ratio.mean()) / (ratio.std() + 1e-6), ], axis=1) feat_tensor = np.zeros((len(item_ids) + 1, 4), dtype=np.float32) feat_tensor[1:] = feats pop_tensor = np.zeros(len(item_ids) + 1, dtype=np.float32) pop_tensor[1:] = norm_pop return item_ids, item_id_to_idx, torch.from_numpy(feat_tensor), torch.from_numpy(pop_tensor) def load_user_likes_and_dislikes(cutoff_ts: int, item_id_to_idx: dict[int, int], uid_to_idx: dict[int, int], N_users: int, is_val: bool = False): """Load user likes and dislikes for long-term preference.""" K_like = 64 K_dis = 32 likes_arr = np.zeros((N_users, K_like), dtype=np.int64) dislikes_arr = np.zeros((N_users, K_dis), dtype=np.int64) stats_arr = np.zeros((N_users, 2), dtype=np.float32) train_likes = pd.read_parquet(DATA_DIR / 'train_likes.parquet') likes_df = train_likes[(train_likes.timestamp < cutoff_ts) & (train_likes.uid.isin(uid_to_idx))].copy() if is_val: pref = pd.read_parquet(DATA_DIR / 'validation_preference_state.parquet') liked_pref = pref[(pref.state == 'liked') & (pref.uid.isin(uid_to_idx))].copy() liked_pref['timestamp'] = cutoff_ts - 1 likes_df = pd.concat([likes_df[['uid', 'item_id', 'timestamp']], liked_pref[['uid', 'item_id', 'timestamp']]]) likes_df['uid_idx'] = likes_df.uid.map(uid_to_idx) likes_df['item_idx'] = likes_df.item_id.map(item_id_to_idx) likes_df = likes_df.dropna(subset=['uid_idx', 'item_idx']).sort_values('timestamp') likes_df['uid_idx'] = likes_df.uid_idx.astype(np.int64) likes_df['item_idx'] = likes_df.item_idx.astype(np.int64) for u, g in likes_df.groupby('uid_idx'): itms = g.item_idx.to_numpy() k_take = min(len(itms), K_like) likes_arr[u, :k_take] = itms[-k_take:] stats_arr[u, 0] = np.log1p(len(itms)) train_dis = pd.read_parquet(DATA_DIR / 'train_dislikes.parquet') dis_df = train_dis[(train_dis.timestamp < cutoff_ts) & (train_dis.uid.isin(uid_to_idx))].copy() if is_val: pref = pd.read_parquet(DATA_DIR / 'validation_preference_state.parquet') dis_pref = pref[(pref.state == 'disliked') & (pref.uid.isin(uid_to_idx))].copy() dis_pref['timestamp'] = cutoff_ts - 1 dis_df = pd.concat([dis_df[['uid', 'item_id', 'timestamp']], dis_pref[['uid', 'item_id', 'timestamp']]]) dis_df['uid_idx'] = dis_df.uid.map(uid_to_idx) dis_df['item_idx'] = dis_df.item_id.map(item_id_to_idx) dis_df = dis_df.dropna(subset=['uid_idx', 'item_idx']).sort_values('timestamp') dis_df['uid_idx'] = dis_df.uid_idx.astype(np.int64) dis_df['item_idx'] = dis_df.item_idx.astype(np.int64) for u, g in dis_df.groupby('uid_idx'): itms = g.item_idx.to_numpy() k_take = min(len(itms), K_dis) dislikes_arr[u, :k_take] = itms[-k_take:] stats_arr[u, 1] = np.log1p(len(itms)) return torch.from_numpy(likes_arr), torch.from_numpy(dislikes_arr), torch.from_numpy(stats_arr) class UserTower(nn.Module): def __init__(self, item_embeddings: nn.Embedding, d_in: int, d_hidden: int, d_out: int): super().__init__() self.item_emb = item_embeddings self.stats_proj = nn.Linear(2, 16) # Channels: last_item (d_in), seq_pool (d_in), likes_pool (d_in), dislikes_pool (d_in), stats (16) in_dim = 4 * d_in + 16 self.mlp = nn.Sequential( nn.Linear(in_dim, d_hidden), nn.LayerNorm(d_hidden), nn.GELU(), nn.Dropout(0.1), nn.Linear(d_hidden, d_out), ) # Learnable popularity balance weight self.pop_weight = nn.Parameter(torch.tensor(0.15, dtype=torch.float32)) def forward(self, last_items, seq_items, seq_weights, likes, dislikes, stats): # 1. Last item embedding mask_last = (last_items > 0).float().unsqueeze(-1) h_last = self.item_emb(last_items) * mask_last # 2. Sequence pool mask_seq = ((seq_items > 0).float() * seq_weights).unsqueeze(-1) emb_seq = self.item_emb(seq_items) * mask_seq h_seq = emb_seq.sum(dim=1) / (mask_seq.sum(dim=1) + 1e-6) # 3. Likes pool mask_l = (likes > 0).float().unsqueeze(-1) emb_l = self.item_emb(likes) * mask_l h_l = emb_l.sum(dim=1) / (mask_l.sum(dim=1) + 1e-6) # 4. Dislikes pool mask_d = (dislikes > 0).float().unsqueeze(-1) emb_d = self.item_emb(dislikes) * mask_d h_d = emb_d.sum(dim=1) / (mask_d.sum(dim=1) + 1e-6) # 5. Stats h_stats = F.gelu(self.stats_proj(stats)) feat = torch.cat([h_last, h_seq, h_l, h_d, h_stats], dim=-1) u_base = self.mlp(feat) u_norm = F.normalize(u_base, p=2, dim=-1) # Concatenate popularity coordinate: # beta = torch.sigmoid(self.pop_weight) beta = self.pop_weight.clamp(0.01, 0.8) scale_sem = torch.sqrt(1.0 - beta**2) u_final = torch.cat([u_norm * scale_sem, beta.expand(len(u_norm), 1)], dim=-1) return u_final class ItemTower(nn.Module): def __init__(self, item_embeddings: nn.Embedding, catalog_feats: torch.Tensor, pop_scores: torch.Tensor, d_in: int, d_hidden: int, d_out: int): super().__init__() self.item_emb = item_embeddings self.register_buffer('catalog_feats', catalog_feats) self.register_buffer('pop_scores', pop_scores) self.feat_proj = nn.Linear(4, 16) self.mlp = nn.Sequential( nn.Linear(d_in + 16, d_hidden), nn.LayerNorm(d_hidden), nn.GELU(), nn.Linear(d_hidden, d_out), ) def forward(self, item_ids, pop_weight_param: torch.Tensor): emb = self.item_emb(item_ids) feats = self.catalog_feats[item_ids] h_feats = F.gelu(self.feat_proj(feats)) x = torch.cat([emb, h_feats], dim=-1) v_base = self.mlp(x) v_norm = F.normalize(v_base, p=2, dim=-1) # Append popularity coordinate beta = pop_weight_param.clamp(0.01, 0.8) scale_sem = torch.sqrt(1.0 - beta**2) pop_val = self.pop_scores[item_ids].unsqueeze(-1) v_final = torch.cat([v_norm * scale_sem, beta * pop_val], dim=-1) return v_final def run_experiment(): print("Loading data...") item_ids, item_id_to_idx, catalog_feats, pop_scores = get_catalog_features() targets_df = pd.read_parquet(DATA_DIR / 'target_users.parquet') uids = targets_df.uid.to_numpy() uid_to_idx = {int(uid): idx for idx, uid in enumerate(uids)} N_users = len(uids) num_items = len(item_ids) + 1 # 30001 cutoff_val = 24786800 # 1. Validation user history at cutoff print("Loading validation user history...") val_likes, val_dislikes, val_stats = load_user_likes_and_dislikes(cutoff_val, item_id_to_idx, uid_to_idx, N_users, is_val=True) # Load recent listens for validation users (last 30 days) t_30d = cutoff_val - 30 * 86400 val_listens = pq.read_table( DATA_DIR / 'train_listens.parquet', columns=['uid', 'item_id', 'timestamp', 'played_ratio_pct'], filters=[('timestamp', '>=', t_30d), ('timestamp', '<', cutoff_val)] ).to_pandas() val_listens = val_listens[val_listens.uid.isin(uid_to_idx) & val_listens.item_id.isin(item_id_to_idx)].sort_values(['uid', 'timestamp']) val_listens['uid_idx'] = val_listens.uid.map(uid_to_idx).astype(int) val_listens['item_idx'] = val_listens.item_id.map(item_id_to_idx).astype(int) K_seq = 32 val_last_items = np.zeros(N_users, dtype=np.int64) val_seq_items = np.zeros((N_users, K_seq), dtype=np.int64) val_seq_weights = np.zeros((N_users, K_seq), dtype=np.float32) for u, g in val_listens.groupby('uid_idx'): itms = g.item_idx.to_numpy() ts = g.timestamp.to_numpy(dtype=np.float64) diff = np.maximum(0.0, float(cutoff_val) - ts) w = np.exp(-diff / (7.0 * 86400.0)).astype(np.float32) val_last_items[u] = itms[-1] k_take = min(len(itms), K_seq) val_seq_items[u, :k_take] = itms[-k_take:] val_seq_weights[u, :k_take] = w[-k_take:] val_inputs = { 'last_items': torch.from_numpy(val_last_items), 'seq_items': torch.from_numpy(val_seq_items), 'seq_weights': torch.from_numpy(val_seq_weights), 'likes': val_likes, 'dislikes': val_dislikes, 'stats': val_stats, } # 2. Extract sequential training examples from 60 days of train listens print("Extracting sequential training data...") t_60d = cutoff_val - 60 * 86400 train_listens = pq.read_table( DATA_DIR / 'train_listens.parquet', columns=['uid', 'item_id', 'timestamp'], filters=[('timestamp', '>=', t_60d), ('timestamp', '<', cutoff_val), ('played_ratio_pct', '>=', 50)] ).to_pandas() train_listens = train_listens[train_listens.uid.isin(uid_to_idx) & train_listens.item_id.isin(item_id_to_idx)].sort_values(['uid', 'timestamp']) train_listens['uid_idx'] = train_listens.uid.map(uid_to_idx).astype(int) train_listens['item_idx'] = train_listens.item_id.map(item_id_to_idx).astype(int) user_seqs = train_listens.groupby('uid_idx')['item_idx'].apply(list).to_dict() # Also load likes for each user train_likes = pd.read_parquet(DATA_DIR / 'train_likes.parquet') train_likes = train_likes[train_likes.uid.isin(uid_to_idx) & train_likes.item_id.isin(item_id_to_idx) & (train_likes.timestamp < cutoff_val)] train_likes['uid_idx'] = train_likes.uid.map(uid_to_idx).astype(int) train_likes['item_idx'] = train_likes.item_id.map(item_id_to_idx).astype(int) user_liked_dict = train_likes.groupby('uid_idx')['item_idx'].apply(list).to_dict() train_last = [] train_seq = [] train_w = [] train_u = [] train_target = [] train_weights = [] rec_weights = np.exp(-np.arange(K_seq)[::-1] * 0.1).astype(np.float32) for u, seq in user_seqs.items(): if len(seq) < 2: continue step = max(1, len(seq) // 30) for t in range(1, len(seq), step): target_itm = seq[t] h = seq[max(0, t - K_seq):t] last_itm = h[-1] pad_seq = np.zeros(K_seq, dtype=np.int64) pad_w = np.zeros(K_seq, dtype=np.float32) pad_seq[:len(h)] = h pad_w[:len(h)] = rec_weights[-len(h):] train_last.append(last_itm) train_seq.append(pad_seq) train_w.append(pad_w) train_u.append(u) train_target.append(target_itm) train_weights.append(1.0) # Add liked items as targets if u in user_liked_dict: liked_items = user_liked_dict[u] for itm in liked_items[-10:]: # Use current end of sequence as context h = seq[-K_seq:] last_itm = h[-1] pad_seq = np.zeros(K_seq, dtype=np.int64) pad_w = np.zeros(K_seq, dtype=np.float32) pad_seq[:len(h)] = h pad_w[:len(h)] = rec_weights[-len(h):] train_last.append(last_itm) train_seq.append(pad_seq) train_w.append(pad_w) train_u.append(u) train_target.append(itm) train_weights.append(7.0) train_last = np.array(train_last, dtype=np.int64) train_seq = np.stack(train_seq, axis=0) train_w = np.stack(train_w, axis=0) train_u = np.array(train_u, dtype=np.int64) train_target = np.array(train_target, dtype=np.int64) train_weights = np.array(train_weights, dtype=np.float32) N_train = len(train_target) print(f"Total training pairs: {N_train}") # Model architecture d_emb = 64 d_hidden = 128 d_out = 63 # + 1 for pop = 64 shared_emb = nn.Embedding(num_items, d_emb, padding_idx=0) nn.init.normal_(shared_emb.weight, std=0.05) with torch.no_grad(): shared_emb.weight[0].fill_(0.0) user_tower = UserTower(shared_emb, d_emb, d_hidden, d_out) item_tower = ItemTower(shared_emb, catalog_feats, pop_scores, d_emb, d_hidden, d_out) logit_scale = nn.Parameter(torch.tensor(math.log(1.0 / 0.05), dtype=torch.float32)) params = list(user_tower.parameters()) + list(item_tower.feat_proj.parameters()) + list(item_tower.mlp.parameters()) + [logit_scale] optimizer = torch.optim.AdamW(params, lr=1e-3, weight_decay=1e-4) # Validation ground truth val_elig = CandidateSet(DATA_DIR / 'validation_eligible_candidates.npz') graded_val = validation_truth(val_elig, set(uids)) val_packed = np.load(DATA_DIR / 'validation_eligible_candidates.npz')['packed_eligible'] val_catalog_items = np.load(DATA_DIR / 'validation_eligible_candidates.npz')['item_ids'] bits = np.unpackbits(val_packed, axis=1, count=30000, bitorder='little').astype(bool) def evaluate_ranker(): user_tower.eval() item_tower.eval() with torch.no_grad(): all_cat_ids = torch.arange(1, num_items, dtype=torch.int64) V = item_tower(all_cat_ids, user_tower.pop_weight) # (30000, d_out + 1) user_vecs_list = [] u_bs = 1024 for s in range(0, N_users, u_bs): e = min(s + u_bs, N_users) u_batch = user_tower( val_inputs['last_items'][s:e], val_inputs['seq_items'][s:e], val_inputs['seq_weights'][s:e], val_inputs['likes'][s:e], val_inputs['dislikes'][s:e], val_inputs['stats'][s:e], ) user_vecs_list.append(u_batch) U = torch.cat(user_vecs_list, dim=0) # (8456, d_out + 1) scores = torch.matmul(U, V.t()).numpy() scores[~bits] = -1e9 ranked = {} for row, uid in enumerate(uids): top_cols = np.argpartition(scores[row], -100)[-100:] top_cols = top_cols[np.argsort(-scores[row][top_cols])] ranked[int(uid)] = list(map(int, val_catalog_items[top_cols])) return score(ranked, graded_val) print("\nInitial zero-shot evaluation...") print(evaluate_ranker()) batch_size = 512 epochs = 5 for epoch in range(1, epochs + 1): t0 = time.time() user_tower.train() item_tower.train() perm = np.random.permutation(N_train) total_loss = 0.0 n_b = 0 for b_start in range(0, N_train, batch_size): b_end = min(b_start + batch_size, N_train) if b_end - b_start < 64: continue idx = perm[b_start:b_end] u_sub = train_u[idx] last_sub = torch.from_numpy(train_last[idx]) seq_sub = torch.from_numpy(train_seq[idx]) w_sub = torch.from_numpy(train_w[idx]) likes_sub = val_likes[u_sub] dislikes_sub = val_dislikes[u_sub] stats_sub = val_stats[u_sub] b_target = torch.from_numpy(train_target[idx]) b_sample_w = torch.from_numpy(train_weights[idx]) u_vecs = user_tower(last_sub, seq_sub, w_sub, likes_sub, dislikes_sub, stats_sub) v_vecs = item_tower(b_target, user_tower.pop_weight) scale = logit_scale.exp().clamp(max=100.0) logits = torch.matmul(u_vecs, v_vecs.t()) * scale targets = torch.arange(len(idx), dtype=torch.int64) ce = F.cross_entropy(logits, targets, reduction='none') loss = (ce * b_sample_w).mean() optimizer.zero_grad() loss.backward() torch.nn.utils.clip_grad_norm_(params, max_norm=1.0) optimizer.step() total_loss += loss.item() n_b += 1 dur = time.time() - t0 print(f"Epoch {epoch:02d} | Time: {dur:.1f}s | Loss: {total_loss/n_b:.4f} | PopWeight: {user_tower.pop_weight.item():.4f}") eval_res = evaluate_ranker() print(f"Validation Score: {eval_res}") if __name__ == '__main__': run_experiment() ## Step 71 · agent · 2026-09-29T06:27:36.943Z ### run_shell_command · 2026-09-29T06:27:36.943Z { "command": "PYTHONPATH=/task python3 /app/solution/train_seq_two_tower.py", "description": "Run sequential two tower training and evaluation" } ### Tool output · 2026-09-29T06:29:08.715Z Output: Loading data... Loading validation user history... Extracting sequential training data... Total training pairs: 320625 Initial zero-shot evaluation... {'quality_ndcg@10': 0.000458, 'quality_users': 4263} Epoch 01 | Time: 13.9s | Loss: 11.9478 | PopWeight: 0.1709 Validation Score: {'quality_ndcg@10': 0.004966, 'quality_users': 4263} Epoch 02 | Time: 13.1s | Loss: 10.4212 | PopWeight: 0.1588 Validation Score: {'quality_ndcg@10': 0.006513, 'quality_users': 4263} Epoch 03 | Time: 13.2s | Loss: 9.3197 | PopWeight: 0.1465 Validation Score: {'quality_ndcg@10': 0.003595, 'quality_users': 4263} Epoch 04 | Time: 13.0s | Loss: 8.3222 | PopWeight: 0.1424 Validation Score: {'quality_ndcg@10': 0.003931, 'quality_users': 4263} Epoch 05 | Time: 13.1s | Loss: 7.4961 | PopWeight: 0.1350 Validation Score: {'quality_ndcg@10': 0.004145, 'quality_users': 4263} Process Group PGID: 2885 ## Step 72 · agent · 2026-09-29T06:29:30.418Z ### Recorded reasoning summary Analyzing PopWeight Discrepancy I'm currently investigating why a `PopWeight` of 0.15 yielded a score of 0.0065, which is lower than the 0.010578 achieved with pure popularity. This is a puzzling deviation, and I'm digging into the specifics of how `PopWeight` is influencing the evaluation. ### run_shell_command · 2026-09-29T06:29:30.418Z { "command": "PYTHONPATH=/task python3 -c \"\nimport pandas as pd\nimport numpy as np\nimport pyarrow.parquet as pq\nfrom scipy import sparse\nfrom pathlib import Path\nfrom ranking_evaluation import CandidateSet, score_rankings\nfrom evaluate_public import validation_truth\n\ndata_dir = Path('/task/data')\ntargets = set(pd.read_parquet(data_dir / 'target_users.parquet').uid.astype(int))\nelig = CandidateSet(data_dir / 'validation_eligible_candidates.npz')\ntruth = validation_truth(elig, targets)\ncatalog = pd.read_parquet(data_dir / 'candidate_catalog.parquet')\nitem_id_to_idx = {id_: idx for idx, id_ in enumerate(catalog.item_id)}\n\ncutoff = 24786800\nt_start = cutoff - 14 * 86400\nlistens = pq.read_table(\n data_dir / 'train_listens.parquet',\n columns=['uid', 'item_id'],\n filters=[('timestamp', '>=', t_start), ('timestamp', '<', cutoff), ('is_organic', '==', 1), ('played_ratio_pct', '>=', 80)]\n).to_pandas()\nlistens = listens[listens.item_id.isin(item_id_to_idx) & listens.uid.isin(targets)]\nlistens['item_idx'] = listens.item_id.map(item_id_to_idx)\n\n# Let's inspect: What does C = X.T @ X represent?\n# C_ij is the number of users who co-listened to item i and item j!\n# How many non-zero entries does C have?\nuids = np.load(data_dir / 'validation_eligible_candidates.npz')['uids']\nuid_to_row = {uid: r for r, uid in enumerate(uids)}\nlistens['row'] = listens.uid.map(uid_to_row)\nrow_indices = listens['row'].to_numpy(dtype=int)\ncol_indices = listens['item_idx'].to_numpy(dtype=int)\ndata = np.ones(len(listens), dtype=np.float32)\n\nX = sparse.csr_matrix((data, (row_indices, col_indices)), shape=(len(uids), 30000))\nC = X.T @ X\nC.setdiag(0)\n\nprint('C shape:', C.shape, 'C nonzeros:', C.nnz, f'sparsity: {C.nnz / (30000*30000):.4%}')\nprint('Items with at least 1 co-occurrence in C:', np.diff(C.indptr).astype(bool).sum())\n\"", "description": "Analyze item co-occurrence properties" } ### Tool output · 2026-09-29T06:29:34.120Z Output: C shape: (30000, 30000) C nonzeros: 23193112 sparsity: 2.5770% Items with at least 1 co-occurrence in C: 30000 Process Group PGID: 2924 ## Step 73 · agent · 2026-09-29T06:29:57.459Z ### Recorded reasoning summary Analyzing Gram Matrix My current focus is on the newly computed item-item Gram matrix, $C$. I've confirmed that all 30,000 items exhibit co-occurrences within it, resulting in a substantial 23,193,112 non-zero entries. This matrix is a critical component for the next stage of analysis. ### Recorded reasoning summary Deriving User Scores I've realized that the user's score for a candidate item is simply the sum of similarities between that candidate and all items in their history, as derived from the Gram matrix. This offers a more efficient way to compute relevance. ### Recorded reasoning summary Exploring Two-Tower Dynamics I'm now considering how the Gram matrix relates to the dot product in a two-tower model. Specifically, I'm investigating how item embeddings $v_i$ and $v_j$ and their relationship $\langle v_i, v_j \rangle$ might approximate the observed co-occurrences $C_{i, j}$ and consequently, how user vectors derived from interaction history can learn to predict relevance based on this. ### Recorded reasoning summary Investigating Performance Gaps I'm examining discrepancies between our Gram matrix insights and the sequential two-tower model's performance. The user history length and item weighting in the sequential model appear significantly different from the broader co-occurrence analysis, potentially explaining the observed performance gap. ### Recorded reasoning summary Testing Embedding Approximation I'm exploring if item embeddings can directly approximate item-item co-occurrences, akin to Word2Vec's implicit PMI factorization. I'm keen to test if PyTorch SGNS or cosine similarity loss on co-occurrence pairs can achieve this, particularly given the effectiveness of local pair training for large-scale datasets. ### run_shell_command · 2026-09-29T06:29:57.459Z { "command": "python3 -c \"\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nimport numpy as np\nimport time\n\n# Benchmark PyTorch training on 2M item pairs\nN_items = 30001\nd = 128\nbatch_size = 4096\nemb = nn.Embedding(N_items, d)\nnn.init.normal_(emb.weight, std=0.05)\nopt = torch.optim.Adam(emb.parameters(), lr=0.01)\n\nsrc = torch.randint(1, N_items, (batch_size,))\ntgt = torch.randint(1, N_items, (batch_size,))\n\nstart = time.time()\nn_steps = 100\nfor _ in range(n_steps):\n opt.zero_grad()\n u = F.normalize(emb(src), dim=-1)\n v = F.normalize(emb(tgt), dim=-1)\n # in-batch dot product: (B, B)\n logits = torch.matmul(u, v.t()) * 20.0\n loss = F.cross_entropy(logits, torch.arange(batch_size))\n loss.backward()\n opt.step()\n\ndur = time.time() - start\nprint(f'Trained {n_steps * batch_size} pairs in {dur:.2f}s ({(n_steps * batch_size)/dur:.0f} pairs/sec)!')\n\"", "description": "Benchmark PyTorch item-item contrastive training speed" } ### Tool output · 2026-09-29T06:30:22.553Z Output: Trained 409600 pairs in 19.05s (21496 pairs/sec)! Process Group PGID: 2953 ## Step 74 · agent · 2026-09-29T06:30:40.623Z ### Recorded reasoning summary Analyzing Batch Size Performance I'm currently investigating the performance impact of varying batch sizes on matrix multiplication operations. Initial findings show a significant throughput of 21,500 pairs per second with a batch size of 4096. This seems to stem from the computational cost of a $(4096, 4096)$ matrix multiplication requiring approximately 190ms due to $16.7$ million operations. I'm now examining how this scales down to a batch size of 1024 for $(1024, 1024)$ computations. ### run_shell_command · 2026-09-29T06:30:40.623Z { "description": "Benchmark batch size 1024", "command": "python3 -c \"\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nimport numpy as np\nimport time\n\nN_items = 30001\nd = 128\nbatch_size = 1024\nemb = nn.Embedding(N_items, d)\nnn.init.normal_(emb.weight, std=0.05)\nopt = torch.optim.Adam(emb.parameters(), lr=0.01)\n\nsrc = torch.randint(1, N_items, (batch_size,))\ntgt = torch.randint(1, N_items, (batch_size,))\n\nstart = time.time()\nn_steps = 100\nfor _ in range(n_steps):\n opt.zero_grad()\n u = F.normalize(emb(src), dim=-1)\n v = F.normalize(emb(tgt), dim=-1)\n logits = torch.matmul(u, v.t()) * 20.0\n loss = F.cross_entropy(logits, torch.arange(batch_size))\n loss.backward()\n opt.step()\n\ndur = time.time() - start\nprint(f'Trained {n_steps * batch_size} pairs in {dur:.2f}s ({(n_steps * batch_size)/dur:.0f} pairs/sec)!')\n\"" } ### Tool output · 2026-09-29T06:30:49.243Z Output: Trained 102400 pairs in 2.76s (37083 pairs/sec)! Process Group PGID: 2972 ## Step 75 · agent · 2026-09-29T06:30:58.887Z ### Recorded reasoning summary Achieving High Throughput I've achieved a throughput of 37,000 pairs per second with a batch size of 1024. My current focus is on understanding how a two-tower neural network can maximize NDCG@10, specifically analyzing the nuances of the graded NDCG@10 evaluation metric. ### run_shell_command · 2026-09-29T06:30:58.887Z { "description": "Test 14 vs 30 vs 45 days of listens", "command": "PYTHONPATH=/task python3 -c \"\nimport pandas as pd\nimport numpy as np\nimport pyarrow.parquet as pq\nfrom scipy import sparse\nfrom pathlib import Path\nfrom ranking_evaluation import CandidateSet, score_rankings\nfrom evaluate_public import validation_truth\n\ndata_dir = Path('/task/data')\ntargets = set(pd.read_parquet(data_dir / 'target_users.parquet').uid.astype(int))\nelig = CandidateSet(data_dir / 'validation_eligible_candidates.npz')\ntruth = validation_truth(elig, targets)\ncatalog = pd.read_parquet(data_dir / 'candidate_catalog.parquet')\nitem_id_to_idx = {id_: idx for idx, id_ in enumerate(catalog.item_id)}\nuids = np.load(data_dir / 'validation_eligible_candidates.npz')['uids']\nuid_to_row = {uid: r for r, uid in enumerate(uids)}\n\ncutoff = 24786800\n\n# Test 1: 30 days of strong listens vs 14 days\nfor days in [14, 30, 45]:\n t_start = cutoff - days * 86400\n listens = pq.read_table(\n data_dir / 'train_listens.parquet',\n columns=['uid', 'item_id', 'timestamp'],\n filters=[('timestamp', '>=', t_start), ('timestamp', '<', cutoff), ('is_organic', '==', 1), ('played_ratio_pct', '>=', 80)]\n ).to_pandas()\n listens = listens[listens.item_id.isin(item_id_to_idx) & listens.uid.isin(targets)]\n listens['item_idx'] = listens.item_id.map(item_id_to_idx)\n listens['row'] = listens.uid.map(uid_to_row)\n \n row_indices = listens['row'].to_numpy(dtype=int)\n col_indices = listens['item_idx'].to_numpy(dtype=int)\n data = np.ones(len(listens), dtype=np.float32)\n \n X = sparse.csr_matrix((data, (row_indices, col_indices)), shape=(len(uids), 30000))\n C = X.T @ X\n C.setdiag(0)\n user_sim = X @ C\n \n packed = np.load(data_dir / 'validation_eligible_candidates.npz')['packed_eligible']\n cat_items = np.load(data_dir / 'validation_eligible_candidates.npz')['item_ids']\n bits = np.unpackbits(packed, axis=1, count=30000, bitorder='little').astype(bool)\n pop = np.log1p(catalog.organic_positive_listens.to_numpy(dtype=np.float32))\n \n ranked = {}\n for r, uid in enumerate(uids):\n s_row = user_sim[r].toarray().ravel()\n if s_row.max() > 0:\n s_row = s_row / s_row.max()\n score_val = s_row + 0.05 * (pop / pop.max())\n score_val[~bits[r]] = -1e9\n top = np.argpartition(score_val, -100)[-100:]\n top = top[np.argsort(-score_val[top])]\n ranked[int(uid)] = list(map(int, cat_items[top]))\n res = score_rankings(ranked, truth)\n print(f'Days={days}: {res}')\n\"" } ### Tool output · 2026-09-29T06:31:35.895Z Output: Days=14: {'quality_ndcg@10': 0.024902, 'quality_users': 4263} Days=30: {'quality_ndcg@10': 0.013137, 'quality_users': 4263} Days=45: {'quality_ndcg@10': 0.012411, 'quality_users': 4263} Process Group PGID: 2985 ## Step 76 · agent · 2026-09-29T06:33:23.151Z ### Recorded reasoning summary Discovering User Behavior I've just had a significant insight! It appears that user music listening habits change quite rapidly. The data shows a much higher score for 14 days compared to 30 or 45 days, suggesting that recent listening is a far stronger indicator of future preferences than older data. ### run_shell_command · 2026-09-29T06:33:23.151Z { "description": "Test fine-grained days window (3, 5, 7, 10, 14)", "command": "PYTHONPATH=/task python3 -c \"\nimport pandas as pd\nimport numpy as np\nimport pyarrow.parquet as pq\nfrom scipy import sparse\nfrom pathlib import Path\nfrom ranking_evaluation import CandidateSet, score_rankings\nfrom evaluate_public import validation_truth\n\ndata_dir = Path('/task/data')\ntargets = set(pd.read_parquet(data_dir / 'target_users.parquet').uid.astype(int))\nelig = CandidateSet(data_dir / 'validation_eligible_candidates.npz')\ntruth = validation_truth(elig, targets)\ncatalog = pd.read_parquet(data_dir / 'candidate_catalog.parquet')\nitem_id_to_idx = {id_: idx for idx, id_ in enumerate(catalog.item_id)}\nuids = np.load(data_dir / 'validation_eligible_candidates.npz')['uids']\nuid_to_row = {uid: r for r, uid in enumerate(uids)}\n\ncutoff = 24786800\n\nfor days in [3, 5, 7, 10, 14]:\n t_start = cutoff - days * 86400\n listens = pq.read_table(\n data_dir / 'train_listens.parquet',\n columns=['uid', 'item_id', 'timestamp'],\n filters=[('timestamp', '>=', t_start), ('timestamp', '<', cutoff), ('is_organic', '==', 1), ('played_ratio_pct', '>=', 80)]\n ).to_pandas()\n listens = listens[listens.item_id.isin(item_id_to_idx) & listens.uid.isin(targets)]\n listens['item_idx'] = listens.item_id.map(item_id_to_idx)\n listens['row'] = listens.uid.map(uid_to_row)\n \n row_indices = listens['row'].to_numpy(dtype=int)\n col_indices = listens['item_idx'].to_numpy(dtype=int)\n data = np.ones(len(listens), dtype=np.float32)\n \n X = sparse.csr_matrix((data, (row_indices, col_indices)), shape=(len(uids), 30000))\n C = X.T @ X\n C.setdiag(0)\n user_sim = X @ C\n \n packed = np.load(data_dir / 'validation_eligible_candidates.npz')['packed_eligible']\n cat_items = np.load(data_dir / 'validation_eligible_candidates.npz')['item_ids']\n bits = np.unpackbits(packed, axis=1, count=30000, bitorder='little').astype(bool)\n pop = np.log1p(catalog.organic_positive_listens.to_numpy(dtype=np.float32))\n \n ranked = {}\n for r, uid in enumerate(uids):\n s_row = user_sim[r].toarray().ravel()\n if s_row.max() > 0:\n s_row = s_row / s_row.max()\n score_val = s_row + 0.05 * (pop / pop.max())\n score_val[~bits[r]] = -1e9\n top = np.argpartition(score_val, -100)[-100:]\n top = top[np.argsort(-score_val[top])]\n ranked[int(uid)] = list(map(int, cat_items[top]))\n res = score_rankings(ranked, truth)\n print(f'Days={days}: {res}')\n\"" } ### Tool output · 2026-09-29T06:33:51.024Z Output: Days=3: {'quality_ndcg@10': 0.022301, 'quality_users': 4263} Days=5: {'quality_ndcg@10': 0.02416, 'quality_users': 4263} Days=7: {'quality_ndcg@10': 0.026995, 'quality_users': 4263} Days=10: {'quality_ndcg@10': 0.02669, 'quality_users': 4263} Days=14: {'quality_ndcg@10': 0.024902, 'quality_users': 4263} Process Group PGID: 3026 ## Step 77 · agent · 2026-09-29T06:35:47.062Z ### run_shell_command · 2026-09-29T06:35:47.062Z { "description": "Test combining listens and likes in user history", "command": "PYTHONPATH=/task python3 -c \"\nimport pandas as pd\nimport numpy as np\nimport pyarrow.parquet as pq\nfrom scipy import sparse\nfrom pathlib import Path\nfrom ranking_evaluation import CandidateSet, score_rankings\nfrom evaluate_public import validation_truth\n\ndata_dir = Path('/task/data')\ntargets = set(pd.read_parquet(data_dir / 'target_users.parquet').uid.astype(int))\nelig = CandidateSet(data_dir / 'validation_eligible_candidates.npz')\ntruth = validation_truth(elig, targets)\ncatalog = pd.read_parquet(data_dir / 'candidate_catalog.parquet')\nitem_id_to_idx = {id_: idx for idx, id_ in enumerate(catalog.item_id)}\nuids = np.load(data_dir / 'validation_eligible_candidates.npz')['uids']\nuid_to_row = {uid: r for r, uid in enumerate(uids)}\n\ncutoff = 24786800\n\n# 7-day listens\nlistens = pq.read_table(\n data_dir / 'train_listens.parquet',\n columns=['uid', 'item_id', 'timestamp'],\n filters=[('timestamp', '>=', cutoff - 7 * 86400), ('timestamp', '<', cutoff), ('is_organic', '==', 1), ('played_ratio_pct', '>=', 80)]\n).to_pandas()\nlistens = listens[listens.item_id.isin(item_id_to_idx) & listens.uid.isin(targets)].copy()\nlistens['weight'] = 1.0\n\n# Likes\nlikes = pd.read_parquet(data_dir / 'train_likes.parquet')\nlikes = likes[likes.item_id.isin(item_id_to_idx) & likes.uid.isin(targets) & (likes.timestamp < cutoff)].copy()\n# Test likes weights and windows:\nfor like_days, like_w in [(7, 3.0), (14, 3.0), (30, 3.0), (90, 1.0), (90, 3.0)]:\n l_sub = likes[likes.timestamp >= cutoff - like_days * 86400].copy()\n l_sub['weight'] = like_w\n comb = pd.concat([listens[['uid', 'item_id', 'weight']], l_sub[['uid', 'item_id', 'weight']]])\n comb['item_idx'] = comb.item_id.map(item_id_to_idx)\n comb['row'] = comb.uid.map(uid_to_row)\n \n # Sum weights per user-item\n agg = comb.groupby(['row', 'item_idx'])['weight'].sum().reset_index()\n X = sparse.csr_matrix((agg['weight'].to_numpy(dtype=np.float32), (agg['row'].to_numpy(dtype=int), agg['item_idx'].to_numpy(dtype=int))), shape=(len(uids), 30000))\n C = X.T @ X\n C.setdiag(0)\n user_sim = X @ C\n \n packed = np.load(data_dir / 'validation_eligible_candidates.npz')['packed_eligible']\n cat_items = np.load(data_dir / 'validation_eligible_candidates.npz')['item_ids']\n bits = np.unpackbits(packed, axis=1, count=30000, bitorder='little').astype(bool)\n pop = np.log1p(catalog.organic_positive_listens.to_numpy(dtype=np.float32))\n \n ranked = {}\n for r, uid in enumerate(uids):\n s_row = user_sim[r].toarray().ravel()\n if s_row.max() > 0:\n s_row = s_row / s_row.max()\n score_val = s_row + 0.05 * (pop / pop.max())\n score_val[~bits[r]] = -1e9\n top = np.argpartition(score_val, -100)[-100:]\n top = top[np.argsort(-score_val[top])]\n ranked[int(uid)] = list(map(int, cat_items[top]))\n res = score_rankings(ranked, truth)\n print(f'Likes days={like_days}, w={like_w}: {res}')\n\"" } ### Tool output · 2026-09-29T06:36:16.662Z Output: Likes days=7, w=3.0: {'quality_ndcg@10': 0.028202, 'quality_users': 4263} Likes days=14, w=3.0: {'quality_ndcg@10': 0.028454, 'quality_users': 4263} Likes days=30, w=3.0: {'quality_ndcg@10': 0.031162, 'quality_users': 4263} Likes days=90, w=1.0: {'quality_ndcg@10': 0.031523, 'quality_users': 4263} Likes days=90, w=3.0: {'quality_ndcg@10': 0.033011, 'quality_users': 4263} Process Group PGID: 3073 ## Step 78 · agent · 2026-09-29T06:36:27.632Z ### run_shell_command · 2026-09-29T06:36:27.632Z { "description": "Test active preference state with listens and likes", "command": "PYTHONPATH=/task python3 -c \"\nimport pandas as pd\nimport numpy as np\nimport pyarrow.parquet as pq\nfrom scipy import sparse\nfrom pathlib import Path\nfrom ranking_evaluation import CandidateSet, score_rankings\nfrom evaluate_public import validation_truth\n\ndata_dir = Path('/task/data')\ntargets = set(pd.read_parquet(data_dir / 'target_users.parquet').uid.astype(int))\nelig = CandidateSet(data_dir / 'validation_eligible_candidates.npz')\ntruth = validation_truth(elig, targets)\ncatalog = pd.read_parquet(data_dir / 'candidate_catalog.parquet')\nitem_id_to_idx = {id_: idx for idx, id_ in enumerate(catalog.item_id)}\nuids = np.load(data_dir / 'validation_eligible_candidates.npz')['uids']\nuid_to_row = {uid: r for r, uid in enumerate(uids)}\n\ncutoff = 24786800\n\n# 7-day listens\nlistens = pq.read_table(\n data_dir / 'train_listens.parquet',\n columns=['uid', 'item_id', 'timestamp'],\n filters=[('timestamp', '>=', cutoff - 7 * 86400), ('timestamp', '<', cutoff), ('is_organic', '==', 1), ('played_ratio_pct', '>=', 80)]\n).to_pandas()\nlistens = listens[listens.item_id.isin(item_id_to_idx) & listens.uid.isin(targets)].copy()\nlistens['weight'] = 1.0\n\n# Likes\nlikes = pd.read_parquet(data_dir / 'train_likes.parquet')\nlikes = likes[likes.item_id.isin(item_id_to_idx) & likes.uid.isin(targets) & (likes.timestamp < cutoff)].copy()\n# Recency weight on likes:\ndiff = np.maximum(0.0, float(cutoff) - likes.timestamp.to_numpy(dtype=float))\nlikes['weight'] = 3.0 * (0.5 + 0.5 * np.exp(-diff / (30 * 86400)))\n\n# Also active likes from preference state\npref = pd.read_parquet(data_dir / 'validation_preference_state.parquet')\nliked_pref = pref[(pref.state == 'liked') & (pref.uid.isin(targets)) & (pref.item_id.isin(item_id_to_idx))].copy()\nliked_pref['weight'] = 5.0\n\ncomb = pd.concat([listens[['uid', 'item_id', 'weight']], likes[['uid', 'item_id', 'weight']], liked_pref[['uid', 'item_id', 'weight']]])\ncomb['item_idx'] = comb.item_id.map(item_id_to_idx)\ncomb['row'] = comb.uid.map(uid_to_row)\n\nagg = comb.groupby(['row', 'item_idx'])['weight'].sum().reset_index()\nX = sparse.csr_matrix((agg['weight'].to_numpy(dtype=np.float32), (agg['row'].to_numpy(dtype=int), agg['item_idx'].to_numpy(dtype=int))), shape=(len(uids), 30000))\nC = X.T @ X\nC.setdiag(0)\nuser_sim = X @ C\n\npacked = np.load(data_dir / 'validation_eligible_candidates.npz')['packed_eligible']\ncat_items = np.load(data_dir / 'validation_eligible_candidates.npz')['item_ids']\nbits = np.unpackbits(packed, axis=1, count=30000, bitorder='little').astype(bool)\npop = np.log1p(catalog.organic_positive_listens.to_numpy(dtype=np.float32))\n\nfor alpha in [0.01, 0.03, 0.05, 0.08, 0.1]:\n ranked = {}\n for r, uid in enumerate(uids):\n s_row = user_sim[r].toarray().ravel()\n if s_row.max() > 0:\n s_row = s_row / s_row.max()\n score_val = s_row + alpha * (pop / pop.max())\n score_val[~bits[r]] = -1e9\n top = np.argpartition(score_val, -100)[-100:]\n top = top[np.argsort(-score_val[top])]\n ranked[int(uid)] = list(map(int, cat_items[top]))\n print(f'alpha={alpha}: {score_rankings(ranked, truth)}')\n\"" } ### Tool output · 2026-09-29T06:36:53.369Z Output: alpha=0.01: {'quality_ndcg@10': 0.020912, 'quality_users': 4263} alpha=0.03: {'quality_ndcg@10': 0.020767, 'quality_users': 4263} alpha=0.05: {'quality_ndcg@10': 0.020694, 'quality_users': 4263} alpha=0.08: {'quality_ndcg@10': 0.020599, 'quality_users': 4263} alpha=0.1: {'quality_ndcg@10': 0.020566, 'quality_users': 4263} Process Group PGID: 3102 ## Step 79 · agent · 2026-09-29T06:37:06.651Z ### Recorded reasoning summary Refining Recommendations My recent thoughts are focused on how the recency of user preferences significantly impacts recommendation relevance. I've realized that weighting older "likes" too heavily, as demonstrated by a 5.0 weight across all time, drastically reduces overall score, suggesting a need to prioritize more recent interactions, specifically within the last 14-30 days. ### run_shell_command · 2026-09-29T06:37:06.651Z { "command": "PYTHONPATH=/task python3 -c \"\nimport pandas as pd\nimport numpy as np\nimport pyarrow.parquet as pq\nfrom scipy import sparse\nfrom pathlib import Path\nfrom ranking_evaluation import CandidateSet, score_rankings\nfrom evaluate_public import validation_truth\n\ndata_dir = Path('/task/data')\ntargets = set(pd.read_parquet(data_dir / 'target_users.parquet').uid.astype(int))\nelig = CandidateSet(data_dir / 'validation_eligible_candidates.npz')\ntruth = validation_truth(elig, targets)\ncatalog = pd.read_parquet(data_dir / 'candidate_catalog.parquet')\nitem_id_to_idx = {id_: idx for idx, id_ in enumerate(catalog.item_id)}\nuids = np.load(data_dir / 'validation_eligible_candidates.npz')['uids']\nuid_to_row = {uid: r for r, uid in enumerate(uids)}\n\ncutoff = 24786800\n\n# 7-day listens\nlistens = pq.read_table(\n data_dir / 'train_listens.parquet',\n columns=['uid', 'item_id', 'timestamp'],\n filters=[('timestamp', '>=', cutoff - 7 * 86400), ('timestamp', '<', cutoff), ('is_organic', '==', 1), ('played_ratio_pct', '>=', 80)]\n).to_pandas()\nlistens = listens[listens.item_id.isin(item_id_to_idx) & listens.uid.isin(targets)].copy()\nlistens['weight'] = 1.0\n\n# Likes in last 30 days\nlikes = pd.read_parquet(data_dir / 'train_likes.parquet')\nlikes = likes[likes.item_id.isin(item_id_to_idx) & likes.uid.isin(targets) & (likes.timestamp >= cutoff - 30 * 86400) & (likes.timestamp < cutoff)].copy()\n\nfor w_like in [1.0, 2.0, 3.0, 5.0]:\n likes['weight'] = w_like\n comb = pd.concat([listens[['uid', 'item_id', 'weight']], likes[['uid', 'item_id', 'weight']]])\n comb['item_idx'] = comb.item_id.map(item_id_to_idx)\n comb['row'] = comb.uid.map(uid_to_row)\n \n agg = comb.groupby(['row', 'item_idx'])['weight'].sum().reset_index()\n X = sparse.csr_matrix((agg['weight'].to_numpy(dtype=np.float32), (agg['row'].to_numpy(dtype=int), agg['item_idx'].to_numpy(dtype=int))), shape=(len(uids), 30000))\n C = X.T @ X\n C.setdiag(0)\n user_sim = X @ C\n \n packed = np.load(data_dir / 'validation_eligible_candidates.npz')['packed_eligible']\n cat_items = np.load(data_dir / 'validation_eligible_candidates.npz')['item_ids']\n bits = np.unpackbits(packed, axis=1, count=30000, bitorder='little').astype(bool)\n pop = np.log1p(catalog.organic_positive_listens.to_numpy(dtype=np.float32))\n \n for alpha in [0.03, 0.05]:\n ranked = {}\n for r, uid in enumerate(uids):\n s_row = user_sim[r].toarray().ravel()\n if s_row.max() > 0:\n s_row = s_row / s_row.max()\n score_val = s_row + alpha * (pop / pop.max())\n score_val[~bits[r]] = -1e9\n top = np.argpartition(score_val, -100)[-100:]\n top = top[np.argsort(-score_val[top])]\n ranked[int(uid)] = list(map(int, cat_items[top]))\n print(f'w_like={w_like}, alpha={alpha}: {score_rankings(ranked, truth)}')\n\"", "description": "Test fine-tuned like weights and alpha" } ### Tool output · 2026-09-29T06:37:36.752Z Output: w_like=1.0, alpha=0.03: {'quality_ndcg@10': 0.02935, 'quality_users': 4263} w_like=1.0, alpha=0.05: {'quality_ndcg@10': 0.029108, 'quality_users': 4263} w_like=2.0, alpha=0.03: {'quality_ndcg@10': 0.03026, 'quality_users': 4263} w_like=2.0, alpha=0.05: {'quality_ndcg@10': 0.03015, 'quality_users': 4263} w_like=3.0, alpha=0.03: {'quality_ndcg@10': 0.031143, 'quality_users': 4263} w_like=3.0, alpha=0.05: {'quality_ndcg@10': 0.031162, 'quality_users': 4263} w_like=5.0, alpha=0.03: {'quality_ndcg@10': 0.031704, 'quality_users': 4263} w_like=5.0, alpha=0.05: {'quality_ndcg@10': 0.031666, 'quality_users': 4263} Process Group PGID: 3137 ## Step 80 · agent · 2026-09-29T06:37:44.778Z ### run_shell_command · 2026-09-29T06:37:44.778Z { "command": "PYTHONPATH=/task python3 -c \"\nimport pandas as pd\nimport numpy as np\nimport pyarrow.parquet as pq\nfrom scipy import sparse\nfrom pathlib import Path\nfrom ranking_evaluation import CandidateSet, score_rankings\nfrom evaluate_public import validation_truth\n\ndata_dir = Path('/task/data')\ntargets = set(pd.read_parquet(data_dir / 'target_users.parquet').uid.astype(int))\nelig = CandidateSet(data_dir / 'validation_eligible_candidates.npz')\ntruth = validation_truth(elig, targets)\ncatalog = pd.read_parquet(data_dir / 'candidate_catalog.parquet')\nitem_id_to_idx = {id_: idx for idx, id_ in enumerate(catalog.item_id)}\nuids = np.load(data_dir / 'validation_eligible_candidates.npz')['uids']\nuid_to_row = {uid: r for r, uid in enumerate(uids)}\n\ncutoff = 24786800\n\n# 7-day listens\nlistens = pq.read_table(\n data_dir / 'train_listens.parquet',\n columns=['uid', 'item_id', 'timestamp'],\n filters=[('timestamp', '>=', cutoff - 7 * 86400), ('timestamp', '<', cutoff), ('is_organic', '==', 1), ('played_ratio_pct', '>=', 80)]\n).to_pandas()\nlistens = listens[listens.item_id.isin(item_id_to_idx) & listens.uid.isin(targets)].copy()\nlistens['weight'] = 1.0\n\nlikes = pd.read_parquet(data_dir / 'train_likes.parquet')\nlikes = likes[likes.item_id.isin(item_id_to_idx) & likes.uid.isin(targets) & (likes.timestamp < cutoff)].copy()\n\npacked = np.load(data_dir / 'validation_eligible_candidates.npz')['packed_eligible']\ncat_items = np.load(data_dir / 'validation_eligible_candidates.npz')['item_ids']\nbits = np.unpackbits(packed, axis=1, count=30000, bitorder='little').astype(bool)\npop = np.log1p(catalog.organic_positive_listens.to_numpy(dtype=np.float32))\n\nfor like_days in [30, 45, 60, 90]:\n l_sub = likes[likes.timestamp >= cutoff - like_days * 86400].copy()\n l_sub['weight'] = 5.0\n comb = pd.concat([listens[['uid', 'item_id', 'weight']], l_sub[['uid', 'item_id', 'weight']]])\n comb['item_idx'] = comb.item_id.map(item_id_to_idx)\n comb['row'] = comb.uid.map(uid_to_row)\n \n agg = comb.groupby(['row', 'item_idx'])['weight'].sum().reset_index()\n X = sparse.csr_matrix((agg['weight'].to_numpy(dtype=np.float32), (agg['row'].to_numpy(dtype=int), agg['item_idx'].to_numpy(dtype=int))), shape=(len(uids), 30000))\n C = X.T @ X\n C.setdiag(0)\n user_sim = X @ C\n \n ranked = {}\n for r, uid in enumerate(uids):\n s_row = user_sim[r].toarray().ravel()\n if s_row.max() > 0:\n s_row = s_row / s_row.max()\n score_val = s_row + 0.03 * (pop / pop.max())\n score_val[~bits[r]] = -1e9\n top = np.argpartition(score_val, -100)[-100:]\n top = top[np.argsort(-score_val[top])]\n ranked[int(uid)] = list(map(int, cat_items[top]))\n print(f'like_days={like_days}: {score_rankings(ranked, truth)}')\n\"", "description": "Test like_days range" } ### Tool output · 2026-09-29T06:38:10.156Z Output: like_days=30: {'quality_ndcg@10': 0.031704, 'quality_users': 4263} like_days=45: {'quality_ndcg@10': 0.032777, 'quality_users': 4263} like_days=60: {'quality_ndcg@10': 0.033397, 'quality_users': 4263} like_days=90: {'quality_ndcg@10': 0.031867, 'quality_users': 4263} Process Group PGID: 3172 ## Step 81 · agent · 2026-09-29T06:38:33.967Z ### Recorded reasoning summary Formulating Model Structure I'm currently focusing on defining the architecture for our two-tower neural network in PyTorch. My next step involves translating the existing ideas into concrete code, ensuring it aligns perfectly with the user's initial requirements for this type of model. ### Recorded reasoning summary Verifying Mathematical Links I'm now intensely scrutinizing the mathematical relationship between the item-item kernel and the two-tower setup. This verification is crucial to ensure our PyTorch implementation accurately reflects the intended offline item embedding computation and on-the-fly user relevance scoring, especially considering the need for scalability. ### Recorded reasoning summary Connecting Item Similarity I've been deeply considering how the item-item similarity matrix $C$ fundamentally connects to the two-tower model's inner product score. It appears that a positive semidefinite similarity matrix can always be represented as an inner product, which directly informs how user and item embeddings are aggregated and compared for scoring. ### Recorded reasoning summary Training Item Embeddings I'm now exploring how to train the item vectors $V$. The goal is to approximate the item-item similarity matrix $C$. I'm considering using an item tower in PyTorch to minimize reconstruction error or employ a contrastive loss. I'm also investigating if $V$ can be directly computed via decomposition of $C$, while respecting the constraint against constructing a global matrix. ### Recorded reasoning summary Refining User Representation I'm now re-evaluating the user tower's design, specifically ensuring it derives representations from interaction history rather than a simple user-ID lookup. This addresses the constraint against global matrices and ensures scalability for large production systems. ### run_shell_command · 2026-09-29T06:38:33.967Z { "command": "PYTHONPATH=/task python3 -c \"\nimport pandas as pd\nimport numpy as np\nimport pyarrow.parquet as pq\nfrom scipy import sparse\nfrom sklearn.decomposition import TruncatedSVD\nfrom pathlib import Path\nfrom ranking_evaluation import CandidateSet, score_rankings\nfrom evaluate_public import validation_truth\n\ndata_dir = Path('/task/data')\ntargets = set(pd.read_parquet(data_dir / 'target_users.parquet').uid.astype(int))\nelig = CandidateSet(data_dir / 'validation_eligible_candidates.npz')\ntruth = validation_truth(elig, targets)\ncatalog = pd.read_parquet(data_dir / 'candidate_catalog.parquet')\nitem_id_to_idx = {id_: idx for idx, id_ in enumerate(catalog.item_id)}\nuids = np.load(data_dir / 'validation_eligible_candidates.npz')['uids']\nuid_to_row = {uid: r for r, uid in enumerate(uids)}\n\ncutoff = 24786800\n\n# 7-day listens\nlistens = pq.read_table(\n data_dir / 'train_listens.parquet',\n columns=['uid', 'item_id', 'timestamp'],\n filters=[('timestamp', '>=', cutoff - 7 * 86400), ('timestamp', '<', cutoff), ('is_organic', '==', 1), ('played_ratio_pct', '>=', 80)]\n).to_pandas()\nlistens = listens[listens.item_id.isin(item_id_to_idx) & listens.uid.isin(targets)].copy()\nlistens['weight'] = 1.0\n\n# Likes in last 60 days\nlikes = pd.read_parquet(data_dir / 'train_likes.parquet')\nlikes = likes[likes.item_id.isin(item_id_to_idx) & likes.uid.isin(targets) & (likes.timestamp >= cutoff - 60 * 86400) & (likes.timestamp < cutoff)].copy()\nlikes['weight'] = 5.0\n\ncomb = pd.concat([listens[['uid', 'item_id', 'weight']], likes[['uid', 'item_id', 'weight']]])\ncomb['item_idx'] = comb.item_id.map(item_id_to_idx)\ncomb['row'] = comb.uid.map(uid_to_row)\n\nagg = comb.groupby(['row', 'item_idx'])['weight'].sum().reset_index()\nX = sparse.csr_matrix((agg['weight'].to_numpy(dtype=np.float32), (agg['row'].to_numpy(dtype=int), agg['item_idx'].to_numpy(dtype=int))), shape=(len(uids), 30000))\nC = X.T @ X\nC.setdiag(0)\n\n# Factorize C: C ~ V @ V.T\nprint('Computing TruncatedSVD of C for d=128...')\nsvd = TruncatedSVD(n_components=128, algorithm='randomized', random_state=42)\n# Since C is symmetric, svd.fit_transform gives U * sqrt(S)\nV = svd.fit_transform(C)\n# Scale V by sqrt of singular values so V @ V.T ~ C\nV = V * np.sqrt(svd.singular_values_) / (np.linalg.norm(V, axis=1, keepdims=True) + 1e-6)\n\nprint('V shape:', V.shape)\n\n# User vector: u = X @ V\nU = X @ V # (8456, 128)\n\npacked = np.load(data_dir / 'validation_eligible_candidates.npz')['packed_eligible']\ncat_items = np.load(data_dir / 'validation_eligible_candidates.npz')['item_ids']\nbits = np.unpackbits(packed, axis=1, count=30000, bitorder='little').astype(bool)\npop = np.log1p(catalog.organic_positive_listens.to_numpy(dtype=np.float32))\n\n# Scores: U @ V.T\nscores = U @ V.T\n# Normalize rows of scores\nscores = scores / (np.max(scores, axis=1, keepdims=True) + 1e-6)\nscores = scores + 0.03 * (pop / pop.max())\nscores[~bits] = -1e9\n\nranked = {}\nfor r, uid in enumerate(uids):\n top = np.argpartition(scores[r], -100)[-100:]\n top = top[np.argsort(-scores[r, top])]\n ranked[int(uid)] = list(map(int, cat_items[top]))\n\nprint('Low-rank Two-Tower score:', score_rankings(ranked, truth))\n\"", "description": "Test low-rank two-tower factorization" } ### Tool output · 2026-09-29T06:38:51.353Z Output: Computing TruncatedSVD of C for d=128... V shape: (30000, 128) Low-rank Two-Tower score: {'quality_ndcg@10': 0.014007, 'quality_users': 4263} Process Group PGID: 3201 ## Step 82 · agent · 2026-09-29T06:39:00.766Z ### Recorded reasoning summary Exploring SVD Anomalies I'm encountering a significant drop in performance with TruncatedSVD on $C$, yielding only 0.014. My current hypothesis is that this is due to raw co-occurrence counts being heavily influenced by dominant "head" items, which overshadows the subtler relationships in the "mid-tail" item neighborhoods. ### Recorded reasoning summary Adapting Matrix Factorization My previous SVD struggles on raw co-occurrence data stemmed from spectrum skew. I'm now investigating how the normalization inherent in neural network approaches like Word2Vec, via cross-entropy and softmax, overcomes this. My current focus is replicating this normalization with a PyTorch Two-Tower model, exploring how user and item tower embeddings interact and their dimensionality's impact on performance. ### run_shell_command · 2026-09-29T06:39:00.766Z { "command": "python3 -c \"\nimport torch\nimport time\n\ntorch.set_num_threads(8)\nU = torch.randn(8456, 256)\nV = torch.randn(30000, 256)\n\nstart = time.time()\nscores = torch.matmul(U, V.t())\ntopk = torch.topk(scores, 100, dim=1)\nprint(f'd=256 scoring took {time.time() - start:.3f}s!')\n\"", "description": "Benchmark d=256 MIPS latency" } ### Tool output · 2026-09-29T06:39:04.564Z Output: d=256 scoring took 0.648s! Process Group PGID: 3236 ## Step 83 · agent · 2026-09-29T06:39:46.019Z ### Recorded reasoning summary Testing direct prediction I'm analyzing the training data for the validation ranker to see if the two towers can directly predict the discovery objective. My focus is on the cutoff value used in `train_*`. ### Recorded reasoning summary Evaluating temporal dynamics I'm examining how different historical windows influence prediction accuracy. Specifically, I'm curious if interactions within the last 60 days, especially strong organic listens and likes, are the key drivers behind the observed 0.0334 score. ### Recorded reasoning summary Clarifying model components I'm currently dissecting how the User Tower processes recent listens and likes, especially the weighting and pooling mechanisms. My goal is to understand the interplay between these components and their impact on the user representation. ### Recorded reasoning summary Integrating residual connections I'm exploring a novel architecture where the user vector directly incorporates pooled item embeddings via a residual connection. This seems promising, as it could preserve linear similarity signals while allowing an MLP to capture complex, non-linear preferences. I'm now considering the optimal structure for the Item Tower. ### Recorded reasoning summary Refining Item Tower I'm now focusing on the Item Tower's structure. It will take item embeddings and catalog features, process them through an MLP with a residual connection, and then append a popularity coordinate. I'm coding a PyTorch script to test this architecture and its ability to achieve high NDCG@10 on the validation set. I'll then investigate training objectives for the embeddings. ### Recorded reasoning summary Exploring Item Embeddings I'm investigating training item embeddings using item-item co-occurrence data. The goal is to see if I can approximate the normalized co-occurrence kernel or raw counts directly. I'm also considering matrix factorization techniques like ALS, but keeping production constraints in mind regarding user-item matrix construction. ### Recorded reasoning summary Investigating Training Discrepancies I'm deeply analyzing why the co-occurrence experiment yielded a 0.0334 score while `train_seq_two_tower.py` produced 0.006. It seems the difference lies in how user-specific item sets were constructed and combined with the $C = X^T X$ matrix, hinting at a misunderstanding of the underlying collaborative filtering mechanisms. ### run_shell_command · 2026-09-29T06:39:46.019Z { "description": "Test log-transformed SVD on item co-occurrence", "command": "PYTHONPATH=/task python3 -c \"\nimport pandas as pd\nimport numpy as np\nimport pyarrow.parquet as pq\nfrom scipy import sparse\nfrom sklearn.decomposition import TruncatedSVD\nfrom pathlib import Path\nfrom ranking_evaluation import CandidateSet, score_rankings\nfrom evaluate_public import validation_truth\n\ndata_dir = Path('/task/data')\ntargets = set(pd.read_parquet(data_dir / 'target_users.parquet').uid.astype(int))\nelig = CandidateSet(data_dir / 'validation_eligible_candidates.npz')\ntruth = validation_truth(elig, targets)\ncatalog = pd.read_parquet(data_dir / 'candidate_catalog.parquet')\nitem_id_to_idx = {id_: idx for idx, id_ in enumerate(catalog.item_id)}\nuids = np.load(data_dir / 'validation_eligible_candidates.npz')['uids']\nuid_to_row = {uid: r for r, uid in enumerate(uids)}\n\ncutoff = 24786800\n\nlistens = pq.read_table(\n data_dir / 'train_listens.parquet',\n columns=['uid', 'item_id', 'timestamp'],\n filters=[('timestamp', '>=', cutoff - 7 * 86400), ('timestamp', '<', cutoff), ('is_organic', '==', 1), ('played_ratio_pct', '>=', 80)]\n).to_pandas()\nlistens = listens[listens.item_id.isin(item_id_to_idx) & listens.uid.isin(targets)].copy()\nlistens['weight'] = 1.0\n\nlikes = pd.read_parquet(data_dir / 'train_likes.parquet')\nlikes = likes[likes.item_id.isin(item_id_to_idx) & likes.uid.isin(targets) & (likes.timestamp >= cutoff - 60 * 86400) & (likes.timestamp < cutoff)].copy()\nlikes['weight'] = 5.0\n\ncomb = pd.concat([listens[['uid', 'item_id', 'weight']], likes[['uid', 'item_id', 'weight']]])\ncomb['item_idx'] = comb.item_id.map(item_id_to_idx)\ncomb['row'] = comb.uid.map(uid_to_row)\n\nagg = comb.groupby(['row', 'item_idx'])['weight'].sum().reset_index()\nX = sparse.csr_matrix((agg['weight'].to_numpy(dtype=np.float32), (agg['row'].to_numpy(dtype=int), agg['item_idx'].to_numpy(dtype=int))), shape=(len(uids), 30000))\nC = X.T @ X\nC.setdiag(0)\n\n# Check PPMI or log1p of C\nC_log = C.copy()\nC_log.data = np.log1p(C_log.data)\n\n# SVD on C_log:\nfor d in [128, 256, 512]:\n svd = TruncatedSVD(n_components=d, algorithm='randomized', random_state=42)\n # fit on C_log\n V = svd.fit_transform(C_log)\n # User vector: U = X @ V\n U = X @ V\n # Normalize\n scores = U @ V.T\n scores = scores / (np.max(scores, axis=1, keepdims=True) + 1e-6)\n \n packed = np.load(data_dir / 'validation_eligible_candidates.npz')['packed_eligible']\n cat_items = np.load(data_dir / 'validation_eligible_candidates.npz')['item_ids']\n bits = np.unpackbits(packed, axis=1, count=30000, bitorder='little').astype(bool)\n pop = np.log1p(catalog.organic_positive_listens.to_numpy(dtype=np.float32))\n \n score_val = scores + 0.03 * (pop / pop.max())\n score_val[~bits] = -1e9\n \n ranked = {}\n for r, uid in enumerate(uids):\n top = np.argpartition(score_val[r], -100)[-100:]\n top = top[np.argsort(-score_val[r, top])]\n ranked[int(uid)] = list(map(int, cat_items[top]))\n print(f'd={d}: {score_rankings(ranked, truth)}')\n\"" } ### Tool output · 2026-09-29T06:41:23.054Z Output: d=128: {'quality_ndcg@10': 0.017189, 'quality_users': 4263} d=256: {'quality_ndcg@10': 0.017703, 'quality_users': 4263} d=512: {'quality_ndcg@10': 0.017545, 'quality_users': 4263} Process Group PGID: 3260 ## Step 84 · agent · 2026-09-29T06:41:40.200Z ### Recorded reasoning summary Exploring Discrepancies I'm observing a significant difference: SVD yields 0.0177, while the full sparse matrix XC results in 0.0334. This divergence appears to stem from XC representing a sparse graph walk, specifically a 2-hop walk. In music recommendation, such graph walk methodologies, akin to personalized PageRank and local neighborhood propagation, tend to exhibit localized connectivity. ### Recorded reasoning summary Investigating Local Preservation I'm focused on how neural networks, specifically in a two-tower setup with non-linear activations and attention, might retain local graph structure. This is crucial to understand if they can overcome the smearing effect I've observed when projecting sparse graph data into dense Euclidean spaces via SVD. ### Recorded reasoning summary Deconstructing Vectors I'm realizing the item vector might simply be a normalized column from a matrix $C$. Crucially, the user tower must generate its vector from interaction history, not an ID lookup. I'm now considering if this framework, with a high dimensionality like 30,000, can truly avoid SVD's localization issues. ### Recorded reasoning summary Analyzing Dot Product Equivalence I'm confirming that the dot product between a user vector and an item vector is precisely the element-wise calculation found in the matrix product $XC$. This holds true even with user vectors being derived from interaction history via an MLP, provided the dimensionality is consistent with the item embedding matrix. ### Recorded reasoning summary Refining Embeddings I'm understanding that production systems require much lower embedding dimensions, around 32-256, for efficient nearest-neighbor search. This implies my previous thought about 30,000 dimensions needs significant adjustment to align with real-world constraints and performance. ### Recorded reasoning summary Developing PyTorch Models I'm now focused on implementing the trainable user and item towers using PyTorch. My immediate goal is to achieve high NDCG scores within the specified dense embedding dimensions. I'm analyzing the training loss and parameters used previously to understand why scores plateaued and how to improve them. ### Recorded reasoning summary Adjusting Training Dynamics I'm identifying that training sequences excluded "likes," and targets heavily favored "listens." This imbalance, coupled with cross-entropy loss not being logQ-corrected for in-batch negatives, inadvertently penalized popular items, driving down the popularity weight and impacting NDCG. ### run_shell_command · 2026-09-29T06:41:40.200Z { "description": "Check LogQ range", "command": "python3 -c \"\nimport pandas as pd\nimport numpy as np\nimport pyarrow.parquet as pq\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nimport math\nimport time\nfrom pathlib import Path\nfrom ranking_evaluation import CandidateSet, score_rankings\nfrom evaluate_public import validation_truth\n\ndata_dir = Path('/task/data')\ntargets = set(pd.read_parquet(data_dir / 'target_users.parquet').uid.astype(int))\nelig = CandidateSet(data_dir / 'validation_eligible_candidates.npz')\ntruth = validation_truth(elig, targets)\ncatalog = pd.read_parquet(data_dir / 'candidate_catalog.parquet')\nitem_ids = catalog.item_id.to_numpy()\nitem_id_to_idx = {int(id_): idx for idx, id_ in enumerate(item_ids, 1)}\nuids = np.load(data_dir / 'validation_eligible_candidates.npz')['uids']\nuid_to_idx = {int(uid): idx for idx, uid in enumerate(uids)}\nN_users = len(uids)\nnum_items = len(item_ids) + 1\n\n# Popularity for logQ\ncat_org = catalog.organic_positive_listens.to_numpy(dtype=np.float32)\npop_probs = (cat_org + 10.0) / (cat_org + 10.0).sum()\nlog_q = torch.from_numpy(np.r_[0.0, np.log(pop_probs)]) # (30001,)\n\nprint('LogQ range:', log_q[1:].min().item(), log_q[1:].max().item())\n\"" } ### Tool output · 2026-09-29T06:41:44.164Z Output: Traceback (most recent call last): File "", line 11, in ModuleNotFoundError: No module named 'ranking_evaluation' Exit Code: 1 Process Group PGID: 3301 ## Step 85 · agent · 2026-09-29T06:41:48.515Z ### run_shell_command · 2026-09-29T06:41:48.515Z { "description": "Check LogQ range with PYTHONPATH", "command": "PYTHONPATH=/task python3 -c \"\nimport pandas as pd\nimport numpy as np\nimport pyarrow.parquet as pq\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nimport math\nimport time\nfrom pathlib import Path\nfrom ranking_evaluation import CandidateSet, score_rankings\nfrom evaluate_public import validation_truth\n\ndata_dir = Path('/task/data')\ncatalog = pd.read_parquet(data_dir / 'candidate_catalog.parquet')\nitem_ids = catalog.item_id.to_numpy()\n\ncat_org = catalog.organic_positive_listens.to_numpy(dtype=np.float32)\npop_probs = (cat_org + 10.0) / (cat_org + 10.0).sum()\nlog_q = torch.from_numpy(np.r_[0.0, np.log(pop_probs)]) # (30001,)\n\nprint('LogQ range:', log_q[1:].min().item(), log_q[1:].max().item())\n\"" } ### Tool output · 2026-09-29T06:41:52.565Z Output: LogQ range: -13.786884307861328 -6.154967308044434 Process Group PGID: 3312 ## Step 86 · agent · 2026-09-29T06:42:27.403Z ### Recorded reasoning summary Designing PyTorch Network My current focus is on constructing a robust Two-Tower Neural Network in PyTorch. I'm delving into the specifics of the User Tower, particularly how to effectively aggregate user interaction history to inform its representation. ### Recorded reasoning summary Refining User Representation I'm now refining the User Tower by integrating recent listens and likes. I'm employing weighted averages of item embeddings for both, feeding these into an MLP. Crucially, I've added residual connections to preserve semantic directions from interaction history. ### Recorded reasoning summary Processing Item Tower I am now implementing the Item Tower, incorporating base embeddings with catalog features. This data will pass through an item MLP, followed by a residual connection and normalization to generate latent item representations. **Calculating Relevance Score** I'm focused on deriving the final relevance score. This involves constructing both user and item vectors of dimension $d+1$, including scalar terms $\alpha$ and $\alpha \cdot p_j$, respectively, and then computing their dot product. ### write_file · 2026-09-29T06:42:27.403Z { "file_path": "/app/solution/train_two_tower_v2.py", "content": "#!/usr/bin/env python3\n\"\"\"Two-Tower Neural Network Ranker with Residual Connections and LogQ Correction.\"\"\"\nfrom __future__ import annotations\n\nimport math\nimport time\nfrom pathlib import Path\n\nimport numpy as np\nimport pandas as pd\nimport pyarrow.parquet as pq\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\nfrom ranking_evaluation import CandidateSet, score_rankings\nfrom evaluate_public import validation_truth\n\nDATA_DIR = Path('/task/data')\nSOLUTION_DIR = Path('/app/solution')\n\n\ndef get_catalog_features():\n cat = pd.read_parquet(DATA_DIR / 'candidate_catalog.parquet')\n item_ids = cat.item_id.to_numpy()\n item_id_to_idx = {int(id_): idx for idx, id_ in enumerate(item_ids, 1)}\n \n pos = np.log1p(cat.positive_listens.to_numpy(dtype=np.float32))\n org = np.log1p(cat.organic_positive_listens.to_numpy(dtype=np.float32))\n pop = np.log1p(cat.popularity_rank.to_numpy(dtype=np.float32))\n ratio = (cat.organic_positive_listens / (cat.positive_listens + 1.0)).to_numpy(dtype=np.float32)\n \n norm_pop = (org - org.min()) / (org.max() - org.min() + 1e-6)\n \n feats = np.stack([\n (pos - pos.mean()) / (pos.std() + 1e-6),\n (org - org.mean()) / (org.std() + 1e-6),\n (pop - pop.mean()) / (pop.std() + 1e-6),\n (ratio - ratio.mean()) / (ratio.std() + 1e-6),\n ], axis=1)\n \n feat_tensor = np.zeros((len(item_ids) + 1, 4), dtype=np.float32)\n feat_tensor[1:] = feats\n \n pop_tensor = np.zeros(len(item_ids) + 1, dtype=np.float32)\n pop_tensor[1:] = norm_pop\n \n # LogQ for in-batch negative sampling correction\n pop_probs = (cat.organic_positive_listens.to_numpy(dtype=np.float32) + 10.0)\n pop_probs = pop_probs / pop_probs.sum()\n logq_tensor = np.zeros(len(item_ids) + 1, dtype=np.float32)\n logq_tensor[1:] = np.log(pop_probs)\n \n return item_ids, item_id_to_idx, torch.from_numpy(feat_tensor), torch.from_numpy(pop_tensor), torch.from_numpy(logq_tensor)\n\n\ndef extract_user_history(cutoff_ts: int, item_id_to_idx: dict[int, int], uid_to_idx: dict[int, int], N_users: int, is_val: bool = False):\n \"\"\"Extract user history tensors at cutoff_ts.\"\"\"\n K_like = 128\n K_listen = 128\n \n likes_items = np.zeros((N_users, K_like), dtype=np.int64)\n likes_weights = np.zeros((N_users, K_like), dtype=np.float32)\n listens_items = np.zeros((N_users, K_listen), dtype=np.int64)\n listens_weights = np.zeros((N_users, K_listen), dtype=np.float32)\n stats = np.zeros((N_users, 2), dtype=np.float32)\n \n # 1. Likes in last 60 days before cutoff\n train_likes = pd.read_parquet(DATA_DIR / 'train_likes.parquet')\n l_df = train_likes[(train_likes.timestamp < cutoff_ts) & (train_likes.timestamp >= cutoff_ts - 60 * 86400) & (train_likes.uid.isin(uid_to_idx))].copy()\n if is_val:\n # Also include active likes\n pref = pd.read_parquet(DATA_DIR / 'validation_preference_state.parquet')\n liked_pref = pref[(pref.state == 'liked') & (pref.uid.isin(uid_to_idx))].copy()\n liked_pref['timestamp'] = cutoff_ts - 1\n l_df = pd.concat([l_df[['uid', 'item_id', 'timestamp']], liked_pref[['uid', 'item_id', 'timestamp']]])\n \n l_df['uid_idx'] = l_df.uid.map(uid_to_idx)\n l_df['item_idx'] = l_df.item_id.map(item_id_to_idx)\n l_df = l_df.dropna(subset=['uid_idx', 'item_idx']).sort_values('timestamp')\n l_df['uid_idx'] = l_df.uid_idx.astype(np.int64)\n l_df['item_idx'] = l_df.item_idx.astype(np.int64)\n \n for u, g in l_df.groupby('uid_idx'):\n itms = g.item_idx.to_numpy()\n ts = g.timestamp.to_numpy(dtype=np.float64)\n diff = np.maximum(0.0, float(cutoff_ts) - ts)\n w = (1.0 + np.exp(-diff / (14.0 * 86400.0))).astype(np.float32)\n k_take = min(len(itms), K_like)\n likes_items[u, :k_take] = itms[-k_take:]\n likes_weights[u, :k_take] = w[-k_take:]\n stats[u, 0] = np.log1p(len(itms))\n\n # 2. Strong organic listens in last 7 days\n listens_table = pq.read_table(\n DATA_DIR / 'train_listens.parquet',\n columns=['uid', 'item_id', 'timestamp', 'played_ratio_pct', 'is_organic'],\n filters=[('timestamp', '>=', cutoff_ts - 7 * 86400), ('timestamp', '<', cutoff_ts)]\n )\n s_df = listens_table.to_pandas()\n s_df = s_df[(s_df.is_organic == 1) & (s_df.played_ratio_pct >= 80) & (s_df.uid.isin(uid_to_idx))].copy()\n s_df['uid_idx'] = s_df.uid.map(uid_to_idx)\n s_df['item_idx'] = s_df.item_id.map(item_id_to_idx)\n s_df = s_df.dropna(subset=['uid_idx', 'item_idx']).sort_values('timestamp')\n s_df['uid_idx'] = s_df.uid_idx.astype(np.int64)\n s_df['item_idx'] = s_df.item_idx.astype(np.int64)\n\n for u, g in s_df.groupby('uid_idx'):\n itms = g.item_idx.to_numpy()\n ts = g.timestamp.to_numpy(dtype=np.float64)\n diff = np.maximum(0.0, float(cutoff_ts) - ts)\n w = np.exp(-diff / (7.0 * 86400.0)).astype(np.float32)\n k_take = min(len(itms), K_listen)\n listens_items[u, :k_take] = itms[-k_take:]\n listens_weights[u, :k_take] = w[-k_take:]\n stats[u, 1] = np.log1p(len(itms))\n \n return {\n 'likes': torch.from_numpy(likes_items),\n 'likes_w': torch.from_numpy(likes_weights),\n 'listens': torch.from_numpy(listens_items),\n 'listens_w': torch.from_numpy(listens_weights),\n 'stats': torch.from_numpy(stats),\n }\n\n\ndef build_future_targets(start_ts: int, end_ts: int, uid_to_idx: dict[int, int], item_id_to_idx: dict[int, int]):\n \"\"\"Collect organic discovery targets in [start_ts, end_ts].\"\"\"\n likes_table = pq.read_table(\n DATA_DIR / 'train_likes.parquet',\n columns=['uid', 'item_id', 'timestamp', 'is_organic'],\n filters=[('timestamp', '>=', start_ts), ('timestamp', '<', end_ts)]\n )\n likes_df = likes_table.to_pandas()\n likes_df = likes_df[(likes_df.is_organic == 1) & (likes_df.uid.isin(uid_to_idx))].copy()\n likes_df['uid_idx'] = likes_df.uid.map(uid_to_idx)\n likes_df['item_idx'] = likes_df.item_id.map(item_id_to_idx)\n likes_df = likes_df.dropna(subset=['item_idx'])\n likes_df['weight'] = 7.0\n \n listens_table = pq.read_table(\n DATA_DIR / 'train_listens.parquet',\n columns=['uid', 'item_id', 'timestamp', 'played_ratio_pct', 'is_organic'],\n filters=[('timestamp', '>=', start_ts), ('timestamp', '<', end_ts)]\n )\n listens_df = listens_table.to_pandas()\n strong = listens_df[(listens_df.is_organic == 1) & (listens_df.played_ratio_pct >= 80) & (listens_df.uid.isin(uid_to_idx))].copy()\n strong['uid_idx'] = strong.uid.map(uid_to_idx)\n strong['item_idx'] = strong.item_id.map(item_id_to_idx)\n strong = strong.dropna(subset=['item_idx'])\n strong['weight'] = 1.0\n \n comb = pd.concat([likes_df[['uid_idx', 'item_idx', 'weight']], strong[['uid_idx', 'item_idx', 'weight']]])\n comb = comb.groupby(['uid_idx', 'item_idx'], as_index=False)['weight'].max()\n \n return comb.uid_idx.to_numpy(dtype=np.int64), comb.item_idx.to_numpy(dtype=np.int64), comb.weight.to_numpy(dtype=np.float32)\n\n\nclass UserTower(nn.Module):\n def __init__(self, item_embeddings: nn.Embedding, d_in: int, d_hidden: int, d_out: int):\n super().__init__()\n self.item_emb = item_embeddings\n self.gamma_likes = nn.Parameter(torch.tensor(3.0, dtype=torch.float32))\n self.gamma_listens = nn.Parameter(torch.tensor(1.0, dtype=torch.float32))\n self.stats_proj = nn.Linear(2, 16)\n \n in_dim = 2 * d_in + 16\n self.mlp = nn.Sequential(\n nn.Linear(in_dim, d_hidden),\n nn.LayerNorm(d_hidden),\n nn.GELU(),\n nn.Dropout(0.1),\n nn.Linear(d_hidden, d_out),\n )\n self.proj_res = nn.Linear(d_in, d_out)\n self.pop_weight = nn.Parameter(torch.tensor(0.20, dtype=torch.float32))\n\n def forward(self, likes, likes_w, listens, listens_w, stats):\n # 1. Pool likes\n mask_l = ((likes > 0).float() * likes_w).unsqueeze(-1)\n emb_l = self.item_emb(likes) * mask_l\n h_l = emb_l.sum(dim=1) / (mask_l.sum(dim=1) + 1e-6)\n\n # 2. Pool listens\n mask_s = ((listens > 0).float() * listens_w).unsqueeze(-1)\n emb_s = self.item_emb(listens) * mask_s\n h_s = emb_s.sum(dim=1) / (mask_s.sum(dim=1) + 1e-6)\n\n h_stats = F.gelu(self.stats_proj(stats))\n feat = torch.cat([h_l, h_s, h_stats], dim=-1)\n u_nonlin = self.mlp(feat)\n \n # Residual linear connection from pooled history\n h_comb = self.gamma_likes * h_l + self.gamma_listens * h_s\n u_res = self.proj_res(h_comb)\n \n u_base = u_nonlin + u_res\n u_norm = F.normalize(u_base, p=2, dim=-1)\n \n beta = self.pop_weight.clamp(0.01, 0.8)\n scale = torch.sqrt(1.0 - beta**2)\n u_final = torch.cat([u_norm * scale, beta.expand(len(u_norm), 1)], dim=-1)\n return u_final\n\n\nclass ItemTower(nn.Module):\n def __init__(self, item_embeddings: nn.Embedding, catalog_feats: torch.Tensor, pop_scores: torch.Tensor, d_in: int, d_hidden: int, d_out: int):\n super().__init__()\n self.item_emb = item_embeddings\n self.register_buffer('catalog_feats', catalog_feats)\n self.register_buffer('pop_scores', pop_scores)\n self.feat_proj = nn.Linear(4, 16)\n self.mlp = nn.Sequential(\n nn.Linear(d_in + 16, d_hidden),\n nn.LayerNorm(d_hidden),\n nn.GELU(),\n nn.Linear(d_hidden, d_out),\n )\n self.proj_res = nn.Linear(d_in, d_out)\n\n def forward(self, item_ids, pop_weight_param: torch.Tensor):\n emb = self.item_emb(item_ids)\n feats = self.catalog_feats[item_ids]\n h_feats = F.gelu(self.feat_proj(feats))\n x = torch.cat([emb, h_feats], dim=-1)\n v_nonlin = self.mlp(x)\n v_res = self.proj_res(emb)\n \n v_base = v_nonlin + v_res\n v_norm = F.normalize(v_base, p=2, dim=-1)\n \n beta = pop_weight_param.clamp(0.01, 0.8)\n scale = torch.sqrt(1.0 - beta**2)\n pop_val = self.pop_scores[item_ids].unsqueeze(-1)\n v_final = torch.cat([v_norm * scale, beta * pop_val], dim=-1)\n return v_final\n\n\ndef run():\n print(\"Loading catalog and users...\")\n item_ids, item_id_to_idx, catalog_feats, pop_scores, log_q = get_catalog_features()\n targets_df = pd.read_parquet(DATA_DIR / 'target_users.parquet')\n uids = targets_df.uid.to_numpy()\n uid_to_idx = {int(uid): idx for idx, uid in enumerate(uids)}\n N_users = len(uids)\n num_items = len(item_ids) + 1 # 30001\n \n cutoff_val = 24786800\n t_7d = cutoff_val - 7 * 86400\n t_14d = cutoff_val - 14 * 86400\n \n print(\"Extracting validation user histories...\")\n val_hist = extract_user_history(cutoff_val, item_id_to_idx, uid_to_idx, N_users, is_val=True)\n \n print(\"Extracting training windows...\")\n hist_w1 = extract_user_history(t_7d, item_id_to_idx, uid_to_idx, N_users, is_val=False)\n u_w1, itm_w1, weight_w1 = build_future_targets(t_7d, cutoff_val, uid_to_idx, item_id_to_idx)\n print(f\"Window 1 targets: {len(u_w1)}\")\n\n hist_w2 = extract_user_history(t_14d, item_id_to_idx, uid_to_idx, N_users, is_val=False)\n u_w2, itm_w2, weight_w2 = build_future_targets(t_14d, t_7d, uid_to_idx, item_id_to_idx)\n print(f\"Window 2 targets: {len(u_w2)}\")\n \n d_emb = 128\n d_hidden = 128\n d_out = 127 # + 1 for pop = 128\n \n shared_emb = nn.Embedding(num_items, d_emb, padding_idx=0)\n nn.init.normal_(shared_emb.weight, std=0.05)\n with torch.no_grad():\n shared_emb.weight[0].fill_(0.0)\n \n user_tower = UserTower(shared_emb, d_emb, d_hidden, d_out)\n item_tower = ItemTower(shared_emb, catalog_feats, pop_scores, d_emb, d_hidden, d_out)\n \n logit_scale = nn.Parameter(torch.tensor(math.log(1.0 / 0.05), dtype=torch.float32))\n \n # We pass user_tower parameters and item_tower specific modules\n item_params = list(item_tower.feat_proj.parameters()) + list(item_tower.mlp.parameters()) + list(item_tower.proj_res.parameters())\n params = list(user_tower.parameters()) + item_params + [logit_scale]\n optimizer = torch.optim.AdamW(params, lr=1e-3, weight_decay=1e-4)\n\n # Validation ground truth\n val_elig = CandidateSet(DATA_DIR / 'validation_eligible_candidates.npz')\n graded_val = validation_truth(val_elig, set(uids))\n val_packed = np.load(DATA_DIR / 'validation_eligible_candidates.npz')['packed_eligible']\n val_catalog_items = np.load(DATA_DIR / 'validation_eligible_candidates.npz')['item_ids']\n bits = np.unpackbits(val_packed, axis=1, count=30000, bitorder='little').astype(bool)\n\n def evaluate():\n user_tower.eval()\n item_tower.eval()\n with torch.no_grad():\n all_cat_ids = torch.arange(1, num_items, dtype=torch.int64)\n V = item_tower(all_cat_ids, user_tower.pop_weight)\n \n user_vecs = []\n bs = 1024\n for s in range(0, N_users, bs):\n e = min(s + bs, N_users)\n ub = user_tower(\n val_hist['likes'][s:e],\n val_hist['likes_w'][s:e],\n val_hist['listens'][s:e],\n val_hist['listens_w'][s:e],\n val_hist['stats'][s:e],\n )\n user_vecs.append(ub)\n U = torch.cat(user_vecs, dim=0)\n \n scores = torch.matmul(U, V.t()).numpy()\n scores[~bits] = -1e9\n \n ranked = {}\n for r, uid in enumerate(uids):\n top_cols = np.argpartition(scores[r], -100)[-100:]\n top_cols = top_cols[np.argsort(-scores[r, top_cols])]\n ranked[int(uid)] = list(map(int, val_catalog_items[top_cols]))\n return score_rankings(ranked, graded_val)\n\n print(\"\\nInitial zero-shot evaluation...\")\n print(evaluate())\n \n # Combine training data\n win_flag = np.concatenate([np.zeros(len(u_w1), dtype=np.int32), np.ones(len(u_w2), dtype=np.int32)])\n all_u = np.concatenate([u_w1, u_w2])\n all_itm = np.concatenate([itm_w1, itm_w2])\n all_w = np.concatenate([weight_w1, weight_w2])\n N_samples = len(all_u)\n print(f\"Total training samples: {N_samples}\")\n \n batch_size = 512\n epochs = 6\n \n for epoch in range(1, epochs + 1):\n t0 = time.time()\n user_tower.train()\n item_tower.train()\n \n perm = np.random.permutation(N_samples)\n total_loss = 0.0\n n_b = 0\n \n for b_start in range(0, N_samples, batch_size):\n b_end = min(b_start + batch_size, N_samples)\n if b_end - b_start < 64:\n continue\n idx = perm[b_start:b_end]\n \n b_u = all_u[idx]\n b_itm = torch.from_numpy(all_itm[idx])\n b_w = torch.from_numpy(all_w[idx])\n b_win = win_flag[idx]\n \n mask1 = (b_win == 0)\n mask2 = (b_win == 1)\n \n u_vecs = torch.zeros((len(idx), d_out + 1), dtype=torch.float32)\n if np.any(mask1):\n idx1 = np.where(mask1)[0]\n u1 = b_u[idx1]\n u_vecs[idx1] = user_tower(\n hist_w1['likes'][u1],\n hist_w1['likes_w'][u1],\n hist_w1['listens'][u1],\n hist_w1['listens_w'][u1],\n hist_w1['stats'][u1],\n )\n if np.any(mask2):\n idx2 = np.where(mask2)[0]\n u2 = b_u[idx2]\n u_vecs[idx2] = user_tower(\n hist_w2['likes'][u2],\n hist_w2['likes_w'][u2],\n hist_w2['listens'][u2],\n hist_w2['listens_w'][u2],\n hist_w2['stats'][u2],\n )\n \n v_vecs = item_tower(b_itm, user_tower.pop_weight)\n scale = logit_scale.exp().clamp(max=100.0)\n \n # In-batch logits: (B, B)\n logits = torch.matmul(u_vecs, v_vecs.t()) * scale\n \n # Apply LogQ correction on negative columns\n # LogQ: log_q is of shape (30001,)\n b_logq = log_q[b_itm].unsqueeze(0) # (1, B)\n logits = logits - b_logq\n \n targets = torch.arange(len(idx), dtype=torch.int64)\n ce = F.cross_entropy(logits, targets, reduction='none')\n loss = (ce * b_w).mean()\n \n optimizer.zero_grad()\n loss.backward()\n torch.nn.utils.clip_grad_norm_(params, max_norm=1.0)\n optimizer.step()\n \n total_loss += loss.item()\n n_b += 1\n \n dur = time.time() - t0\n print(f\"Epoch {epoch:02d} | Time: {dur:.1f}s | Loss: {total_loss/n_b:.4f} | PopWeight: {user_tower.pop_weight.item():.4f}\")\n val_res = evaluate()\n print(f\"Validation Score: {val_res}\")\n\n\nif __name__ == '__main__':\n run()\n" } ### Tool output · 2026-09-29T06:42:27.416Z Successfully created and wrote to new file: /app/solution/train_two_tower_v2.py. Here is the updated code: #!/usr/bin/env python3 """Two-Tower Neural Network Ranker with Residual Connections and LogQ Correction.""" from __future__ import annotations import math import time from pathlib import Path import numpy as np import pandas as pd import pyarrow.parquet as pq import torch import torch.nn as nn import torch.nn.functional as F from ranking_evaluation import CandidateSet, score_rankings from evaluate_public import validation_truth DATA_DIR = Path('/task/data') SOLUTION_DIR = Path('/app/solution') def get_catalog_features(): cat = pd.read_parquet(DATA_DIR / 'candidate_catalog.parquet') item_ids = cat.item_id.to_numpy() item_id_to_idx = {int(id_): idx for idx, id_ in enumerate(item_ids, 1)} pos = np.log1p(cat.positive_listens.to_numpy(dtype=np.float32)) org = np.log1p(cat.organic_positive_listens.to_numpy(dtype=np.float32)) pop = np.log1p(cat.popularity_rank.to_numpy(dtype=np.float32)) ratio = (cat.organic_positive_listens / (cat.positive_listens + 1.0)).to_numpy(dtype=np.float32) norm_pop = (org - org.min()) / (org.max() - org.min() + 1e-6) feats = np.stack([ (pos - pos.mean()) / (pos.std() + 1e-6), (org - org.mean()) / (org.std() + 1e-6), (pop - pop.mean()) / (pop.std() + 1e-6), (ratio - ratio.mean()) / (ratio.std() + 1e-6), ], axis=1) feat_tensor = np.zeros((len(item_ids) + 1, 4), dtype=np.float32) feat_tensor[1:] = feats pop_tensor = np.zeros(len(item_ids) + 1, dtype=np.float32) pop_tensor[1:] = norm_pop # LogQ for in-batch negative sampling correction pop_probs = (cat.organic_positive_listens.to_numpy(dtype=np.float32) + 10.0) pop_probs = pop_probs / pop_probs.sum() logq_tensor = np.zeros(len(item_ids) + 1, dtype=np.float32) logq_tensor[1:] = np.log(pop_probs) return item_ids, item_id_to_idx, torch.from_numpy(feat_tensor), torch.from_numpy(pop_tensor), torch.from_numpy(logq_tensor) def extract_user_history(cutoff_ts: int, item_id_to_idx: dict[int, int], uid_to_idx: dict[int, int], N_users: int, is_val: bool = False): """Extract user history tensors at cutoff_ts.""" K_like = 128 K_listen = 128 likes_items = np.zeros((N_users, K_like), dtype=np.int64) likes_weights = np.zeros((N_users, K_like), dtype=np.float32) listens_items = np.zeros((N_users, K_listen), dtype=np.int64) listens_weights = np.zeros((N_users, K_listen), dtype=np.float32) stats = np.zeros((N_users, 2), dtype=np.float32) # 1. Likes in last 60 days before cutoff train_likes = pd.read_parquet(DATA_DIR / 'train_likes.parquet') l_df = train_likes[(train_likes.timestamp < cutoff_ts) & (train_likes.timestamp >= cutoff_ts - 60 * 86400) & (train_likes.uid.isin(uid_to_idx))].copy() if is_val: # Also include active likes pref = pd.read_parquet(DATA_DIR / 'validation_preference_state.parquet') liked_pref = pref[(pref.state == 'liked') & (pref.uid.isin(uid_to_idx))].copy() liked_pref['timestamp'] = cutoff_ts - 1 l_df = pd.concat([l_df[['uid', 'item_id', 'timestamp']], liked_pref[['uid', 'item_id', 'timestamp']]]) l_df['uid_idx'] = l_df.uid.map(uid_to_idx) l_df['item_idx'] = l_df.item_id.map(item_id_to_idx) l_df = l_df.dropna(subset=['uid_idx', 'item_idx']).sort_values('timestamp') l_df['uid_idx'] = l_df.uid_idx.astype(np.int64) l_df['item_idx'] = l_df.item_idx.astype(np.int64) for u, g in l_df.groupby('uid_idx'): itms = g.item_idx.to_numpy() ts = g.timestamp.to_numpy(dtype=np.float64) diff = np.maximum(0.0, float(cutoff_ts) - ts) w = (1.0 + np.exp(-diff / (14.0 * 86400.0))).astype(np.float32) k_take = min(len(itms), K_like) likes_items[u, :k_take] = itms[-k_take:] likes_weights[u, :k_take] = w[-k_take:] stats[u, 0] = np.log1p(len(itms)) # 2. Strong organic listens in last 7 days listens_table = pq.read_table( DATA_DIR / 'train_listens.parquet', columns=['uid', 'item_id', 'timestamp', 'played_ratio_pct', 'is_organic'], filters=[('timestamp', '>=', cutoff_ts - 7 * 86400), ('timestamp', '<', cutoff_ts)] ) s_df = listens_table.to_pandas() s_df = s_df[(s_df.is_organic == 1) & (s_df.played_ratio_pct >= 80) & (s_df.uid.isin(uid_to_idx))].copy() s_df['uid_idx'] = s_df.uid.map(uid_to_idx) s_df['item_idx'] = s_df.item_id.map(item_id_to_idx) s_df = s_df.dropna(subset=['uid_idx', 'item_idx']).sort_values('timestamp') s_df['uid_idx'] = s_df.uid_idx.astype(np.int64) s_df['item_idx'] = s_df.item_idx.astype(np.int64) for u, g in s_df.groupby('uid_idx'): itms = g.item_idx.to_numpy() ts = g.timestamp.to_numpy(dtype=np.float64) diff = np.maximum(0.0, float(cutoff_ts) - ts) w = np.exp(-diff / (7.0 * 86400.0)).astype(np.float32) k_take = min(len(itms), K_listen) listens_items[u, :k_take] = itms[-k_take:] listens_weights[u, :k_take] = w[-k_take:] stats[u, 1] = np.log1p(len(itms)) return { 'likes': torch.from_numpy(likes_items), 'likes_w': torch.from_numpy(likes_weights), 'listens': torch.from_numpy(listens_items), 'listens_w': torch.from_numpy(listens_weights), 'stats': torch.from_numpy(stats), } def build_future_targets(start_ts: int, end_ts: int, uid_to_idx: dict[int, int], item_id_to_idx: dict[int, int]): """Collect organic discovery targets in [start_ts, end_ts].""" likes_table = pq.read_table( DATA_DIR / 'train_likes.parquet', columns=['uid', 'item_id', 'timestamp', 'is_organic'], filters=[('timestamp', '>=', start_ts), ('timestamp', '<', end_ts)] ) likes_df = likes_table.to_pandas() likes_df = likes_df[(likes_df.is_organic == 1) & (likes_df.uid.isin(uid_to_idx))].copy() likes_df['uid_idx'] = likes_df.uid.map(uid_to_idx) likes_df['item_idx'] = likes_df.item_id.map(item_id_to_idx) likes_df = likes_df.dropna(subset=['item_idx']) likes_df['weight'] = 7.0 listens_table = pq.read_table( DATA_DIR / 'train_listens.parquet', columns=['uid', 'item_id', 'timestamp', 'played_ratio_pct', 'is_organic'], filters=[('timestamp', '>=', start_ts), ('timestamp', '<', end_ts)] ) listens_df = listens_table.to_pandas() strong = listens_df[(listens_df.is_organic == 1) & (listens_df.played_ratio_pct >= 80) & (listens_df.uid.isin(uid_to_idx))].copy() strong['uid_idx'] = strong.uid.map(uid_to_idx) strong['item_idx'] = strong.item_id.map(item_id_to_idx) strong = strong.dropna(subset=['item_idx']) strong['weight'] = 1.0 comb = pd.concat([likes_df[['uid_idx', 'item_idx', 'weight']], strong[['uid_idx', 'item_idx', 'weight']]]) comb = comb.groupby(['uid_idx', 'item_idx'], as_index=False)['weight'].max() return comb.uid_idx.to_numpy(dtype=np.int64), comb.item_idx.to_numpy(dtype=np.int64), comb.weight.to_numpy(dtype=np.float32) class UserTower(nn.Module): def __init__(self, item_embeddings: nn.Embedding, d_in: int, d_hidden: int, d_out: int): super().__init__() self.item_emb = item_embeddings self.gamma_likes = nn.Parameter(torch.tensor(3.0, dtype=torch.float32)) self.gamma_listens = nn.Parameter(torch.tensor(1.0, dtype=torch.float32)) self.stats_proj = nn.Linear(2, 16) in_dim = 2 * d_in + 16 self.mlp = nn.Sequential( nn.Linear(in_dim, d_hidden), nn.LayerNorm(d_hidden), nn.GELU(), nn.Dropout(0.1), nn.Linear(d_hidden, d_out), ) self.proj_res = nn.Linear(d_in, d_out) self.pop_weight = nn.Parameter(torch.tensor(0.20, dtype=torch.float32)) def forward(self, likes, likes_w, listens, listens_w, stats): # 1. Pool likes mask_l = ((likes > 0).float() * likes_w).unsqueeze(-1) emb_l = self.item_emb(likes) * mask_l h_l = emb_l.sum(dim=1) / (mask_l.sum(dim=1) + 1e-6) # 2. Pool listens mask_s = ((listens > 0).float() * listens_w).unsqueeze(-1) emb_s = self.item_emb(listens) * mask_s h_s = emb_s.sum(dim=1) / (mask_s.sum(dim=1) + 1e-6) h_stats = F.gelu(self.stats_proj(stats)) feat = torch.cat([h_l, h_s, h_stats], dim=-1) u_nonlin = self.mlp(feat) # Residual linear connection from pooled history h_comb = self.gamma_likes * h_l + self.gamma_listens * h_s u_res = self.proj_res(h_comb) u_base = u_nonlin + u_res u_norm = F.normalize(u_base, p=2, dim=-1) beta = self.pop_weight.clamp(0.01, 0.8) scale = torch.sqrt(1.0 - beta**2) u_final = torch.cat([u_norm * scale, beta.expand(len(u_norm), 1)], dim=-1) return u_final class ItemTower(nn.Module): def __init__(self, item_embeddings: nn.Embedding, catalog_feats: torch.Tensor, pop_scores: torch.Tensor, d_in: int, d_hidden: int, d_out: int): super().__init__() self.item_emb = item_embeddings self.register_buffer('catalog_feats', catalog_feats) self.register_buffer('pop_scores', pop_scores) self.feat_proj = nn.Linear(4, 16) self.mlp = nn.Sequential( nn.Linear(d_in + 16, d_hidden), nn.LayerNorm(d_hidden), nn.GELU(), nn.Linear(d_hidden, d_out), ) self.proj_res = nn.Linear(d_in, d_out) def forward(self, item_ids, pop_weight_param: torch.Tensor): emb = self.item_emb(item_ids) feats = self.catalog_feats[item_ids] h_feats = F.gelu(self.feat_proj(feats)) x = torch.cat([emb, h_feats], dim=-1) v_nonlin = self.mlp(x) v_res = self.proj_res(emb) v_base = v_nonlin + v_res v_norm = F.normalize(v_base, p=2, dim=-1) beta = pop_weight_param.clamp(0.01, 0.8) scale = torch.sqrt(1.0 - beta**2) pop_val = self.pop_scores[item_ids].unsqueeze(-1) v_final = torch.cat([v_norm * scale, beta * pop_val], dim=-1) return v_final def run(): print("Loading catalog and users...") item_ids, item_id_to_idx, catalog_feats, pop_scores, log_q = get_catalog_features() targets_df = pd.read_parquet(DATA_DIR / 'target_users.parquet') uids = targets_df.uid.to_numpy() uid_to_idx = {int(uid): idx for idx, uid in enumerate(uids)} N_users = len(uids) num_items = len(item_ids) + 1 # 30001 cutoff_val = 24786800 t_7d = cutoff_val - 7 * 86400 t_14d = cutoff_val - 14 * 86400 print("Extracting validation user histories...") val_hist = extract_user_history(cutoff_val, item_id_to_idx, uid_to_idx, N_users, is_val=True) print("Extracting training windows...") hist_w1 = extract_user_history(t_7d, item_id_to_idx, uid_to_idx, N_users, is_val=False) u_w1, itm_w1, weight_w1 = build_future_targets(t_7d, cutoff_val, uid_to_idx, item_id_to_idx) print(f"Window 1 targets: {len(u_w1)}") hist_w2 = extract_user_history(t_14d, item_id_to_idx, uid_to_idx, N_users, is_val=False) u_w2, itm_w2, weight_w2 = build_future_targets(t_14d, t_7d, uid_to_idx, item_id_to_idx) print(f"Window 2 targets: {len(u_w2)}") d_emb = 128 d_hidden = 128 d_out = 127 # + 1 for pop = 128 shared_emb = nn.Embedding(num_items, d_emb, padding_idx=0) nn.init.normal_(shared_emb.weight, std=0.05) with torch.no_grad(): shared_emb.weight[0].fill_(0.0) user_tower = UserTower(shared_emb, d_emb, d_hidden, d_out) item_tower = ItemTower(shared_emb, catalog_feats, pop_scores, d_emb, d_hidden, d_out) logit_scale = nn.Parameter(torch.tensor(math.log(1.0 / 0.05), dtype=torch.float32)) # We pass user_tower parameters and item_tower specific modules item_params = list(item_tower.feat_proj.parameters()) + list(item_tower.mlp.parameters()) + list(item_tower.proj_res.parameters()) params = list(user_tower.parameters()) + item_params + [logit_scale] optimizer = torch.optim.AdamW(params, lr=1e-3, weight_decay=1e-4) # Validation ground truth val_elig = CandidateSet(DATA_DIR / 'validation_eligible_candidates.npz') graded_val = validation_truth(val_elig, set(uids)) val_packed = np.load(DATA_DIR / 'validation_eligible_candidates.npz')['packed_eligible'] val_catalog_items = np.load(DATA_DIR / 'validation_eligible_candidates.npz')['item_ids'] bits = np.unpackbits(val_packed, axis=1, count=30000, bitorder='little').astype(bool) def evaluate(): user_tower.eval() item_tower.eval() with torch.no_grad(): all_cat_ids = torch.arange(1, num_items, dtype=torch.int64) V = item_tower(all_cat_ids, user_tower.pop_weight) user_vecs = [] bs = 1024 for s in range(0, N_users, bs): e = min(s + bs, N_users) ub = user_tower( val_hist['likes'][s:e], val_hist['likes_w'][s:e], val_hist['listens'][s:e], val_hist['listens_w'][s:e], val_hist['stats'][s:e], ) user_vecs.append(ub) U = torch.cat(user_vecs, dim=0) scores = torch.matmul(U, V.t()).numpy() scores[~bits] = -1e9 ranked = {} for r, uid in enumerate(uids): top_cols = np.argpartition(scores[r], -100)[-100:] top_cols = top_cols[np.argsort(-scores[r, top_cols])] ranked[int(uid)] = list(map(int, val_catalog_items[top_cols])) return score_rankings(ranked, graded_val) print("\nInitial zero-shot evaluation...") print(evaluate()) # Combine training data win_flag = np.concatenate([np.zeros(len(u_w1), dtype=np.int32), np.ones(len(u_w2), dtype=np.int32)]) all_u = np.concatenate([u_w1, u_w2]) all_itm = np.concatenate([itm_w1, itm_w2]) all_w = np.concatenate([weight_w1, weight_w2]) N_samples = len(all_u) print(f"Total training samples: {N_samples}") batch_size = 512 epochs = 6 for epoch in range(1, epochs + 1): t0 = time.time() user_tower.train() item_tower.train() perm = np.random.permutation(N_samples) total_loss = 0.0 n_b = 0 for b_start in range(0, N_samples, batch_size): b_end = min(b_start + batch_size, N_samples) if b_end - b_start < 64: continue idx = perm[b_start:b_end] b_u = all_u[idx] b_itm = torch.from_numpy(all_itm[idx]) b_w = torch.from_numpy(all_w[idx]) b_win = win_flag[idx] mask1 = (b_win == 0) mask2 = (b_win == 1) u_vecs = torch.zeros((len(idx), d_out + 1), dtype=torch.float32) if np.any(mask1): idx1 = np.where(mask1)[0] u1 = b_u[idx1] u_vecs[idx1] = user_tower( hist_w1['likes'][u1], hist_w1['likes_w'][u1], hist_w1['listens'][u1], hist_w1['listens_w'][u1], hist_w1['stats'][u1], ) if np.any(mask2): idx2 = np.where(mask2)[0] u2 = b_u[idx2] u_vecs[idx2] = user_tower( hist_w2['likes'][u2], hist_w2['likes_w'][u2], hist_w2['listens'][u2], hist_w2['listens_w'][u2], hist_w2['stats'][u2], ) v_vecs = item_tower(b_itm, user_tower.pop_weight) scale = logit_scale.exp().clamp(max=100.0) # In-batch logits: (B, B) logits = torch.matmul(u_vecs, v_vecs.t()) * scale # Apply LogQ correction on negative columns # LogQ: log_q is of shape (30001,) b_logq = log_q[b_itm].unsqueeze(0) # (1, B) logits = logits - b_logq targets = torch.arange(len(idx), dtype=torch.int64) ce = F.cross_entropy(logits, targets, reduction='none') loss = (ce * b_w).mean() optimizer.zero_grad() loss.backward() torch.nn.utils.clip_grad_norm_(params, max_norm=1.0) optimizer.step() total_loss += loss.item() n_b += 1 dur = time.time() - t0 print(f"Epoch {epoch:02d} | Time: {dur:.1f}s | Loss: {total_loss/n_b:.4f} | PopWeight: {user_tower.pop_weight.item():.4f}") val_res = evaluate() print(f"Validation Score: {val_res}") if __name__ == '__main__': run() ## Step 87 · agent · 2026-09-29T06:42:29.889Z ### run_shell_command · 2026-09-29T06:42:29.889Z { "description": "Run two-tower v2 training and validation", "command": "PYTHONPATH=/task python3 /app/solution/train_two_tower_v2.py" } ### Tool output · 2026-09-29T06:47:07.417Z Output: Loading catalog and users... Extracting validation user histories... Extracting training windows... Window 1 targets: 176193 Window 2 targets: 171289 Initial zero-shot evaluation... {'quality_ndcg@10': 0.000314, 'quality_users': 4263} Total training samples: 347482 Epoch 01 | Time: 41.9s | Loss: 7.3282 | PopWeight: 0.3815 Validation Score: {'quality_ndcg@10': 0.012005, 'quality_users': 4263} Epoch 02 | Time: 40.7s | Loss: 6.6127 | PopWeight: 0.4646 Validation Score: {'quality_ndcg@10': 0.012469, 'quality_users': 4263} Epoch 03 | Time: 41.7s | Loss: 5.9963 | PopWeight: 0.4947 Validation Score: {'quality_ndcg@10': 0.012934, 'quality_users': 4263} Epoch 04 | Time: 43.7s | Loss: 5.4618 | PopWeight: 0.5039 Validation Score: {'quality_ndcg@10': 0.011946, 'quality_users': 4263} Epoch 05 | Time: 42.9s | Loss: 5.0402 | PopWeight: 0.5046 Validation Score: {'quality_ndcg@10': 0.010801, 'quality_users': 4263} Epoch 06 | Time: 37.3s | Loss: 4.7058 | PopWeight: 0.5081 Validation Score: {'quality_ndcg@10': 0.011721, 'quality_users': 4263} Process Group PGID: 3335 ## Step 88 · agent · 2026-09-29T06:47:55.925Z ### Recorded reasoning summary Improving Validation Score I'm pleased to report significant progress, having surpassed pure popularity with a validation score of 0.012934 by epoch 3. I'm now focused on understanding how `PopWeight`'s increase to ~0.50 influenced this, and I'm aiming to push the score even higher, targeting 0.033 or beyond. ### Recorded reasoning summary Revisiting Past Successes I'm looking back at a previous experiment where we achieved a `quality_ndcg@10` of 0.033397. My current focus is to meticulously analyze the code from that run, aiming to pinpoint the exact parameters and logic that led to such a strong performance. This detailed re-examination is key to understanding how to replicate and potentially surpass that result. ### Recorded reasoning summary Analyzing Co-occurrence Matrix I've been examining the structure of $C = X^T X$, recognizing it as a co-occurrence matrix derived from user interactions. I'm now investigating whether the resultant `user_sim = X @ C` computation can be effectively represented and potentially optimized within a Two-Tower Neural Network architecture. ### Recorded reasoning summary Refining Network Architecture I'm currently focusing on the practical implementation of a Two-Tower Neural Network. My efforts are directed towards ensuring that both the user and item towers can be independently precomputed, a crucial aspect for meeting strict request-time latency budgets. I am also ensuring the user tower utilizes interaction history for representation. ### Recorded reasoning summary Decomposing User and Item I'm confirming that the architecture must support independent precomputation for both user and item towers. The user tower will leverage interaction history, not just IDs, and the item tower embeddings are precomputed and indexed for efficient retrieval via maximum inner-product search at request time. ### Recorded reasoning summary Synthesizing Two-Tower Insights I'm integrating my understanding of item co-occurrence with the two-tower architecture. My current thought is to treat the user embedding as a weighted sum of item embeddings from their history, aiming to boost performance beyond SVD by learning these relationships directly within the neural network. ### Recorded reasoning summary Training Item Embeddings I'm now focusing on how to train the item embeddings within PyTorch such that their dot product approximates item co-occurrence, considering different loss functions for this purpose. I'm exploring how to make the user tower learn a representation as a weighted sum of item embeddings from their history. ### Recorded reasoning summary Investigating Production Constraints I'm concerned about the scalability of constructing a full item-item co-occurrence matrix for millions of items, as it would exceed practical memory limits. I need to investigate how production systems like Google, YouTube, Spotify, and Pinterest address this challenge when training two-tower models. ### Recorded reasoning summary Exploring Training Strategies I'm analyzing how large production systems train two-tower models using mini-batches and in-batch negatives. My current focus is understanding why our previous training with these methods only achieved a modest score, and I'm investigating the limited number of gradient steps as a key factor. ### Recorded reasoning summary Expanding Training Data I'm realizing that my current approach of using only the last two windows for training is too restrictive. I'm now considering utilizing all positive interactions from the past 90 days, which amounts to millions of listens and likes, to create a far richer training set. I'm also exploring the possibility of a higher learning rate for embeddings and sampling negatives from the entire catalog, rather than just within batches, to address the mid-tail item exposure issue. ### run_shell_command · 2026-09-29T06:47:55.925Z { "description": "Check samples across 4 windows", "command": "PYTHONPATH=/task python3 -c \"\nimport pandas as pd\nimport numpy as np\nimport pyarrow.parquet as pq\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nimport math\nimport time\nfrom pathlib import Path\n\ndata_dir = Path('/task/data')\ncutoff = 24786800\n\n# Let's count how many targets we get across 4 windows (28 days)\ncat = pd.read_parquet(data_dir / 'candidate_catalog.parquet')\nitem_id_to_idx = {id_: idx for idx, id_ in enumerate(cat.item_id, 1)}\ntargets = set(pd.read_parquet(data_dir / 'target_users.parquet').uid)\n\ntotal_samples = 0\nfor w in range(4):\n end_ts = cutoff - w * 7 * 86400\n start_ts = end_ts - 7 * 86400\n table = pq.read_table(\n data_dir / 'train_listens.parquet',\n columns=['uid', 'item_id'],\n filters=[('timestamp', '>=', start_ts), ('timestamp', '<', end_ts), ('is_organic', '==', 1), ('played_ratio_pct', '>=', 80)]\n )\n df = table.to_pandas()\n df = df[df.uid.isin(targets) & df.item_id.isin(item_id_to_idx)]\n total_samples += len(df)\n print(f'Window {w} ({w*7}-{(w+1)*7}d ago): {len(df)} samples')\n\nprint(f'Total across 4 windows: {total_samples}')\n\"" } ### Tool output · 2026-09-29T06:48:01.391Z Output: Window 0 (0-7d ago): 304885 samples Window 1 (7-14d ago): 289060 samples Window 2 (14-21d ago): 287015 samples Window 3 (21-28d ago): 289176 samples Total across 4 windows: 1170136 Process Group PGID: 3390 ## Step 89 · agent · 2026-09-29T06:48:20.672Z ### Recorded reasoning summary Processing Training Data My thoughts are currently focused on the sheer scale of the training data. I've processed 1.17 million samples across four windows, which, with a batch size of 1024, translates to 1,142 batches per epoch. Impressively, each epoch completes in roughly 30 seconds on CPU, processing at 37,000 samples per second. The next step is to consider pretraining item embeddings using Item2Vec skip-gram. ### Recorded reasoning summary Evaluating Pretraining Efficiency I'm now investigating the speed of pretraining item embeddings on track transitions. Given that training 400,000 item pairs in PyTorch was remarkably fast, I anticipate this crucial step, potentially involving 3.6 million transitions over 3 epochs, could be completed in about 60 seconds. This quick pretraining is key to immediately establishing strong musical similarity in embeddings. This is a continuation of the previous summary, so it will be appended. **Processing Training Data** My thoughts are currently focused on the sheer scale of the training data. I've processed 1.17 million samples across four windows, which, with a batch size of 1024, translates to 1,142 batches per epoch. Impressively, each epoch completes in roughly 30 seconds on CPU, processing at 37,000 samples per second. The next step is to consider pretraining item embeddings using Item2Vec skip-gram. ## Recorded session usage [ { "messageId": "ba26118f-65d1-443d-9ca6-a3aab7516fe3", "timestamp": "2026-09-29T05:48:48.743Z", "model": "gemini-3.8-flash", "tokens": { "input": 10136, "output": 77, "cached": 7656, "thoughts": 55, "tool": 0, "total": 10268 } }, { "messageId": "7a6347ff-fd3f-474d-a50e-9f9f183cbd88", "timestamp": "2026-09-29T05:48:51.142Z", "model": "gemini-3.8-flash", "tokens": { "input": 10324, "output": 73, "cached": 0, "thoughts": 103, "tool": 0, "total": 10500 } }, { "messageId": "2f7b1b12-6572-4594-9662-cc13fcd34f42", "timestamp": "2026-09-29T05:48:57.249Z", "model": "gemini-3.8-flash", "tokens": { "input": 14917, "output": 45, "cached": 7776, "thoughts": 719, "tool": 0, "total": 15681 } }, { "messageId": "3148fe5e-8777-44cc-938f-9756230d0fce", "timestamp": "2026-09-29T05:48:59.901Z", "model": "gemini-3.8-flash", "tokens": { "input": 15767, "output": 81, "cached": 11678, "thoughts": 17, 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