yambda-discovery-ranking-v01 / gemini-3-8-flash-high-basic-r04-b9049e57 Recorded model: gemini-3.8-flash ## Step 1 · user · 2026-09-29T01:22:39.753Z 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 - **Directory Structure:** Showing up to 200 items (files + folders). /app/ └───solution/ ## Step 2 · user · 2026-09-29T01:22:39.795Z # Build a music-discovery ranker Build the strongest ranking model you can for the "Made for You" shelf of a music streaming service. 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, fitted representation, example submission, or modeling architecture is supplied. Build and tune the best model you can, 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 builds the artifact and a concise `/app/solution/README.md` in `/app/solution/`. 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. - You have up to 30 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 · 2026-09-29T01:22:44.243Z ### Recorded reasoning summary Analyzing Prompt & Data I'm currently dissecting the core prompt, focusing on the "Made for You" shelf objective and the specific training data cutoff. My aim is to grasp the environment and inherent constraints to build the most effective ranking model. ### Recorded reasoning summary Exploring Task Environment I'm now examining the available files and system constraints within the task environment. My focus is on understanding the provided data structures and any limitations that might impact model development and submission. ### update_topic · 2026-09-29T01:22:44.243Z { "summary": "Starting initial research into the music discovery ranker task. We will inspect the directory structure, available python packages, evaluation code, data sizes, and system hardware to plan our strategy.", "title": "Initial Task and Environment Research", "strategic_intent": "Inspect task files, data schemas, hardware resources, and evaluation script." } ### Tool output · 2026-09-29T01:22:44.279Z Current topic: "Initial Task and Environment Research" Topic summary: Starting initial research into the music discovery ranker task. We will inspect the directory structure, available python packages, evaluation code, data sizes, and system hardware to plan our strategy. Strategic Intent: Inspect task files, data schemas, hardware resources, and evaluation script. ### run_shell_command · 2026-09-29T01:22:44.243Z { "command": "ls -la /task && ls -la /task/data", "description": "List files in /task and /task/data" } ### Tool output · 2026-09-29T01:22:44.279Z Output: total 36 dr-xr-xr-x 1 root root 4096 Sep 29 01:20 . drwxr-xr-x 1 root root 4096 Sep 29 01:22 .. dr-xr-xr-x 1 root root 4096 Sep 29 01:17 data -r--r--r-- 1 root root 5774 Sep 29 01:16 evaluate_public.py -r--r--r-- 1 root root 8265 Sep 29 01:16 ranking_evaluation.py -r--r--r-- 1 root root 1278 Sep 29 01:16 requirements.txt total 347392 dr-xr-xr-x 1 root root 4096 Sep 29 01:17 . dr-xr-xr-x 1 root root 4096 Sep 29 01:20 .. -r--r--r-- 1 root root 349352 Sep 29 01:16 candidate_catalog.parquet -r--r--r-- 1 root root 84127 Sep 29 01:16 target_users.parquet -r--r--r-- 1 root root 7736341 Sep 29 01:16 test_eligible_candidates.npz -r--r--r-- 1 root root 1092329 Sep 29 01:17 test_preference_state.parquet -r--r--r-- 1 root root 857213 Sep 29 01:16 train_dislikes.parquet -r--r--r-- 1 root root 5374652 Sep 29 01:16 train_likes.parquet -r--r--r-- 1 root root 319183255 Sep 29 01:16 train_listens.parquet -r--r--r-- 1 root root 155988 Sep 29 01:16 train_undislikes.parquet -r--r--r-- 1 root root 1855885 Sep 29 01:16 train_unlikes.parquet -r--r--r-- 1 root root 36571 Sep 29 01:16 val_dislikes.parquet -r--r--r-- 1 root root 298484 Sep 29 01:16 val_likes.parquet -r--r--r-- 1 root root 9947609 Sep 29 01:16 val_listens.parquet -r--r--r-- 1 root root 7845 Sep 29 01:16 val_undislikes.parquet -r--r--r-- 1 root root 72536 Sep 29 01:16 val_unlikes.parquet -r--r--r-- 1 root root 7573821 Sep 29 01:16 validation_eligible_candidates.npz -r--r--r-- 1 root root 1061761 Sep 29 01:17 validation_preference_state.parquet Process Group PGID: 1612 ## Step 4 · agent · 2026-09-29T01:22:46.258Z ### run_shell_command · 2026-09-29T01:22:46.258Z { "command": "lscpu; free -h; nvidia-smi || true", "description": "Check CPU, memory, and GPU availability" } ### Tool output · 2026-09-29T01:22:46.280Z Output: Architecture: x86_64 CPU op-mode(s): 32-bit, 64-bit Address sizes: 48 bits physical, 48 bits virtual Byte Order: Little Endian CPU(s): 8 On-line CPU(s) list: 0-7 Vendor ID: AuthenticAMD Model name: AMD EPYC 7R13 Processor CPU family: 25 Model: 1 Thread(s) per core: 2 Core(s) per socket: 4 Socket(s): 1 Stepping: 1 BogoMIPS: 5299.99 Flags: fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ht syscall nx mmxext fxsr_opt pdpe1gb rdtscp lm constant_tsc rep_good nopl xtopology nonstop_tsc cpuid extd_apicid aperfmperf tsc_known_freq pni pclmulqdq ssse3 fma cx16 pcid sse4_1 sse4_2 x2apic movbe popcnt aes xsave avx f16c rdrand hypervisor lahf_lm cmp_legacy cr8_legacy abm sse4a misalignsse 3dnowprefetch topoext ssbd ibrs ibpb stibp vmmcall fsgsbase bmi1 avx2 smep bmi2 invpcid rdseed adx smap clflushopt clwb sha_ni xsaveopt xsavec xgetbv1 clzero xsaveerptr rdpru wbnoinvd arat npt nrip_save vaes vpclmulqdq rdpid Hypervisor vendor: KVM Virtualization type: full L1d cache: 128 KiB (4 instances) L1i cache: 128 KiB (4 instances) L2 cache: 2 MiB (4 instances) L3 cache: 16 MiB (1 instance) NUMA node(s): 1 NUMA node0 CPU(s): 0-7 Vulnerability Gather data sampling: Not affected Vulnerability Ghostwrite: Not affected Vulnerability Indirect target selection: Not affected Vulnerability Itlb multihit: Not affected Vulnerability L1tf: Not affected Vulnerability Mds: Not affected Vulnerability Meltdown: Not affected Vulnerability Mmio stale data: Not affected Vulnerability Old microcode: Not affected Vulnerability Reg file data sampling: Not affected Vulnerability Retbleed: Not affected Vulnerability Spec rstack overflow: Mitigation; Safe RET Vulnerability Spec store bypass: Mitigation; Speculative Store Bypass disabled via prctl Vulnerability Spectre v1: Mitigation; usercopy/swapgs barriers and __user pointer sanitization Vulnerability Spectre v2: Mitigation; Retpolines; IBPB conditional; IBRS_FW; STIBP always-on; RSB filling; PBRSB-eIBRS Not affected; BHI Not affected Vulnerability Srbds: Not affected Vulnerability Tsa: Mitigation; Clear CPU buffers Vulnerability Tsx async abort: Not affected Vulnerability Vmscape: Not affected /usr/bin/bash: line 4: free: command not found /usr/bin/bash: line 4: nvidia-smi: command not found Process Group PGID: 1616 ### read_file · 2026-09-29T01:22:46.258Z { "file_path": "/task/evaluate_public.py" } ### Tool error · 2026-09-29T01:22:46.280Z Path not in workspace: Attempted path "/task/evaluate_public.py" resolves outside the allowed workspace directories: /app or the project temp directory: /home/agent/.gemini/tmp/app ## Step 5 · agent · 2026-09-29T01:22:48.470Z ### Recorded reasoning summary Checking System Memory I'm currently examining available system memory to understand resource constraints. My next step is to analyze the `evaluate_public.py` script in detail. ### run_shell_command · 2026-09-29T01:22:48.470Z { "description": "Check system memory and read evaluate_public.py", "command": "head -n 5 /proc/meminfo && cat /task/evaluate_public.py" } ### Tool output · 2026-09-29T01:22:48.487Z Output: MemTotal: 15988968 kB MemFree: 5363488 kB MemAvailable: 14557848 kB Buffers: 196276 kB Cached: 8873924 kB #!/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() Process Group PGID: 1621 ## Step 6 · agent · 2026-09-29T01:22:50.433Z ### run_shell_command · 2026-09-29T01:22:50.433Z { "description": "Read requirements.txt and ranking_evaluation.py", "command": "cat /task/requirements.txt && cat /task/ranking_evaluation.py" } ### Tool output · 2026-09-29T01:22:50.448Z Output: # Exact third-party distribution inventory for yambda-discovery-ranking-v01. # # 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. The same inventory is used for source preparation, # task building, reference scoring, judging, and solver attempts. 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 idna==3.18 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 mdit-py-plugins==0.6.1 mdurl==0.1.2 nbformat==5.11.1 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 six==1.17.0 text-unidecode==1.3 threadpoolctl==3.6.0 tqdm==4.70.0 traitlets==5.16.1 typing-extensions==4.16.0 tzdata==2026.3 urllib3==2.7.0 webencodings==0.6.1 """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), } Process Group PGID: 1625 ## Step 7 · agent · 2026-09-29T01:22:53.629Z ### Recorded reasoning summary Checking Installed Libraries I'm currently inventorying our available libraries, noting versions for numpy, scipy, pandas, and scikit-learn. My next step is to determine if lightgbm or xgboost are present. ### run_shell_command · 2026-09-29T01:22:53.629Z { "description": "Check available ML libraries", "command": "python3 -c \"\nfor pkg in ['lightgbm', 'xgboost', 'catboost', 'torch', 'implicit', 'numba', 'scipy', 'sklearn']:\n try:\n __import__(pkg)\n print(f'{pkg}: available')\n except ImportError:\n print(f'{pkg}: NOT available')\n\"" } ### Tool output · 2026-09-29T01:22:55.034Z Output: lightgbm: NOT available xgboost: NOT available catboost: NOT available torch: NOT available implicit: NOT available numba: NOT available scipy: available sklearn: available Process Group PGID: 1629 ## Step 8 · agent · 2026-09-29T01:22:58.818Z ### Recorded reasoning summary Exploring Available Libraries I'm pleased to confirm that I have access to core scientific computing libraries including `scipy`, `sklearn`, `numpy`, and `pandas`. This is a crucial first step, and I will now focus on how best to leverage these tools to meet the project's objectives within the given constraints and time limit. ### run_shell_command · 2026-09-29T01:22:58.818Z { "command": "python3 -c \"\nimport pandas as pd\nimport numpy as np\nfrom pathlib import Path\n\nDATA = Path('/task/data')\n\nfor f in sorted(DATA.glob('*.parquet')):\n df = pd.read_parquet(f)\n print(f'{f.name:35s}: shape={df.shape}, cols={list(df.columns)}')\n if 'timestamp' in df.columns:\n print(f' timestamp min={df.timestamp.min()}, max={df.timestamp.max()}')\n\nfor f in sorted(DATA.glob('*.npz')):\n with np.load(f) as z:\n print(f'{f.name:35s}: files={z.files}')\n for k in z.files:\n print(f' {k}: shape={z[k].shape}, dtype={z[k].dtype}')\n\"", "description": "Inspect dataset shapes, column names, timestamps, and npz contents" } ### Tool output · 2026-09-29T01:23:00.785Z Output: candidate_catalog.parquet : shape=(30000, 4), cols=['item_id', 'positive_listens', 'organic_positive_listens', 'popularity_rank'] target_users.parquet : shape=(8456, 3), cols=['uid', 'recent_positive_listens', 'history_positive_listens'] test_preference_state.parquet : shape=(484776, 3), cols=['uid', 'item_id', 'state'] train_dislikes.parquet : shape=(101075, 4), cols=['uid', 'timestamp', 'item_id', 'is_organic'] timestamp min=75, max=24786580 train_likes.parquet : shape=(814831, 4), cols=['uid', 'timestamp', 'item_id', 'is_organic'] timestamp min=80, max=24786790 train_listens.parquet : shape=(43484159, 6), cols=['uid', 'timestamp', 'item_id', 'is_organic', 'played_ratio_pct', 'track_length_seconds'] timestamp min=0, max=24786795 train_undislikes.parquet : shape=(19960, 4), cols=['uid', 'timestamp', 'item_id', 'is_organic'] timestamp min=15950, max=24785395 train_unlikes.parquet : shape=(292665, 4), cols=['uid', 'timestamp', 'item_id', 'is_organic'] timestamp min=250, max=24786570 val_dislikes.parquet : shape=(3563, 4), cols=['uid', 'timestamp', 'item_id', 'is_organic'] timestamp min=24789920, max=25393280 val_likes.parquet : shape=(37795, 4), cols=['uid', 'timestamp', 'item_id', 'is_organic'] timestamp min=24788815, max=25393390 val_listens.parquet : shape=(1501697, 6), cols=['uid', 'timestamp', 'item_id', 'is_organic', 'played_ratio_pct', 'track_length_seconds'] timestamp min=24788600, max=25393395 val_undislikes.parquet : shape=(606, 4), cols=['uid', 'timestamp', 'item_id', 'is_organic'] timestamp min=24810715, max=25393035 val_unlikes.parquet : shape=(9346, 4), cols=['uid', 'timestamp', 'item_id', 'is_organic'] timestamp min=24789005, max=25393385 validation_preference_state.parquet: shape=(469124, 3), cols=['uid', 'item_id', 'state'] test_eligible_candidates.npz : files=['uids', 'item_ids', 'packed_eligible'] uids: shape=(8456,), dtype=uint32 item_ids: shape=(30000,), dtype=uint32 packed_eligible: shape=(8456, 3750), dtype=uint8 validation_eligible_candidates.npz : files=['uids', 'item_ids', 'packed_eligible'] uids: shape=(8456,), dtype=uint32 item_ids: shape=(30000,), dtype=uint32 packed_eligible: shape=(8456, 3750), dtype=uint8 Process Group PGID: 1647 ## Step 9 · agent · 2026-09-29T01:23:05.757Z ### Recorded reasoning summary Processing Input Data I've begun by examining the provided data files. I've noted the total number of users in `target_users.parquet` and the item count along with its ordering and relevant columns from `candidate_catalog.parquet`. My next step is to understand the `validation_` file. ### Recorded reasoning summary Analyzing Data Sizes I'm now processing the `.npz` files, noting the user and item counts, and investigating the memory implications of loading the massive `train_listens.parquet` file. I'm also meticulously dissecting the timestamp data to establish precise training, validation, and test windows, which are crucial for the upcoming analysis of target users and candidate eligibility. ### run_shell_command · 2026-09-29T01:23:05.757Z { "description": "Check eligible candidates per user statistics", "command": "python3 -c \"\nimport numpy as np\n\nfor name in ['validation_eligible_candidates.npz', 'test_eligible_candidates.npz']:\n with np.load(f'/task/data/{name}') as z:\n packed = z['packed_eligible']\n # unpackbits\n unpacked = np.unpackbits(packed, axis=1)[:, :30000]\n sums = unpacked.sum(axis=1)\n print(f'{name}: eligible items per user: min={sums.min()}, max={sums.max()}, mean={sums.mean():.1f}, median={np.median(sums)}')\n\"" } ### Tool output · 2026-09-29T01:23:06.417Z Output: validation_eligible_candidates.npz: eligible items per user: min=24657, max=30000, mean=29067.3, median=29242.0 test_eligible_candidates.npz: eligible items per user: min=24552, max=30000, mean=29041.3, median=29220.0 Process Group PGID: 1667 ## Step 10 · agent · 2026-09-29T01:23:12.084Z ### Recorded reasoning summary Investigating Ineligibility I'm currently diving into the ~950 ineligible items per user, trying to pinpoint the exact criteria. My current hypothesis is that pre-existing states like 'listened' or 'disliked' might be contributing factors, and I'm examining `validation_preference_state` to confirm this. ### run_shell_command · 2026-09-29T01:23:12.084Z { "description": "Check preference state details and relationship with eligibility", "command": "python3 -c \"\nimport pandas as pd\nimport numpy as np\n\npref = pd.read_parquet('/task/data/validation_preference_state.parquet')\nprint('Preference state counts:\\n', pref['state'].value_counts())\nprint('Users in pref:', pref['uid'].nunique())\n\n# Check overlap of preference state with eligibility\nwith np.load('/task/data/validation_eligible_candidates.npz') as z:\n uids = z['uids']\n items = z['item_ids']\n packed = z['packed_eligible']\n uid2idx = {u: i for i, u in enumerate(uids)}\n item2idx = {it: i for i, it in enumerate(items)}\n\nliked = pref[pref.state == 'liked']\ndisliked = pref[pref.state == 'disliked']\n\ndef check_elig(df):\n eligs = []\n for u, it in zip(df.uid[:1000], df.item_id[:1000]):\n if u in uid2idx and it in item2idx:\n r = uid2idx[u]\n c = item2idx[it]\n b = (packed[r, c // 8] >> (c % 8)) & 1\n eligs.append(b)\n return np.mean(eligs) if eligs else None\n\nprint('Fraction of liked in eligibility:', check_elig(liked))\nprint('Fraction of disliked in eligibility:', check_elig(disliked))\n\"" } ### Tool output · 2026-09-29T01:23:13.048Z Output: Preference state counts: state liked 418435 disliked 50689 Name: count, dtype: int64 Users in pref: 7038 Fraction of liked in eligibility: 0.0 Fraction of disliked in eligibility: 0.0 Process Group PGID: 1677 ## Step 11 · agent · 2026-09-29T01:23:27.299Z ### Recorded reasoning summary Clarifying Eligibility Rules I'm examining the platform's ownership of candidate eligibility. Specifically, I'm investigating why `liked` and `disliked` preference states currently register as 0.0 fraction within the eligibility framework, considering all modeling within that boundary is considered in scope for analysis. ### run_shell_command · 2026-09-29T01:23:27.299Z { "description": "Check validation truth stats", "command": "python3 -c \"\nimport pandas as pd\nimport numpy as np\nfrom ranking_evaluation import CandidateSet\nimport sys\nsys.path.append('/task')\nfrom evaluate_public import validation_truth\n\ntargets = set(pd.read_parquet('/task/data/target_users.parquet').uid.astype(int))\neligibility = CandidateSet('/task/data/validation_eligible_candidates.npz')\ntruth = validation_truth(eligibility, targets)\nprint('Scored users in val:', len(truth))\ntot_items = sum(len(v) for v in truth.values())\ngrade3 = sum(sum(1 for g in v.values() if g == 3) for v in truth.values())\ngrade1 = sum(sum(1 for g in v.values() if g == 1) for v in truth.values())\nprint(f'Total positive user-item pairs in val: {tot_items} (grade 3: {grade3}, grade 1: {grade1})')\n\"" } ### Tool output · 2026-09-29T01:23:28.011Z Output: Traceback (most recent call last): File "", line 4, in ModuleNotFoundError: No module named 'ranking_evaluation' Exit Code: 1 Process Group PGID: 1695 ## Step 12 · agent · 2026-09-29T01:23:31.785Z ### run_shell_command · 2026-09-29T01:23:31.785Z { "description": "Check validation truth stats", "command": "python3 -c \"\nimport sys\nsys.path.append('/task')\nimport pandas as pd\nimport numpy as np\nfrom ranking_evaluation import CandidateSet\nfrom evaluate_public import validation_truth\n\ntargets = set(pd.read_parquet('/task/data/target_users.parquet').uid.astype(int))\neligibility = CandidateSet('/task/data/validation_eligible_candidates.npz')\ntruth = validation_truth(eligibility, targets)\nprint('Scored users in val:', len(truth))\ntot_items = sum(len(v) for v in truth.values())\ngrade3 = sum(sum(1 for g in v.values() if g == 3) for v in truth.values())\ngrade1 = sum(sum(1 for g in v.values() if g == 1) for v in truth.values())\nprint(f'Total positive user-item pairs in val: {tot_items} (grade 3: {grade3}, grade 1: {grade1})')\n\"" } ### Tool output · 2026-09-29T01:23:33.059Z Output: Scored users in val: 4263 Total positive user-item pairs in val: 37769 (grade 3: 4417, grade 1: 33352) Process Group PGID: 1706 ## Step 13 · agent · 2026-09-29T01:23:36.102Z ### Recorded reasoning summary Testing Baseline Performance I'm currently evaluating a simple global popularity baseline against our validation set. Initial checks indicate that out of 8456 users, 4263 have relevant items, yielding 37,769 positive pairs. I'm scripting a quick comparison to see its performance. ### run_shell_command · 2026-09-29T01:23:36.102Z { "description": "Check candidate catalog details", "command": "python3 -c \"\nimport sys\nsys.path.append('/task')\nimport pandas as pd\nimport numpy as np\n\n# Load candidate catalog\ncatalog = pd.read_parquet('/task/data/candidate_catalog.parquet')\nprint('Catalog columns:', catalog.columns)\nprint(catalog.head())\n\"" } ### Tool output · 2026-09-29T01:23:36.860Z Output: Catalog columns: Index(['item_id', 'positive_listens', 'organic_positive_listens', 'popularity_rank'], 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 Process Group PGID: 1726 ## Step 14 · agent · 2026-09-29T01:23:42.351Z ### Recorded reasoning summary Generating Initial Ranking I'm currently developing a Python test to establish a baseline ranking. This involves processing a 30,000-item candidate catalog, ordered by popularity, and selecting the top 100 eligible items for each user based on this rank. ### run_shell_command · 2026-09-29T01:23:42.351Z { "description": "Evaluate popularity baseline on validation", "command": "python3 -c \"\nimport sys\nsys.path.append('/task')\nimport pandas as pd\nimport numpy as np\nfrom ranking_evaluation import CandidateSet\nfrom evaluate_public import score, validation_truth\n\n# Load validation eligibility\nwith np.load('/task/data/validation_eligible_candidates.npz') as z:\n uids = z['uids'].astype(np.int64)\n item_ids = z['item_ids'].astype(np.int64)\n packed = z['packed_eligible']\n\ncatalog = pd.read_parquet('/task/data/candidate_catalog.parquet')\n# catalog items in order of popularity\n# Let's map item_ids in catalog to their column index in candidate npz\ncol_map = {item: i for i, item in enumerate(item_ids)}\ncatalog_cols = np.array([col_map[item] for item in catalog.item_id], dtype=np.int32)\ncatalog_items = catalog.item_id.to_numpy(dtype=np.int64)\n\n# For each user, find top 100 eligible items\nranked = {}\nfor r, uid in enumerate(uids):\n # unpack bits for user r\n bits = np.unpackbits(packed[r], count=len(item_ids), bitorder='little')\n # filter catalog_items where bits[catalog_cols] == 1\n eligible_items = catalog_items[bits[catalog_cols] == 1]\n ranked[uid] = eligible_items[:100].tolist()\n\ntargets = set(uids)\neligibility = CandidateSet('/task/data/validation_eligible_candidates.npz')\ntruth = validation_truth(eligibility, targets)\nprint('Catalog positive_listens popularity NDCG@10:', score(ranked, truth))\n\n# Now try organic_positive_listens popularity\ncatalog_org = catalog.sort_values('organic_positive_listens', ascending=False)\ncatalog_org_cols = np.array([col_map[item] for item in catalog_org.item_id], dtype=np.int32)\ncatalog_org_items = catalog_org.item_id.to_numpy(dtype=np.int64)\n\nranked_org = {}\nfor r, uid in enumerate(uids):\n bits = np.unpackbits(packed[r], count=len(item_ids), bitorder='little')\n eligible_items = catalog_org_items[bits[catalog_org_cols] == 1]\n ranked_org[uid] = eligible_items[:100].tolist()\n\nprint('Catalog organic_positive_listens popularity NDCG@10:', score(ranked_org, truth))\n\"" } ### Tool output · 2026-09-29T01:23:45.696Z Output: Catalog positive_listens popularity NDCG@10: {'quality_ndcg@10': 0.01071, 'quality_users': 4263} Catalog organic_positive_listens popularity NDCG@10: {'quality_ndcg@10': 0.010578, 'quality_users': 4263} Process Group PGID: 1743 ## Step 15 · agent · 2026-09-29T01:23:52.336Z ### Recorded reasoning summary Establishing Baseline Performance My current focus is on establishing a solid baseline, with an initial NDCG@10 of approximately 1.07%. I'm also prioritizing the immediate generation of a valid submission file for testing to ensure structural integrity from the outset. ### run_shell_command · 2026-09-29T01:23:52.336Z { "description": "Generate initial baseline submission and validate it", "command": "python3 -c \"\nimport sys\nsys.path.append('/task')\nimport numpy as np\nimport pandas as pd\nfrom ranking_evaluation import validate_ranking, CandidateSet\n\n# Load test eligibility\nwith np.load('/task/data/test_eligible_candidates.npz') as z:\n uids = z['uids'].astype(np.int64)\n item_ids = z['item_ids'].astype(np.int64)\n packed = z['packed_eligible']\n\ncatalog = pd.read_parquet('/task/data/candidate_catalog.parquet')\ncol_map = {item: i for i, item in enumerate(item_ids)}\ncatalog_cols = np.array([col_map[item] for item in catalog.item_id], dtype=np.int32)\ncatalog_items = catalog.item_id.to_numpy(dtype=np.int64)\n\nrecords = []\nfor r, uid in enumerate(uids):\n bits = np.unpackbits(packed[r], count=len(item_ids), bitorder='little')\n eligible_items = catalog_items[bits[catalog_cols] == 1][:100]\n for rank_idx, item in enumerate(eligible_items, 1):\n records.append((uid, item, rank_idx))\n\nsub_df = pd.DataFrame(records, columns=['uid', 'item_id', 'rank'])\nsub_df.to_parquet('/app/solution/submission.parquet', index=False)\nprint('Submission saved! Shape:', sub_df.shape)\n\n# Verify with ranking_evaluation\ntargets = set(uids)\nelig = CandidateSet('/task/data/test_eligible_candidates.npz')\nnorm = validate_ranking(pd.read_parquet('/app/solution/submission.parquet'), targets)\nfor uid, item in norm[['uid', 'item_id']].itertuples(index=False, name=None):\n if not elig.is_eligible(uid, item):\n raise ValueError(f'Item {item} for uid {uid} is not eligible!')\nprint('Submission validation SUCCESSFUL! Exactly 100 rows per user, all ranks 1..100, all items eligible.')\n\"" } ### Tool output · 2026-09-29T01:23:55.968Z Output: Submission saved! Shape: (845600, 3) Submission validation SUCCESSFUL! Exactly 100 rows per user, all ranks 1..100, all items eligible. Process Group PGID: 1763 ## Step 16 · agent · 2026-09-29T01:23:58.528Z ### write_file · 2026-09-29T01:23:58.528Z { "file_path": "/app/solution/README.md", "content": "# Music Discovery Ranking Solution\n\n## Overview\nMusic Discovery candidate ranker for target streaming users.\nEvaluated on graded NDCG@10 on organic discovery events (grade 3 for organic likes, grade 1 for organic listens >= 80%).\n\n## Methodology\n- Baseline popularity ranking verified and structurally valid.\n- Active development of personalized recommendation models (collaborative filtering, matrix factorization, item-item similarity, user profile matching, gradient boosting re-ranking).\n" } ### Tool output · 2026-09-29T01:23:58.538Z Successfully created and wrote to new file: /app/solution/README.md. Here is the updated code: # Music Discovery Ranking Solution ## Overview Music Discovery candidate ranker for target streaming users. Evaluated on graded NDCG@10 on organic discovery events (grade 3 for organic likes, grade 1 for organic listens >= 80%). ## Methodology - Baseline popularity ranking verified and structurally valid. - Active development of personalized recommendation models (collaborative filtering, matrix factorization, item-item similarity, user profile matching, gradient boosting re-ranking). ## Step 17 · agent · 2026-09-29T01:24:03.432Z ### Recorded reasoning summary Reviewing Data Parameters I'm currently examining the provided data parameters. My focus is on understanding the training data cutoff and the subsequent validation window, specifically noting the 30-minute gap before the seven-day public validation period begins. ### Recorded reasoning summary Analyzing Event Structures I've been dissecting the event data, noting the distinct parquet files for 'listens', 'likes', 'dislikes', 'unlikes', and 'undislikes'. Each contains user, timestamp, and item information, with 'listens' offering additional playback details. I'm particularly focused on how these event types might function as features for the ranking model. ### run_shell_command · 2026-09-29T01:24:03.432Z { "command": "python3 -c \"\nimport pandas as pd\nimport pyarrow.parquet as pq\n\ntarget_users = pd.read_parquet('/task/data/target_users.parquet')\nprint('Target users count:', len(target_users))\nprint(target_users.head())\n\nt = pq.read_table('/task/data/train_listens.parquet', columns=['uid'])\nuids = t['uid'].to_numpy()\nunique_uids = set(uids)\nprint('Total unique uids in train_listens:', len(unique_uids))\ntarget_uid_set = set(target_users.uid)\nprint('Target uids in train_listens:', len(target_uid_set.intersection(unique_uids)))\n\"", "description": "Inspect target users in train_listens" } ### Tool output · 2026-09-29T01:24:06.358Z Output: Target users count: 8456 uid recent_positive_listens history_positive_listens 0 100 844.0 2604 1 200 50.0 286 2 300 168.0 359 3 600 1985.0 2423 4 700 1776.0 6114 Total unique uids in train_listens: 9238 Target uids in train_listens: 8456 Process Group PGID: 1780 ## Step 18 · agent · 2026-09-29T01:24:09.457Z ### Recorded reasoning summary Analyzing User Data It's interesting to note the substantial overlap between our target users and those present in `train_listens`. With 8,456 target users all accounted for, and a candidate pool of 30,000 items, I'm now investigating the unique items within `train_listens` and `candidate_catalog`. ### run_shell_command · 2026-09-29T01:24:09.457Z { "description": "Inspect unique items in train_listens and candidate catalog", "command": "python3 -c \"\nimport pyarrow.parquet as pq\nimport pandas as pd\n\ncat = pd.read_parquet('/task/data/candidate_catalog.parquet')\nprint('Candidate catalog unique items:', cat['item_id'].nunique())\nt = pq.read_table('/task/data/train_listens.parquet', columns=['item_id'])\nitem_ids = t['item_id'].to_numpy()\nunique_items = set(item_ids)\nprint('Total unique item_ids in train_listens:', len(unique_items))\ncat_item_set = set(cat['item_id'])\nprint('Candidate items in train_listens:', len(cat_item_set.intersection(unique_items)))\n\"" } ### Tool output · 2026-09-29T01:24:13.382Z Output: Candidate catalog unique items: 30000 Total unique item_ids in train_listens: 845395 Candidate items in train_listens: 30000 Process Group PGID: 1802 ## Step 19 · agent · 2026-09-29T01:24:38.750Z ### Recorded reasoning summary Analyzing Candidate Item Coverage My analysis reveals that all 30,000 items in the candidate catalog are present within the training listen data, out of a total of 845,395 unique item IDs. I'm now investigating how target users have interacted with these specific 30,000 items. ### run_shell_command · 2026-09-29T01:24:38.750Z { "description": "Check if validation truth items appeared in user history", "command": "python3 -c \"\nimport pandas as pd\nimport numpy as np\nimport sys\nsys.path.append('/task')\nfrom ranking_evaluation import CandidateSet\nfrom evaluate_public import validation_truth\n\n# Check validation truth items: did the user listen to or like them in train?\ntargets = set(pd.read_parquet('/task/data/target_users.parquet').uid.astype(int))\neligibility = CandidateSet('/task/data/validation_eligible_candidates.npz')\ntruth = validation_truth(eligibility, targets)\n\n# Load some train data for these users\n# Let's inspect a subset or load train_likes and train_listens\ntrain_likes = pd.read_parquet('/task/data/train_likes.parquet')\ntrain_pref = pd.read_parquet('/task/data/validation_preference_state.parquet')\n\n# Check how many (uid, item_id) in truth were in train_pref\ntruth_pairs = [(u, it) for u, items in truth.items() for it in items]\ntruth_df = pd.DataFrame(truth_pairs, columns=['uid', 'item_id'])\nprint(f'Total truth pairs: {len(truth_df)}')\n\npref_merged = pd.merge(truth_df, train_pref, on=['uid', 'item_id'])\nprint(f'Truth pairs in validation_preference_state: {len(pref_merged)}')\n\n# Now let us sample some users to see if their truth items were in train_listens\nsample_users = list(truth.keys())[:100]\nsample_truth = truth_df[truth_df.uid.isin(sample_users)]\nprint(f'Sample truth pairs: {len(sample_truth)}')\n\nimport pyarrow.parquet as pq\ntable = pq.read_table('/task/data/train_listens.parquet', \n filters=[('uid', 'in', sample_users)],\n columns=['uid', 'item_id', 'played_ratio_pct'])\nlistens_sample = table.to_pandas()\nprint(f'Listens for sample users: {len(listens_sample)}')\n\nm = pd.merge(sample_truth, listens_sample, on=['uid', 'item_id'])\nprint(f'Sample truth pairs that appeared in train_listens: {len(m)} (unique pairs: {m[[\\\"uid\\\", \\\"item_id\\\"]].drop_duplicates().shape[0]}) out of {len(sample_truth)}')\n\"" } ### Tool output · 2026-09-29T01:24:40.125Z Output: Total truth pairs: 37769 Truth pairs in validation_preference_state: 0 Sample truth pairs: 858 Listens for sample users: 591918 Sample truth pairs that appeared in train_listens: 0 (unique pairs: 0) out of 858 Process Group PGID: 1823 ## Step 20 · agent · 2026-09-29T01:24:45.135Z ### Recorded reasoning summary Investigating Training Pair Count I'm currently digging into why zero truth pairs appeared in my training set. I'm planning to broaden my investigation across more users and examine the contents of `validation_eligible_candidates.npz` to understand the underlying issue. ### run_shell_command · 2026-09-29T01:24:45.135Z { "description": "Check if listened items in candidate catalog are ever eligible", "command": "python3 -c \"\nimport pandas as pd\nimport numpy as np\nimport pyarrow.parquet as pq\n\n# Let us check 5 users completely: what is the overlap between their train listens/likes and eligible candidates?\nwith np.load('/task/data/validation_eligible_candidates.npz') as z:\n uids = z['uids'].astype(np.int64)\n item_ids = z['item_ids'].astype(np.int64)\n packed = z['packed_eligible']\n\nuid2row = {u: i for i, u in enumerate(uids)}\nitem2col = {it: i for i, it in enumerate(item_ids)}\n\nsample_uids = uids[:10].tolist()\n\ntable = pq.read_table('/task/data/train_listens.parquet',\n filters=[('uid', 'in', sample_uids)],\n columns=['uid', 'item_id'])\ndf_listens = table.to_pandas()\ndf_likes = pd.read_parquet('/task/data/train_likes.parquet')\ndf_likes_sample = df_likes[df_likes.uid.isin(sample_uids)]\n\nfor u in sample_uids:\n r = uid2row[u]\n bits = np.unpackbits(packed[r], count=len(item_ids), bitorder='little')\n user_listens = set(df_listens[df_listens.uid == u].item_id)\n user_likes = set(df_likes_sample[df_likes_sample.uid == u].item_id)\n \n # check how many listened items are in candidate catalog\n listened_in_cat = [it for it in user_listens if it in item2col]\n eligible_listened = [it for it in listened_in_cat if bits[item2col[it]] == 1]\n \n print(f'User {u}: listened_in_cat={len(listened_in_cat)}, of which eligible={len(eligible_listened)}')\n\"" } ### Tool output · 2026-09-29T01:24:46.094Z Output: User 100: listened_in_cat=682, of which eligible=0 User 200: listened_in_cat=168, of which eligible=0 User 300: listened_in_cat=69, of which eligible=0 User 600: listened_in_cat=680, of which eligible=0 User 700: listened_in_cat=1653, of which eligible=0 User 800: listened_in_cat=2524, of which eligible=0 User 900: listened_in_cat=334, of which eligible=0 User 1000: listened_in_cat=499, of which eligible=0 User 1100: listened_in_cat=1440, of which eligible=0 User 1400: listened_in_cat=1262, of which eligible=0 Process Group PGID: 1849 ## Step 21 · agent · 2026-09-29T01:25:12.456Z ### Recorded reasoning summary Discovering New Music I'm focusing on ensuring the recommendations I generate are truly novel. My current thinking prioritizes identifying and surfacing only those tracks the user hasn't encountered before, making this a genuine discovery engine. ### Recorded reasoning summary Refining Ranking Strategies My current focus is on refining the ranking algorithms. I'm exploring implicit feedback collaborative filtering methods, aiming for speed and effectiveness. The goal is to accurately predict organic listening and preference within the next week, considering a large user and item catalog. I'm beginning by analyzing the performance of loading the training data. ### run_shell_command · 2026-09-29T01:25:12.456Z { "command": "python3 -c \"\nimport time\nimport pyarrow.parquet as pq\n\nt0 = time.time()\ntable = pq.read_table('/task/data/train_listens.parquet', columns=['uid', 'item_id', 'played_ratio_pct', 'is_organic', 'timestamp'])\nt1 = time.time()\nprint(f'Reading train_listens took {t1 - t0:.2f}s, rows={len(table)}')\n\"", "description": "Benchmark loading time of train_listens.parquet" } ### Tool output · 2026-09-29T01:25:13.655Z Output: Reading train_listens took 0.71s, rows=43484159 Process Group PGID: 1871 ## Step 22 · agent · 2026-09-29T01:25:19.964Z ### Recorded reasoning summary Analyzing Data Efficiently I'm impressed by the speed of the data loading, processing 43.48 million rows in under a second. My current focus is on segmenting this data by key metrics: organic status, play ratio percentages, and differentiating between the 30,000 candidate items and the rest, to understand event distribution. ### run_shell_command · 2026-09-29T01:25:19.964Z { "command": "python3 -c \"\nimport pyarrow.parquet as pq\nimport pyarrow.compute as pc\nimport numpy as np\n\ntable = pq.read_table('/task/data/train_listens.parquet', columns=['uid', 'item_id', 'played_ratio_pct', 'is_organic', 'timestamp'])\ntotal_rows = len(table)\nprint(f'Total rows: {total_rows}')\n\norg = pc.equal(table['is_organic'], 1)\nprint(f'Organic: {pc.sum(org).as_py()} ({pc.mean(org).as_py():.2%})')\n\nplayed_50 = pc.greater_equal(table['played_ratio_pct'], 50)\nprint(f'Played >= 50%: {pc.sum(played_50).as_py()} ({pc.mean(played_50).as_py():.2%})')\n\nplayed_80 = pc.greater_equal(table['played_ratio_pct'], 80)\nprint(f'Played >= 80%: {pc.sum(played_80).as_py()} ({pc.mean(played_80).as_py():.2%})')\n\norg_80 = pc.and_(org, played_80)\nprint(f'Organic & played >= 80%: {pc.sum(org_80).as_py()} ({pc.mean(org_80).as_py():.2%})')\n\n# Check timestamps range\nts = table['timestamp']\nmin_ts, max_ts = pc.min(ts).as_py(), pc.max(ts).as_py()\nprint(f'Timestamps: {min_ts} to {max_ts} (range: {(max_ts - min_ts) / 86400:.1f} days)')\n\"", "description": "Analyze event statistics and time range in train_listens" } ### Tool output · 2026-09-29T01:25:21.847Z Output: Total rows: 43484159 Organic: 22477622 (51.69%) Played >= 50%: 27500755 (63.24%) Played >= 80%: 25755197 (59.23%) Organic & played >= 80%: 12177583 (28.00%) Timestamps: 0 to 24786795 (range: 286.9 days) Process Group PGID: 1893 ## Step 23 · agent · 2026-09-29T01:25:25.220Z ### Recorded reasoning summary Analyzing User Data I'm currently examining the `target_users.parquet` file. It contains user identifiers along with their recent and historical positive listening counts, spanning a period of approximately 287 days. ### run_shell_command · 2026-09-29T01:25:25.220Z { "command": "python3 -c \"\nimport pandas as pd\n\nlikes = pd.read_parquet('/task/data/train_likes.parquet')\nprint('Likes:', len(likes), likes.head())\nunlikes = pd.read_parquet('/task/data/train_unlikes.parquet')\nprint('Unlikes:', len(unlikes))\ndislikes = pd.read_parquet('/task/data/train_dislikes.parquet')\nprint('Dislikes:', len(dislikes))\nundislikes = pd.read_parquet('/task/data/train_undislikes.parquet')\nprint('Undislikes:', len(undislikes))\n\"", "description": "Inspect feedback event counts" } ### Tool output · 2026-09-29T01:25:26.004Z Output: Likes: 814831 uid timestamp item_id is_organic 0 100 44755 732449 1 1 100 1155860 6568592 0 2 100 1259125 5411243 1 3 100 1260005 7371186 0 4 100 1263935 4943655 0 Unlikes: 292665 Dislikes: 101075 Undislikes: 19960 Process Group PGID: 1915 ## Step 24 · agent · 2026-09-29T01:25:33.301Z ### Recorded reasoning summary Analyzing Validation Data I'm processing the `validation_preference_state.parquet` file. It contains 469,124 rows representing active preference states at the training cutoff, intended for validation ranking. ### Recorded reasoning summary Exploring Collaborative Filtering I'm considering matrix factorization techniques, specifically TruncatedSVD, iALS, and ALS, as potential models for this task. The goal is to leverage collaborative filtering to build effective recommendation systems. ### run_shell_command · 2026-09-29T01:25:33.301Z { "description": "Check target user listens in candidate catalog", "command": "python3 -c \"\nimport pyarrow.parquet as pq\nimport pyarrow.compute as pc\nimport pandas as pd\nimport numpy as np\n\ncat = pd.read_parquet('/task/data/candidate_catalog.parquet')\ncat_set = set(cat.item_id)\n\ntable = pq.read_table('/task/data/train_listens.parquet', columns=['uid', 'item_id', 'played_ratio_pct', 'is_organic', 'timestamp'])\ndf = table.to_pandas()\ntarget_users = set(pd.read_parquet('/task/data/target_users.parquet').uid)\ndf_target = df[df.uid.isin(target_users)]\nprint(f'Total target listens: {len(df_target)}')\n\nin_cat = df_target.item_id.isin(cat_set)\nprint(f'Listens in 30k catalog: {in_cat.sum()} ({in_cat.mean():.2%})')\n\"" } ### Tool output · 2026-09-29T01:25:41.169Z Output: Total target listens: 43222297 Listens in 30k catalog: 31062540 (71.87%) Process Group PGID: 1932 ## Step 25 · agent · 2026-09-29T01:25:46.831Z ### Recorded reasoning summary Analyzing User Engagement My analysis is honing in on target user engagement. It's clear nearly all listens come from this group, with a significant concentration within the 30,000-item candidate catalog. This suggests a highly engaged core audience. ### Recorded reasoning summary Forming Interaction Matrix I'm considering building a User-Item interaction matrix, exploring both dense and sparse formats. The memory requirements for even a large dense matrix appear manageable within our existing RAM, with sparse matrices being even more efficient. This approach seems promising for further analysis. ### run_shell_command · 2026-09-29T01:25:46.831Z { "command": "python3 -c \"\nimport pyarrow.parquet as pq\nimport pandas as pd\nimport numpy as np\n\n# Check user-item stats\ntable = pq.read_table('/task/data/train_listens.parquet', columns=['uid', 'item_id', 'timestamp', 'played_ratio_pct', 'is_organic'])\ndf = table.to_pandas()\ncat = set(pd.read_parquet('/task/data/candidate_catalog.parquet').item_id)\ndf_cat = df[df.item_id.isin(cat)]\n\n# Filter to positive listens: played_ratio_pct >= 50 or 80\ndf_pos = df_cat[df_cat.played_ratio_pct >= 50]\nprint(f'Positive listens in catalog: {len(df_pos)}')\nunique_pairs = df_pos.drop_duplicates(subset=['uid', 'item_id'])\nprint(f'Unique user-item positive pairs in catalog: {len(unique_pairs)}')\nprint(f'Average pairs per user: {len(unique_pairs) / 8456:.1f}')\n\"", "description": "Check unique user-item pairs in catalog" } ### Tool output · 2026-09-29T01:25:53.256Z Output: Positive listens in catalog: 20056329 Unique user-item positive pairs in catalog: 5564926 Average pairs per user: 658.1 Process Group PGID: 1954 ## Step 26 · agent · 2026-09-29T01:25:56.294Z ### Recorded reasoning summary Analyzing User Data My current focus is on understanding the scale of the user-item interaction data. It appears we have a substantial number of unique catalog items per user, averaging 658, leading to a total of 5.56 million unique user-item pairs. This volume is translating into significant memory considerations for sparse matrix representations. ### run_shell_command · 2026-09-29T01:25:56.294Z { "description": "Test evaluate_public.py on submission.parquet", "command": "python3 /task/evaluate_public.py /app/solution/submission.parquet" } ### Tool output · 2026-09-29T01:25:58.798Z Output: { "model": "/app/solution/submission.parquet", "scores": { "quality_ndcg@10": 0.0, "quality_users": 4263 } } Process Group PGID: 1976 ## Step 27 · agent · 2026-09-29T01:26:08.296Z ### Recorded reasoning summary Correcting Evaluation Data I've identified a critical discrepancy: my submission was evaluated against `test_eligible_candidates.npz`, but the evaluation script `evaluate_public.py` correctly uses `validation_eligible_candidates.npz`. This explains the zero NDCG score and necessitates a re-evaluation using the proper dataset. ### Recorded reasoning summary Investigating Submission Discrepancies I've confirmed the public evaluator uses `validation_eligible_candidates.npz`, not `test_eligible_candidates.npz`. The submission's items were all eligible for validation, meaning the issue isn't directly with eligibility checks but likely with how the ranked items align with validation criteria, especially since test eligibility is a subset of validation. ### run_shell_command · 2026-09-29T01:26:08.296Z { "description": "Check evaluation of submission.parquet against validation truth", "command": "python3 -c \"\nimport pandas as pd\nimport sys\nsys.path.append('/task')\nfrom ranking_evaluation import read_ranking, CandidateSet, score_rankings, ranked_items\nfrom evaluate_public import validation_truth\n\nsub = read_ranking('/app/solution/submission.parquet')\neligibility = CandidateSet('/task/data/validation_eligible_candidates.npz')\ntargets = set(sub.uid.unique())\ntruth = validation_truth(eligibility, targets)\nranked = ranked_items(sub)\nprint('Evaluation of submission.parquet on val truth:', score_rankings(ranked, truth))\n\"" } ### Tool output · 2026-09-29T01:26:09.011Z Output: Traceback (most recent call last): File "", line 8, in File "/task/ranking_evaluation.py", line 98, in read_ranking return pd.read_parquet(path) if path.suffix == ".parquet" else pd.read_csv(path) ^^^^^^^^^^^ AttributeError: 'str' object has no attribute 'suffix' Exit Code: 1 Process Group PGID: 1996 ## Step 28 · agent · 2026-09-29T01:26:13.582Z ### run_shell_command · 2026-09-29T01:26:13.582Z { "description": "Check score on submission.parquet", "command": "python3 -c \"\nfrom pathlib import Path\nimport pandas as pd\nimport sys\nsys.path.append('/task')\nfrom ranking_evaluation import read_ranking, CandidateSet, score_rankings, ranked_items\nfrom evaluate_public import validation_truth\n\nsub = read_ranking(Path('/app/solution/submission.parquet'))\neligibility = CandidateSet('/task/data/validation_eligible_candidates.npz')\ntargets = set(sub.uid.unique())\ntruth = validation_truth(eligibility, targets)\nranked = ranked_items(sub)\nprint('Evaluation of submission.parquet on val truth:', score_rankings(ranked, truth))\n\"" } ### Tool output · 2026-09-29T01:26:15.316Z Output: Evaluation of submission.parquet on val truth: {'quality_ndcg@10': 0.0, 'quality_users': 4263} Process Group PGID: 2007 ## Step 29 · agent · 2026-09-29T01:26:17.769Z ### run_shell_command · 2026-09-29T01:26:17.769Z { "description": "Inspect submission.parquet ranking ordering", "command": "python3 -c \"\nfrom pathlib import Path\nimport pandas as pd\nimport sys\nsys.path.append('/task')\nfrom ranking_evaluation import read_ranking, ranked_items\n\nsub = read_ranking(Path('/app/solution/submission.parquet'))\nprint(sub.head(15))\nprint(sub.dtypes)\n\nordered = sub.sort_values(['uid', 'rank'])\nr = {int(uid): list(map(int, values)) for uid, values in ordered.groupby('uid').item_id}\nprint('User 100 top 10:', r[100][:10])\n\"" } ### Tool output · 2026-09-29T01:26:18.859Z Output: uid item_id rank 0 100 5862961 1 1 100 6901374 2 2 100 5635052 3 3 100 3542184 4 4 100 8213481 5 5 100 906358 6 6 100 2859641 7 7 100 3033749 8 8 100 2651314 9 9 100 5252571 10 10 100 5436843 11 11 100 5927996 12 12 100 8902934 13 13 100 5734320 14 14 100 1464852 15 uid int64 item_id int64 rank int64 dtype: object User 100 top 10: [5862961, 6901374, 5635052, 3542184, 8213481, 906358, 2859641, 3033749, 2651314, 5252571] Process Group PGID: 2027 ## Step 30 · agent · 2026-09-29T01:26:27.305Z ### Recorded reasoning summary Investigating Discrepancies I'm currently digging into why the popularity baseline calculation yielded different results. It seems there might be a subtle difference in how the data is being processed between the two instances. I'm scrutinizing the earlier script for clues. ### Recorded reasoning summary Revisiting Candidate Selection I've identified a potential issue with how user IDs (`uids`) are being handled. It appears the user IDs used for candidate generation might not perfectly align with those in the submission file, leading to discrepancies. I'm now comparing these `uids` directly. ### run_shell_command · 2026-09-29T01:26:27.305Z { "command": "python3 -c \"\nfrom pathlib import Path\nimport pandas as pd\nimport numpy as np\nimport sys\nsys.path.append('/task')\nfrom ranking_evaluation import CandidateSet\nfrom evaluate_public import validation_truth\n\ntargets = set(pd.read_parquet('/task/data/target_users.parquet').uid.astype(int))\neligibility = CandidateSet('/task/data/validation_eligible_candidates.npz')\ntruth = validation_truth(eligibility, targets)\n\n# Check user 100\nprint('User 100 in truth?', 100 in truth)\nif 100 in truth:\n print('User 100 truth items:', truth[100])\n\n# Check test eligibility vs val eligibility for user 100\nwith np.load('/task/data/validation_eligible_candidates.npz') as val_z, np.load('/task/data/test_eligible_candidates.npz') as test_z:\n val_items = val_z['item_ids']\n val_pack = val_z['packed_eligible']\n test_pack = test_z['packed_eligible']\n \n val_bits_0 = np.unpackbits(val_pack[0], count=len(val_items), bitorder='little')\n test_bits_0 = np.unpackbits(test_pack[0], count=len(val_items), bitorder='little')\n print('Diff between val and test bits for user 0:', np.sum(val_bits_0 != test_bits_0))\n print('Items in val but not test:', np.sum((val_bits_0 == 1) & (test_bits_0 == 0)))\n\"", "description": "Check user 100 truth and bitmask difference" } ### Tool output · 2026-09-29T01:26:28.751Z Output: User 100 in truth? False Diff between val and test bits for user 0: 24 Items in val but not test: 24 Process Group PGID: 2046 ## Step 31 · agent · 2026-09-29T01:26:32.029Z ### run_shell_command · 2026-09-29T01:26:32.029Z { "description": "Inspect scores across users for submission.parquet", "command": "python3 -c \"\nfrom pathlib import Path\nimport pandas as pd\nimport numpy as np\nimport sys\nsys.path.append('/task')\nfrom ranking_evaluation import CandidateSet, read_ranking, ranked_items, ndcg_at_k\nfrom evaluate_public import validation_truth\n\nsub = read_ranking(Path('/app/solution/submission.parquet'))\neligibility = CandidateSet('/task/data/validation_eligible_candidates.npz')\ntargets = set(sub.uid.unique())\ntruth = validation_truth(eligibility, targets)\nranked = ranked_items(sub)\n\nscores = [ndcg_at_k(ranked[uid], gold, 10) for uid, gold in truth.items()]\nprint('Number of truth users:', len(truth))\nprint('Number of nonzero scores:', sum(1 for s in scores if s > 0))\nprint('Max score:', max(scores))\nprint('Mean score:', np.mean(scores))\n\"" } ### Tool output · 2026-09-29T01:26:35.136Z Output: Number of truth users: 4263 Number of nonzero scores: 0 Max score: 0.0 Mean score: 0.0 Process Group PGID: 2066 ## Step 32 · agent · 2026-09-29T01:26:39.978Z ### Recorded reasoning summary Investigating Catalog Data I'm re-examining the initial data loading and processing, specifically why non-zero scores were reported as zero. I'm tracing back to the `pd.read_parquet` call and the subsequent handling of "Catalog positive" entries to pinpoint the discrepancy. ### run_shell_command · 2026-09-29T01:26:39.978Z { "command": "python3 -c \"\nimport sys\nsys.path.append('/task')\nimport pandas as pd\nimport numpy as np\nfrom ranking_evaluation import CandidateSet\nfrom evaluate_public import score, validation_truth\n\nwith np.load('/task/data/validation_eligible_candidates.npz') as z:\n uids = z['uids'].astype(np.int64)\n item_ids = z['item_ids'].astype(np.int64)\n packed = z['packed_eligible']\n\ncatalog = pd.read_parquet('/task/data/candidate_catalog.parquet')\ncol_map = {item: i for i, item in enumerate(item_ids)}\ncatalog_cols = np.array([col_map[item] for item in catalog.item_id], dtype=np.int32)\ncatalog_items = catalog.item_id.to_numpy(dtype=np.int64)\n\nranked = {}\nfor r, uid in enumerate(uids):\n bits = np.unpackbits(packed[r], count=len(item_ids), bitorder='little')\n eligible_items = catalog_items[bits[catalog_cols] == 1]\n ranked[uid] = eligible_items[:100].tolist()\n\ntargets = set(uids)\neligibility = CandidateSet('/task/data/validation_eligible_candidates.npz')\ntruth = validation_truth(eligibility, targets)\nprint('Catalog positive_listens popularity NDCG@10:', score(ranked, truth))\n\n# Now let us compare ranked[uids[0]][:10] with submission.parquet\nsub = pd.read_parquet('/app/solution/submission.parquet')\nsub_u0 = sub[sub.uid == uids[0]].sort_values('rank').item_id.tolist()[:10]\nprint('ranked u0:', ranked[uids[0]][:10])\nprint('sub_u0: ', sub_u0)\n\"", "description": "Compare ranked u0 with sub_u0" } ### Tool output · 2026-09-29T01:26:42.430Z Output: Catalog positive_listens popularity NDCG@10: {'quality_ndcg@10': 0.01071, 'quality_users': 4263} ranked u0: [5862961, 6901374, 5635052, 3542184, 8213481, 906358, 2859641, 3033749, 2651314, 5252571] sub_u0: [5862961, 6901374, 5635052, 3542184, 8213481, 906358, 2859641, 3033749, 2651314, 5252571] Process Group PGID: 2086 ## Step 33 · agent · 2026-09-29T01:26:45.414Z ### Recorded reasoning summary Investigating Discrepancy I'm examining why the ranked lists for `u0` matched, yet the `ndcg_at_k` calculation yielded zero. I'm currently scrutinizing the data types of `uids` in the `z` array to pinpoint the source of this unexpected test result. ### run_shell_command · 2026-09-29T01:26:45.414Z { "command": "python3 -c \"\nimport sys\nsys.path.append('/task')\nimport pandas as pd\nfrom ranking_evaluation import read_ranking, ranked_items, score_rankings, CandidateSet\nfrom evaluate_public import validation_truth\nfrom pathlib import Path\n\nsub = read_ranking(Path('/app/solution/submission.parquet'))\nr = ranked_items(sub)\nprint('type of key in r:', type(list(r.keys())[0]))\neligibility = CandidateSet('/task/data/validation_eligible_candidates.npz')\ntargets = set(sub.uid.unique())\ntruth = validation_truth(eligibility, targets)\nprint('type of key in truth:', type(list(truth.keys())[0]))\nprint('First key in r:', list(r.keys())[0], 'First key in truth:', list(truth.keys())[0])\n\n# Now score_rankings(r, truth)\nprint(score_rankings(r, truth))\n\"", "description": "Check score_rankings on submission.parquet again" } ### Tool output · 2026-09-29T01:26:47.168Z Output: type of key in r: type of key in truth: First key in r: 100 First key in truth: 200 {'quality_ndcg@10': 0.0, 'quality_users': 4263} Process Group PGID: 2106 ## Step 34 · agent · 2026-09-29T01:26:51.143Z ### Recorded reasoning summary Investigating Metric Discrepancies I'm currently analyzing a puzzling difference in scoring metrics. It appears that two functions, `score` and `score_rankings`, are producing distinct results for what I believed were identical inputs. My immediate next step is to inspect the actual rankings (`ranked` and `r`) at a specific index (200) to identify the precise divergence causing this unexpected outcome. ### run_shell_command · 2026-09-29T01:26:51.143Z { "description": "Compare ranked_val with r_sub", "command": "python3 -c \"\nimport sys\nsys.path.append('/task')\nimport pandas as pd\nimport numpy as np\nfrom ranking_evaluation import read_ranking, ranked_items, CandidateSet\nfrom pathlib import Path\n\n# Load ranked from first snippet:\nwith np.load('/task/data/validation_eligible_candidates.npz') as z:\n uids = z['uids'].astype(np.int64)\n item_ids = z['item_ids'].astype(np.int64)\n packed = z['packed_eligible']\n\ncatalog = pd.read_parquet('/task/data/candidate_catalog.parquet')\ncol_map = {item: i for i, item in enumerate(item_ids)}\ncatalog_cols = np.array([col_map[item] for item in catalog.item_id], dtype=np.int32)\ncatalog_items = catalog.item_id.to_numpy(dtype=np.int64)\n\nranked_val = {}\nfor r_idx, uid in enumerate(uids):\n bits = np.unpackbits(packed[r_idx], count=len(item_ids), bitorder='little')\n eligible_items = catalog_items[bits[catalog_cols] == 1]\n ranked_val[uid] = eligible_items[:100].tolist()\n\nsub = read_ranking(Path('/app/solution/submission.parquet'))\nr_sub = ranked_items(sub)\n\nprint('ranked_val[200][:10]:', ranked_val[200][:10])\nprint('r_sub[200][:10]: ', r_sub[200][:10])\nprint('Are all users identical between ranked_val and r_sub?')\ndiff_count = 0\nfor u in uids:\n if ranked_val[u][:10] != r_sub[u][:10]:\n diff_count += 1\nprint('Users with diff top 10:', diff_count)\n\"" } ### Tool output · 2026-09-29T01:26:53.214Z Output: ranked_val[200][:10]: [5862961, 6901374, 3542184, 9378983, 8213481, 906358, 3033749, 2651314, 5252571, 5436843] r_sub[200][:10]: [5862961, 6901374, 3542184, 9378983, 8213481, 906358, 3033749, 2651314, 5252571, 5436843] Are all users identical between ranked_val and r_sub? Users with diff top 10: 806 Process Group PGID: 2126 ## Step 35 · agent · 2026-09-29T01:26:57.183Z ### Recorded reasoning summary Analyzing Score Discrepancies I'm currently scrutinizing a peculiar difference between `ranked_val` and `r_sub` in my evaluation. The `quality_ndcg@10` score, which I expected to be consistent, is showing an unexpected `0.01071`. I'm digging into why `score(ranked_val, truth)` differs from `score(r_sub, truth)` to pinpoint the source of this anomaly. ### run_shell_command · 2026-09-29T01:26:57.183Z { "description": "Evaluate both ranked_val and r_sub on truth", "command": "python3 -c \"\nimport sys\nsys.path.append('/task')\nimport pandas as pd\nimport numpy as np\nfrom ranking_evaluation import read_ranking, ranked_items, score_rankings, CandidateSet\nfrom evaluate_public import validation_truth\nfrom pathlib import Path\n\nwith np.load('/task/data/validation_eligible_candidates.npz') as z:\n uids = z['uids'].astype(np.int64)\n item_ids = z['item_ids'].astype(np.int64)\n packed = z['packed_eligible']\n\ncatalog = pd.read_parquet('/task/data/candidate_catalog.parquet')\ncol_map = {item: i for i, item in enumerate(item_ids)}\ncatalog_cols = np.array([col_map[item] for item in catalog.item_id], dtype=np.int32)\ncatalog_items = catalog.item_id.to_numpy(dtype=np.int64)\n\nranked_val = {}\nfor r_idx, uid in enumerate(uids):\n bits = np.unpackbits(packed[r_idx], count=len(item_ids), bitorder='little')\n eligible_items = catalog_items[bits[catalog_cols] == 1]\n ranked_val[uid] = eligible_items[:100].tolist()\n\ntargets = set(uids)\neligibility = CandidateSet('/task/data/validation_eligible_candidates.npz')\ntruth = validation_truth(eligibility, targets)\n\nprint('Score ranked_val:', score_rankings(ranked_val, truth))\n\nsub = read_ranking(Path('/app/solution/submission.parquet'))\nr_sub = ranked_items(sub)\nprint('Score r_sub: ', score_rankings(r_sub, truth))\n\"" } ### Tool output · 2026-09-29T01:26:59.985Z Output: Score ranked_val: {'quality_ndcg@10': 0.01071, 'quality_users': 4263} Score r_sub: {'quality_ndcg@10': 0.0, 'quality_users': 4263} Process Group PGID: 2143 ## Step 36 · agent · 2026-09-29T01:27:06.202Z ### Recorded reasoning summary Investigating Zero Values I'm puzzled by a score of 0.01071 for `ranked_val` while `r_sub` registers as 0.0. I'm delving into the extraction logic for `r_sub` from `sub` and examining the `ranked_items` function within `ranking_evaluation.py` to understand this discrepancy. ### run_shell_command · 2026-09-29T01:27:06.202Z { "description": "Debug difference between ranked_val and r_sub", "command": "python3 -c \"\nimport sys\nsys.path.append('/task')\nimport pandas as pd\nimport numpy as np\nfrom ranking_evaluation import read_ranking, ranked_items, CandidateSet, ndcg_at_k\nfrom evaluate_public import validation_truth\nfrom pathlib import Path\n\nwith np.load('/task/data/validation_eligible_candidates.npz') as z:\n uids = z['uids'].astype(np.int64)\n item_ids = z['item_ids'].astype(np.int64)\n packed = z['packed_eligible']\n\ncatalog = pd.read_parquet('/task/data/candidate_catalog.parquet')\ncol_map = {item: i for i, item in enumerate(item_ids)}\ncatalog_cols = np.array([col_map[item] for item in catalog.item_id], dtype=np.int32)\ncatalog_items = catalog.item_id.to_numpy(dtype=np.int64)\n\nranked_val = {}\nfor r_idx, uid in enumerate(uids):\n bits = np.unpackbits(packed[r_idx], count=len(item_ids), bitorder='little')\n eligible_items = catalog_items[bits[catalog_cols] == 1]\n ranked_val[uid] = eligible_items[:100].tolist()\n\ntargets = set(uids)\neligibility = CandidateSet('/task/data/validation_eligible_candidates.npz')\ntruth = validation_truth(eligibility, targets)\n\nsub = read_ranking(Path('/app/solution/submission.parquet'))\nr_sub = ranked_items(sub)\n\n# Find a user where ndcg_at_k(ranked_val[uid], truth[uid], 10) > 0\nfor uid in truth:\n s_val = ndcg_at_k(ranked_val[uid], truth[uid], 10)\n s_sub = ndcg_at_k(r_sub[uid], truth[uid], 10)\n if s_val > 0:\n print(f'User {uid}: val_score={s_val}, sub_score={s_sub}')\n print(' truth:', truth[uid])\n print(' val top 10:', ranked_val[uid][:10])\n print(' sub top 10:', r_sub[uid][:10])\n break\n\"" } ### Tool output · 2026-09-29T01:27:08.809Z Output: User 1400: val_score=0.09653121191524809, sub_score=0.0 truth: {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} val top 10: [5862961, 6901374, 5635052, 3542184, 9378983, 8213481, 906358, 2859641, 3033749, 2651314] sub top 10: [5635052, 8213481, 906358, 2859641, 3033749, 2651314, 6853370, 3418493, 5927996, 6155741] Process Group PGID: 2163 ## Step 37 · agent · 2026-09-29T01:27:14.904Z ### Recorded reasoning summary Confirming Accuracy Now I'm reviewing the top 10 values and cross-referencing them with the ground truth. It's promising to see several identified values, like 6901374 and 9378983, present in the truth set. I'm specifically noting their original rankings within the top 10 list. ### Recorded reasoning summary Uncovering Eligibility Logic I've realized the core issue: items listened to during validation are correctly excluded from the test set's eligible candidates. This explains the zero hits when comparing the submission to validation truth, as the test set was inherently designed to omit these already-heard items. ### run_shell_command · 2026-09-29T01:27:14.904Z { "description": "Evaluate val_baseline.parquet with evaluate_public.py", "command": "python3 -c \"\nimport sys\nsys.path.append('/task')\nimport pandas as pd\nimport numpy as np\n\nwith np.load('/task/data/validation_eligible_candidates.npz') as z:\n uids = z['uids'].astype(np.int64)\n item_ids = z['item_ids'].astype(np.int64)\n packed = z['packed_eligible']\n\ncatalog = pd.read_parquet('/task/data/candidate_catalog.parquet')\ncol_map = {item: i for i, item in enumerate(item_ids)}\ncatalog_cols = np.array([col_map[item] for item in catalog.item_id], dtype=np.int32)\ncatalog_items = catalog.item_id.to_numpy(dtype=np.int64)\n\nrecords = []\nfor r, uid in enumerate(uids):\n bits = np.unpackbits(packed[r], count=len(item_ids), bitorder='little')\n eligible_items = catalog_items[bits[catalog_cols] == 1][:100]\n for rank_idx, item in enumerate(eligible_items, 1):\n records.append((uid, item, rank_idx))\n\ndf = pd.DataFrame(records, columns=['uid', 'item_id', 'rank'])\ndf.to_parquet('/app/solution/val_baseline.parquet', index=False)\n\" && python3 /task/evaluate_public.py /app/solution/val_baseline.parquet" } ### Tool output · 2026-09-29T01:27:19.965Z Output: { "model": "/app/solution/val_baseline.parquet", "scores": { "quality_ndcg@10": 0.01071, "quality_users": 4263 } } Process Group PGID: 2183 ## Step 38 · agent · 2026-09-29T01:27:22.388Z ### run_shell_command · 2026-09-29T01:27:22.388Z { "description": "Clean up temporary file", "command": "rm -f /app/solution/val_baseline.parquet" } ### Tool output · 2026-09-29T01:27:22.404Z Output: (empty) Process Group PGID: 2218 ## Step 39 · agent · 2026-09-29T01:27:30.893Z ### update_topic · 2026-09-29T01:27:30.893Z { "strategic_intent": "Design and experiment with personalized recommendation models on validation.", "summary": "Completed initial exploration and baseline validation. We verified the evaluation pipeline, understanding of eligibility masks, and confirmed candidate catalog baseline NDCG@10 of 0.01071. Now we transition to modeling: researching item-item collaborative filtering, matrix factorization, and user-item interactions to significantly boost discovery NDCG@10.", "title": "Personalized Recommendation Modeling & Validation" } ### Tool output · 2026-09-29T01:27:30.896Z Current topic: "Personalized Recommendation Modeling & Validation" Topic summary: Completed initial exploration and baseline validation. We verified the evaluation pipeline, understanding of eligibility masks, and confirmed candidate catalog baseline NDCG@10 of 0.01071. Now we transition to modeling: researching item-item collaborative filtering, matrix factorization, and user-item interactions to significantly boost discovery NDCG@10. Strategic Intent: Design and experiment with personalized recommendation models on validation. ## Step 40 · agent · 2026-09-29T01:27:44.130Z ### Recorded reasoning summary Analyzing Recommendation Drivers My current focus is on dissecting the core components of an effective recommendation engine for our specific scenario. I'm meticulously examining user behavior patterns, particularly their listening history and associated timestamps, to identify key drivers for generating relevant suggestions. ### Recorded reasoning summary Refining Feature Importance I'm now delving into how specific user interactions, such as organic listens versus explicit likes and dislikes, will influence recommendation grades. My aim is to quantify their relative impact for NDCG@10, particularly focusing on achieving grade 3 and grade 1 organic outcomes within the week. ### Recorded reasoning summary Evaluating Matrix Inversion I'm currently assessing the computational feasibility of inverting a $30000 \times 30000$ matrix, as required by the EASE model. This large-scale inversion could be a bottleneck, and I'm exploring alternatives or optimizations if it proves impractical. ### Recorded reasoning summary Exploring Alternative Strategies I'm shifting focus to computational efficiency. Instead of direct matrix inversion for EASE, I'm now testing item-item similarity methods, including cosine and BM25. I'm also evaluating truncated SVD for latent factor models, as it shows potential for significantly faster computation on validation sets. ### run_shell_command · 2026-09-29T01:27:44.130Z { "command": "python3 -c \"\nimport sys\nsys.path.append('/task')\nimport time\nimport numpy as np\nimport pandas as pd\nimport scipy.sparse as sp\nfrom sklearn.decomposition import TruncatedSVD\nfrom evaluate_public import score, validation_truth\nfrom ranking_evaluation import CandidateSet\n\nt0 = time.time()\nprint('Loading data...')\n\n# Load target users and candidates\ntarget_users = pd.read_parquet('/task/data/target_users.parquet')\nuids = target_users.uid.to_numpy(dtype=np.int64)\nuid_to_idx = {u: i for i, u in enumerate(uids)}\n\ncat = pd.read_parquet('/task/data/candidate_catalog.parquet')\nitem_ids = cat.item_id.to_numpy(dtype=np.int64)\nitem_to_idx = {it: i for i, it in enumerate(item_ids)}\n\n# Load validation eligibility\nwith np.load('/task/data/validation_eligible_candidates.npz') as z:\n val_uids = z['uids'].astype(np.int64)\n val_items = z['item_ids'].astype(np.int64)\n val_packed = z['packed_eligible']\nval_col_map = np.array([uid_to_idx[u] for u in val_uids])\nval_item_map = np.array([item_to_idx[it] for it in val_items])\n\neligibility = CandidateSet('/task/data/validation_eligible_candidates.npz')\ntruth = validation_truth(eligibility, set(val_uids))\nprint(f'Truth loaded ({len(truth)} users). Time: {time.time() - t0:.2f}s')\n\n# Read train listens\nimport pyarrow.parquet as pq\ntable = pq.read_table('/task/data/train_listens.parquet', \n columns=['uid', 'item_id', 'timestamp', 'played_ratio_pct', 'is_organic'])\ndf_listens = table.to_pandas()\nprint(f'Train listens loaded: {len(df_listens)}. Time: {time.time() - t0:.2f}s')\n\n# Filter to target users and catalog items\ndf_listens = df_listens[df_listens.uid.isin(uid_to_idx) & df_listens.item_id.isin(item_to_idx)]\nprint(f'Filtered listens: {len(df_listens)}. Time: {time.time() - t0:.2f}s')\n\"", "description": "Load and filter train listens in python" } ### Tool output · 2026-09-29T01:27:56.795Z Output: Loading data... Truth loaded (4263 users). Time: 0.69s Train listens loaded: 43484159. Time: 1.17s Filtered listens: 31062540. Time: 11.08s Process Group PGID: 2221 ## Step 41 · agent · 2026-09-29T01:28:00.772Z ### Recorded reasoning summary Optimizing Data Filtering I'm analyzing the performance of filtering 43 million rows, which currently takes about 10 seconds. I've identified the `df_listens.uid.isin(uid_to_idx)` operation as a key area. Given that the unique UIDs in `train_listens` (9,238) largely overlap with the target users (8,456), a significant portion, approximately 99.4%, of the rows are already relevant. I'm now exploring strategies to further accelerate this process. ### run_shell_command · 2026-09-29T01:28:00.772Z { "description": "Benchmark fast PyArrow filtering", "command": "python3 -c \"\nimport time\nimport numpy as np\nimport pyarrow.parquet as pq\nimport pyarrow.compute as pc\nimport pandas as pd\n\nt0 = time.time()\ncat = pd.read_parquet('/task/data/candidate_catalog.parquet')\nitem_set = pyarrow.compute.SetLookupOptions(value_set=pyarrow.array(cat.item_id.to_numpy()))\n\ntable = pq.read_table('/task/data/train_listens.parquet', columns=['uid', 'item_id', 'timestamp', 'played_ratio_pct', 'is_organic'])\nt1 = time.time()\nprint(f'Read table: {t1 - t0:.2f}s')\n\n# Filter using pc.is_in\nmask = pc.is_in(table['item_id'], options=item_set)\nt2 = time.time()\nprint(f'pc.is_in took: {t2 - t1:.2f}s, matches: {pc.sum(mask).as_py()}')\n\nfiltered = table.filter(mask)\nt3 = time.time()\nprint(f'Filter table took: {t3 - t2:.2f}s, len={len(filtered)}')\n\"" } ### Tool output · 2026-09-29T01:28:01.533Z Output: Traceback (most recent call last): File "", line 10, in NameError: name 'pyarrow' is not defined Exit Code: 1 Process Group PGID: 2250 ## Step 42 · agent · 2026-09-29T01:28:09.398Z ### run_shell_command · 2026-09-29T01:28:09.398Z { "description": "Test fast PyArrow filtering", "command": "python3 -c \"\nimport time\nimport numpy as np\nimport pyarrow as pa\nimport pyarrow.parquet as pq\nimport pyarrow.compute as pc\nimport pandas as pd\n\nt0 = time.time()\ncat = pd.read_parquet('/task/data/candidate_catalog.parquet')\nitem_set = pc.SetLookupOptions(value_set=pa.array(cat.item_id.to_numpy()))\n\ntable = pq.read_table('/task/data/train_listens.parquet', columns=['uid', 'item_id', 'timestamp', 'played_ratio_pct', 'is_organic'])\nt1 = time.time()\nprint(f'Read table: {t1 - t0:.2f}s')\n\nmask = pc.is_in(table['item_id'], options=item_set)\nt2 = time.time()\nprint(f'pc.is_in took: {t2 - t1:.2f}s, matches: {pc.sum(mask).as_py()}')\n\nfiltered = table.filter(mask)\nt3 = time.time()\nprint(f'Filter table took: {t3 - t2:.2f}s, len={len(filtered)}')\n\"" } ### Tool output · 2026-09-29T01:28:11.623Z Output: Read table: 0.41s pc.is_in took: 0.56s, matches: 31249613 Filter table took: 0.41s, len=31249613 Process Group PGID: 2267 ## Step 43 · agent · 2026-09-29T01:28:22.279Z ### Recorded reasoning summary Optimizing Data Processing I've successfully optimized the data reading and filtering stages, achieving a significant 10x speedup. My next steps involve rigorously testing the construction of the sparse user-item interaction matrix, the SVD computations (both Truncated and randomized), candidate score prediction, and finally, evaluating NDCG@10 on the validation data. ### run_shell_command · 2026-09-29T01:28:22.279Z { "description": "Test TruncatedSVD model on validation", "command": "python3 -c \"\nimport sys\nsys.path.append('/task')\nimport time\nimport numpy as np\nimport pyarrow as pa\nimport pyarrow.parquet as pq\nimport pyarrow.compute as pc\nimport pandas as pd\nimport scipy.sparse as sp\nfrom sklearn.decomposition import TruncatedSVD\nfrom evaluate_public import score, validation_truth\nfrom ranking_evaluation import CandidateSet\n\nt0 = time.time()\ntarget_users = pd.read_parquet('/task/data/target_users.parquet')\nuids = target_users.uid.to_numpy(dtype=np.int64)\nuid_to_idx = {u: i for i, u in enumerate(uids)}\n\ncat = pd.read_parquet('/task/data/candidate_catalog.parquet')\ncat_items = cat.item_id.to_numpy(dtype=np.int64)\nitem_to_idx = {it: i for i, it in enumerate(cat_items)}\n\n# Load validation eligibility\nwith np.load('/task/data/validation_eligible_candidates.npz') as z:\n val_uids = z['uids'].astype(np.int64)\n val_items = z['item_ids'].astype(np.int64)\n val_packed = z['packed_eligible']\n\n# Map val_items index to cat_items index\nval_to_cat = np.array([item_to_idx[it] for it in val_items])\n\neligibility = CandidateSet('/task/data/validation_eligible_candidates.npz')\ntruth = validation_truth(eligibility, set(val_uids))\n\n# Read train listens\nitem_set = pc.SetLookupOptions(value_set=pa.array(cat_items))\ntable = pq.read_table('/task/data/train_listens.parquet', columns=['uid', 'item_id', 'timestamp', 'played_ratio_pct', 'is_organic'])\nfiltered = table.filter(pc.is_in(table['item_id'], options=item_set))\n\nu_arr = filtered['uid'].to_numpy()\nit_arr = filtered['item_id'].to_numpy()\npct_arr = filtered['played_ratio_pct'].to_numpy()\norg_arr = filtered['is_organic'].to_numpy()\nts_arr = filtered['timestamp'].to_numpy()\n\n# Map to matrix indices\n# Only keep uids that are in uid_to_idx\nu_mask = np.isin(u_arr, uids)\nu_arr = u_arr[u_mask]\nit_arr = it_arr[u_mask]\npct_arr = pct_arr[u_mask]\norg_arr = org_arr[u_mask]\nts_arr = ts_arr[u_mask]\n\nu_idx = np.vectorize(uid_to_idx.get)(u_arr)\nit_idx = np.vectorize(item_to_idx.get)(it_arr)\n\n# Weights: base weight for completion + bonus for organic + time decay\n# Max timestamp in train is 24786800\nCUTOFF = 24786800.0\n# Half-life of 60 days (60 * 86400 = 5,184,000s)\ntime_weight = np.exp((ts_arr - CUTOFF) / (60.0 * 86400.0))\n\n# Positive listen weight: >=50%: 1.0, >=80%: 2.0, organic bonus: +1.0\nlisten_weight = np.where(pct_arr >= 80, 2.0, np.where(pct_arr >= 50, 1.0, 0.2))\nlisten_weight += np.where(org_arr == 1, 1.0, 0.0)\nweights = listen_weight * time_weight\n\nprint(f'Building sparse matrix, elapsed: {time.time() - t0:.2f}s')\nX = sp.csr_matrix((weights, (u_idx, it_idx)), shape=(len(uids), len(cat_items)))\nprint(f'Matrix shape: {X.shape}, nnz: {X.nnz}')\n\n# Add likes data\nlikes = pd.read_parquet('/task/data/train_likes.parquet')\nlikes = likes[likes.uid.isin(uid_to_idx) & likes.item_id.isin(item_to_idx)]\nl_u = np.vectorize(uid_to_idx.get)(likes.uid.to_numpy())\nl_it = np.vectorize(item_to_idx.get)(likes.item_id.to_numpy())\nl_ts = likes.timestamp.to_numpy()\nl_org = likes.is_organic.to_numpy()\nl_tw = np.exp((l_ts - CUTOFF) / (60.0 * 86400.0))\nl_w = (np.where(l_org == 1, 5.0, 3.0)) * l_tw\n\nX_likes = sp.csr_matrix((l_w, (l_u, l_it)), shape=(len(uids), len(cat_items)))\nX = X + X_likes\n\nprint(f'Total matrix nnz: {X.nnz}, elapsed: {time.time() - t0:.2f}s')\n\n# Run SVD\nprint('Computing SVD...')\nsvd = TruncatedSVD(n_components=64, random_state=42)\nU = svd.fit_transform(X) # (8456, 64)\nV = svd.components_ # (64, 30000)\nprint(f'SVD explained variance ratio sum: {svd.explained_variance_ratio_.sum():.4f}, elapsed: {time.time() - t0:.2f}s')\n\n# Predict scores: S = U @ V\n# Let's score target users\nprint('Scoring users and evaluating...')\nranked = {}\n# Process in batches of 500 users\nBATCH_SIZE = 500\nfor start in range(0, len(val_uids), BATCH_SIZE):\n end = min(start + BATCH_SIZE, len(val_uids))\n batch_uids = val_uids[start:end]\n batch_u_idx = [uid_to_idx[u] for u in batch_uids]\n \n # Compute scores for this batch: (batch_size, 30000)\n batch_scores = U[batch_u_idx] @ V\n \n # For each user, get top 100 eligible items\n for i, (r_idx, uid) in enumerate(zip(range(start, end), batch_uids)):\n bits = np.unpackbits(val_packed[r_idx], count=len(val_items), bitorder='little')\n # Mask out ineligible items\n user_scores = batch_scores[i].copy()\n # Eligible mask in terms of cat_items:\n # val_items mapped to cat_items:\n # Ineligible items get -inf\n mask = np.ones(len(cat_items), dtype=bool)\n # where bits == 0, mark ineligible\n mask[val_to_cat[bits == 0]] = False\n user_scores[~mask] = -1e9\n \n # Top 100\n top100_idx = np.argpartition(user_scores, -100)[-100:]\n top100_sorted = top100_idx[np.argsort(-user_scores[top100_idx])]\n ranked[uid] = cat_items[top100_sorted].tolist()\n\nres = score(ranked, truth)\nprint(f'TruncatedSVD(64) NDCG@10: {res}')\n\"" } ### Tool output · 2026-09-29T01:28:46.031Z Output: Building sparse matrix, elapsed: 12.91s Matrix shape: (8456, 30000), nnz: 7868664 Total matrix nnz: 7876748, elapsed: 14.76s Computing SVD... SVD explained variance ratio sum: 0.4563, elapsed: 19.15s Scoring users and evaluating... TruncatedSVD(64) NDCG@10: {'quality_ndcg@10': 0.021702, 'quality_users': 4263} Process Group PGID: 2289 ## Step 44 · agent · 2026-09-29T01:28:56.571Z ### Recorded reasoning summary Improving SVD Performance My latest experiments with TruncatedSVD, specifically using 64 components, yielded impressive results, achieving a score of 0.021702, more than double the baseline. This significant improvement was also realized in a remarkably short time, just 19 seconds. I'm now eager to explore the impact of varying the number of components used in the SVD process to further refine these outcomes. ### run_shell_command · 2026-09-29T01:28:56.571Z { "description": "Evaluate SVD(128) raw vs cosine", "command": "python3 -c \"\nimport sys\nsys.path.append('/task')\nimport time\nimport numpy as np\nimport pyarrow as pa\nimport pyarrow.parquet as pq\nimport pyarrow.compute as pc\nimport pandas as pd\nimport scipy.sparse as sp\nfrom sklearn.decomposition import TruncatedSVD\nfrom evaluate_public import score, validation_truth\nfrom ranking_evaluation import CandidateSet\n\ntarget_users = pd.read_parquet('/task/data/target_users.parquet')\nuids = target_users.uid.to_numpy(dtype=np.int64)\nuid_to_idx = {u: i for i, u in enumerate(uids)}\n\ncat = pd.read_parquet('/task/data/candidate_catalog.parquet')\ncat_items = cat.item_id.to_numpy(dtype=np.int64)\nitem_to_idx = {it: i for i, it in enumerate(cat_items)}\n\nwith np.load('/task/data/validation_eligible_candidates.npz') as z:\n val_uids = z['uids'].astype(np.int64)\n val_items = z['item_ids'].astype(np.int64)\n val_packed = z['packed_eligible']\nval_to_cat = np.array([item_to_idx[it] for it in val_items])\n\neligibility = CandidateSet('/task/data/validation_eligible_candidates.npz')\ntruth = validation_truth(eligibility, set(val_uids))\n\n# Fast matrix builder\nitem_set = pc.SetLookupOptions(value_set=pa.array(cat_items))\ntable = pq.read_table('/task/data/train_listens.parquet', columns=['uid', 'item_id', 'timestamp', 'played_ratio_pct', 'is_organic'])\nfiltered = table.filter(pc.is_in(table['item_id'], options=item_set))\n\nu_arr = filtered['uid'].to_numpy()\nit_arr = filtered['item_id'].to_numpy()\npct_arr = filtered['played_ratio_pct'].to_numpy()\norg_arr = filtered['is_organic'].to_numpy()\nts_arr = filtered['timestamp'].to_numpy()\n\nu_mask = np.isin(u_arr, uids)\nu_idx = np.fromiter((uid_to_idx[x] for x in u_arr[u_mask]), dtype=np.int32, count=int(u_mask.sum()))\nit_idx = np.fromiter((item_to_idx[x] for x in it_arr[u_mask]), dtype=np.int32, count=int(u_mask.sum()))\n\nCUTOFF = 24786800.0\n# Try half-life of 45 days\ntime_weight = np.exp((ts_arr[u_mask] - CUTOFF) / (45.0 * 86400.0))\nlisten_weight = np.where(pct_arr[u_mask] >= 80, 2.0, np.where(pct_arr[u_mask] >= 50, 1.0, 0.2))\nlisten_weight += np.where(org_arr[u_mask] == 1, 1.5, 0.0)\nweights = listen_weight * time_weight\n\nX = sp.csr_matrix((weights, (u_idx, it_idx)), shape=(len(uids), len(cat_items)))\n\nlikes = pd.read_parquet('/task/data/train_likes.parquet')\nlikes = likes[likes.uid.isin(uid_to_idx) & likes.item_id.isin(item_to_idx)]\nl_u = np.fromiter((uid_to_idx[x] for x in likes.uid), dtype=np.int32, count=len(likes))\nl_it = np.fromiter((item_to_idx[x] for x in likes.item_id), dtype=np.int32, count=len(likes))\nl_tw = np.exp((likes.timestamp.to_numpy() - CUTOFF) / (45.0 * 86400.0))\nl_w = np.where(likes.is_organic.to_numpy() == 1, 5.0, 3.0) * l_tw\nX = X + sp.csr_matrix((l_w, (l_u, l_it)), shape=(len(uids), len(cat_items)))\n\n# Test SVD with 128 components\nprint('Computing SVD(128)...')\nsvd = TruncatedSVD(n_components=128, random_state=42)\nU = svd.fit_transform(X)\nV = svd.components_\n\ndef eval_preds(U_mat, V_mat, name):\n ranked = {}\n BATCH = 500\n for start in range(0, len(val_uids), BATCH):\n end = min(start + BATCH, len(val_uids))\n batch_uids = val_uids[start:end]\n batch_scores = U_mat[[uid_to_idx[u] for u in batch_uids]] @ V_mat\n for i, (r_idx, uid) in enumerate(zip(range(start, end), batch_uids)):\n bits = np.unpackbits(val_packed[r_idx], count=len(val_items), bitorder='little')\n user_scores = batch_scores[i].copy()\n mask = np.ones(len(cat_items), dtype=bool)\n mask[val_to_cat[bits == 0]] = False\n user_scores[~mask] = -1e9\n top100_idx = np.argpartition(user_scores, -100)[-100:]\n top100_sorted = top100_idx[np.argsort(-user_scores[top100_idx])]\n ranked[uid] = cat_items[top100_sorted].tolist()\n print(f'{name} NDCG@10: {score(ranked, truth)}')\n\neval_preds(U, V, 'SVD(128) raw')\n\n# Normalized embeddings (cosine similarity)\nU_norm = U / (np.linalg.norm(U, axis=1, keepdims=True) + 1e-9)\nV_norm = V / (np.linalg.norm(V, axis=0, keepdims=True) + 1e-9)\neval_preds(U_norm, V_norm, 'SVD(128) cosine')\n\"" } ### Tool output · 2026-09-29T01:29:27.089Z Output: Computing SVD(128)... SVD(128) raw NDCG@10: {'quality_ndcg@10': 0.020271, 'quality_users': 4263} SVD(128) cosine NDCG@10: {'quality_ndcg@10': 0.005956, 'quality_users': 4263} Process Group PGID: 2318 ## Step 45 · agent · 2026-09-29T01:29:40.511Z ### Recorded reasoning summary Exploring SVD Performance I'm observing an intriguing trend where the raw SVD score for 128 dimensions is lower than for 64. This suggests potential overfitting or that a higher dimensionality isn't necessarily improving the raw metric. I'm now turning my attention to item-item collaborative filtering approaches like cosine and BM25 to see how they perform in comparison and if they reveal different insights into item similarity. ### Recorded reasoning summary Investigating Item-Item CF I'm delving into the mechanics of item-item collaborative filtering, specifically exploring how TF-IDF or BM25 normalization impacts the item similarity matrix. The current focus is on optimizing the calculation of predicted scores to avoid materializing a massive similarity matrix. I'm evaluating whether computing user-user similarities first offers a more memory-efficient pathway. ### run_shell_command · 2026-09-29T01:29:40.511Z { "description": "Test User-User Collaborative Filtering", "command": "python3 -c \"\nimport sys\nsys.path.append('/task')\nimport time\nimport numpy as np\nimport scipy.sparse as sp\nfrom sklearn.preprocessing import normalize\nfrom evaluate_public import score, validation_truth\nfrom ranking_evaluation import CandidateSet\nimport pandas as pd\n\ntarget_users = pd.read_parquet('/task/data/target_users.parquet')\nuids = target_users.uid.to_numpy(dtype=np.int64)\nuid_to_idx = {u: i for i, u in enumerate(uids)}\n\ncat = pd.read_parquet('/task/data/candidate_catalog.parquet')\ncat_items = cat.item_id.to_numpy(dtype=np.int64)\nitem_to_idx = {it: i for i, it in enumerate(cat_items)}\n\nwith np.load('/task/data/validation_eligible_candidates.npz') as z:\n val_uids = z['uids'].astype(np.int64)\n val_items = z['item_ids'].astype(np.int64)\n val_packed = z['packed_eligible']\nval_to_cat = np.array([item_to_idx[it] for it in val_items])\n\neligibility = CandidateSet('/task/data/validation_eligible_candidates.npz')\ntruth = validation_truth(eligibility, set(val_uids))\n\n# Fast load likes + recent listens\n# Let us test loading likes and recent listens\nlikes = pd.read_parquet('/task/data/train_likes.parquet')\nlikes = likes[likes.uid.isin(uid_to_idx) & likes.item_id.isin(item_to_idx)]\nl_u = np.fromiter((uid_to_idx[x] for x in likes.uid), dtype=np.int32, count=len(likes))\nl_it = np.fromiter((item_to_idx[x] for x in likes.item_id), dtype=np.int32, count=len(likes))\n\nCUTOFF = 24786800.0\n# Likes weights\nl_tw = np.exp((likes.timestamp.to_numpy() - CUTOFF) / (60.0 * 86400.0))\nl_w = np.where(likes.is_organic.to_numpy() == 1, 5.0, 3.0) * l_tw\n\n# Let us also load listens from last 90 days\nimport pyarrow.parquet as pq\nimport pyarrow.compute as pc\nimport pyarrow as pa\n\nitem_set = pc.SetLookupOptions(value_set=pa.array(cat_items))\n# 90 days before cutoff = CUTOFF - 90 * 86400 = 17010800\ntable = pq.read_table('/task/data/train_listens.parquet', \n filters=[('timestamp', '>=', 17010800)],\n columns=['uid', 'item_id', 'timestamp', 'played_ratio_pct', 'is_organic'])\nfiltered = table.filter(pc.is_in(table['item_id'], options=item_set))\n\nu_arr = filtered['uid'].to_numpy()\nit_arr = filtered['item_id'].to_numpy()\npct_arr = filtered['played_ratio_pct'].to_numpy()\norg_arr = filtered['is_organic'].to_numpy()\nts_arr = filtered['timestamp'].to_numpy()\n\nu_mask = np.isin(u_arr, uids)\nu_idx = np.fromiter((uid_to_idx[x] for x in u_arr[u_mask]), dtype=np.int32, count=int(u_mask.sum()))\nit_idx = np.fromiter((item_to_idx[x] for x in it_arr[u_mask]), dtype=np.int32, count=int(u_mask.sum()))\n\ntime_weight = np.exp((ts_arr[u_mask] - CUTOFF) / (60.0 * 86400.0))\nlisten_weight = np.where(pct_arr[u_mask] >= 80, 2.0, np.where(pct_arr[u_mask] >= 50, 1.0, 0.2))\nlisten_weight += np.where(org_arr[u_mask] == 1, 1.0, 0.0)\nweights = listen_weight * time_weight\n\nX_listens = sp.csr_matrix((weights, (u_idx, it_idx)), shape=(len(uids), len(cat_items)))\nX_likes = sp.csr_matrix((l_w, (l_u, l_it)), shape=(len(uids), len(cat_items)))\nX = X_listens + X_likes\n\nprint(f'Matrix built, nnz={X.nnz}')\n\n# User-User CF:\n# Normalize rows of X (user vectors)\nX_norm = normalize(X, norm='l2', axis=1) # (8456, 30000)\n\nt0 = time.time()\n# Compute user-user similarity: S_user = X_norm @ X_norm.T (8456 x 8456)\nprint('Computing user-user similarity...')\nS_user = (X_norm @ X_norm.T).toarray()\n# Zero diagonal\nnp.fill_diagonal(S_user, 0.0)\nprint(f'User-user similarity computed: {time.time() - t0:.2f}s, shape={S_user.shape}')\n\n# Power transform on similarities to sharpen neighbors\n# S_user = np.power(np.maximum(S_user, 0), 2.0)\n# Optional: keep top-K neighbors per user\nK = 200\nprint(f'Keeping top-{K} neighbors...')\nfor i in range(len(uids)):\n row = S_user[i]\n if len(row) > K:\n thresh = np.partition(row, -K)[-K]\n row[row < thresh] = 0.0\n\n# Predicted scores: R = S_user @ X\nprint('Computing predicted scores...')\nt1 = time.time()\n# Compute in batches\nranked = {}\nBATCH = 500\nfor start in range(0, len(val_uids), BATCH):\n end = min(start + BATCH, len(val_uids))\n batch_uids = val_uids[start:end]\n batch_indices = [uid_to_idx[u] for u in batch_uids]\n \n # Dense @ Sparse -> Dense (batch_size, 30000)\n batch_scores = S_user[batch_indices] @ X # or S_user @ X.toarray()\n \n for i, (r_idx, uid) in enumerate(zip(range(start, end), batch_uids)):\n bits = np.unpackbits(val_packed[r_idx], count=len(val_items), bitorder='little')\n user_scores = np.asarray(batch_scores[i]).ravel().copy()\n mask = np.ones(len(cat_items), dtype=bool)\n mask[val_to_cat[bits == 0]] = False\n user_scores[~mask] = -1e9\n top100_idx = np.argpartition(user_scores, -100)[-100:]\n top100_sorted = top100_idx[np.argsort(-user_scores[top100_idx])]\n ranked[uid] = cat_items[top100_sorted].tolist()\n\nprint(f'Scoring finished: {time.time() - t1:.2f}s')\nres = score(ranked, truth)\nprint(f'User-User CF (top-{K}) NDCG@10: {res}')\n\"" } ### Tool output · 2026-09-29T01:30:15.747Z Output: Matrix built, nnz=4395173 Computing user-user similarity... User-user similarity computed: 5.07s, shape=(8456, 8456) Keeping top-200 neighbors... Computing predicted scores... Scoring finished: 21.51s User-User CF (top-200) NDCG@10: {'quality_ndcg@10': 0.027614, 'quality_users': 4263} Process Group PGID: 2347 ## Step 46 · agent · 2026-09-29T01:30:32.281Z ### Recorded reasoning summary Analyzing User-User CF Success I'm thrilled to report a significant leap in our User-User CF performance for top-200 recommendations, hitting an NDCG@10 of 0.027614. This represents almost a threefold improvement over the baseline. My next step is to dissect the underlying reasons for this remarkable effectiveness. ### Recorded reasoning summary Refining Similarity Metrics I'm exploring ways to enhance our similarity calculations. Specifically, I’m investigating applying power exponents to the similarity matrix and testing various $K$ values. Additionally, I'm considering incorporating IDF weighting for items to better reflect niche taste shared between users, alongside a deeper dive into how different user interaction weights like likes, dislikes, and organic plays impact performance. ### run_shell_command · 2026-09-29T01:30:32.281Z { "description": "Grid search alpha and K on User-User CF with IDF", "command": "python3 -c \"\nimport sys\nsys.path.append('/task')\nimport time\nimport numpy as np\nimport scipy.sparse as sp\nfrom sklearn.preprocessing import normalize\nfrom evaluate_public import score, validation_truth\nfrom ranking_evaluation import CandidateSet\nimport pandas as pd\nimport pyarrow.parquet as pq\nimport pyarrow.compute as pc\nimport pyarrow as pa\n\ntarget_users = pd.read_parquet('/task/data/target_users.parquet')\nuids = target_users.uid.to_numpy(dtype=np.int64)\nuid_to_idx = {u: i for i, u in enumerate(uids)}\n\ncat = pd.read_parquet('/task/data/candidate_catalog.parquet')\ncat_items = cat.item_id.to_numpy(dtype=np.int64)\nitem_to_idx = {it: i for i, it in enumerate(cat_items)}\n\nwith np.load('/task/data/validation_eligible_candidates.npz') as z:\n val_uids = z['uids'].astype(np.int64)\n val_items = z['item_ids'].astype(np.int64)\n val_packed = z['packed_eligible']\nval_to_cat = np.array([item_to_idx[it] for it in val_items])\n\neligibility = CandidateSet('/task/data/validation_eligible_candidates.npz')\ntruth = validation_truth(eligibility, set(val_uids))\n\nCUTOFF = 24786800.0\n\n# 1. Load likes\nlikes = pd.read_parquet('/task/data/train_likes.parquet')\nlikes = likes[likes.uid.isin(uid_to_idx) & likes.item_id.isin(item_to_idx)]\nl_u = np.fromiter((uid_to_idx[x] for x in likes.uid), dtype=np.int32, count=len(likes))\nl_it = np.fromiter((item_to_idx[x] for x in likes.item_id), dtype=np.int32, count=len(likes))\nl_tw = np.exp((likes.timestamp.to_numpy() - CUTOFF) / (60.0 * 86400.0))\nl_w = np.where(likes.is_organic.to_numpy() == 1, 5.0, 3.0) * l_tw\n\n# 2. Load listens from last 120 days\nitem_set = pc.SetLookupOptions(value_set=pa.array(cat_items))\ntable = pq.read_table('/task/data/train_listens.parquet', \n filters=[('timestamp', '>=', int(CUTOFF - 120 * 86400))],\n columns=['uid', 'item_id', 'timestamp', 'played_ratio_pct', 'is_organic'])\nfiltered = table.filter(pc.is_in(table['item_id'], options=item_set))\n\nu_arr = filtered['uid'].to_numpy()\nit_arr = filtered['item_id'].to_numpy()\npct_arr = filtered['played_ratio_pct'].to_numpy()\norg_arr = filtered['is_organic'].to_numpy()\nts_arr = filtered['timestamp'].to_numpy()\n\nu_mask = np.isin(u_arr, uids)\nu_idx = np.fromiter((uid_to_idx[x] for x in u_arr[u_mask]), dtype=np.int32, count=int(u_mask.sum()))\nit_idx = np.fromiter((item_to_idx[x] for x in it_arr[u_mask]), dtype=np.int32, count=int(u_mask.sum()))\n\ntime_weight = np.exp((ts_arr[u_mask] - CUTOFF) / (60.0 * 86400.0))\nlisten_weight = np.where(pct_arr[u_mask] >= 80, 2.0, np.where(pct_arr[u_mask] >= 50, 1.0, 0.2))\nlisten_weight += np.where(org_arr[u_mask] == 1, 1.5, 0.0)\nweights = listen_weight * time_weight\n\nX_listens = sp.csr_matrix((weights, (u_idx, it_idx)), shape=(len(uids), len(cat_items)))\nX_likes = sp.csr_matrix((l_w, (l_u, l_it)), shape=(len(uids), len(cat_items)))\nX = X_listens + X_likes\n\n# Compute IDF for each item\n# Number of users interacting with item\nitem_doc_freq = np.diff(X.tocsc().indptr)\nidf = np.log((len(uids) + 1.0) / (item_doc_freq + 1.0)) + 1.0\n# Apply IDF to X\nX_idf = X.multiply(idf).tocsr()\n\n# Compute user similarities with X_idf\nX_norm = normalize(X_idf, norm='l2', axis=1)\nS_raw = (X_norm @ X_norm.T).toarray()\nnp.fill_diagonal(S_raw, 0.0)\n# Zero negative\nS_raw = np.maximum(S_raw, 0.0)\n\ndef test_config(alpha, K):\n S = np.power(S_raw, alpha)\n for i in range(len(uids)):\n row = S[i]\n thresh = np.partition(row, -K)[-K]\n row[row < thresh] = 0.0\n \n # R = S @ X\n ranked = {}\n BATCH = 500\n for start in range(0, len(val_uids), BATCH):\n end = min(start + BATCH, len(val_uids))\n batch_uids = val_uids[start:end]\n batch_indices = [uid_to_idx[u] for u in batch_uids]\n batch_scores = S[batch_indices] @ X\n for i, (r_idx, uid) in enumerate(zip(range(start, end), batch_uids)):\n bits = np.unpackbits(val_packed[r_idx], count=len(val_items), bitorder='little')\n user_scores = np.asarray(batch_scores[i]).ravel().copy()\n mask = np.ones(len(cat_items), dtype=bool)\n mask[val_to_cat[bits == 0]] = False\n user_scores[~mask] = -1e9\n top100_idx = np.argpartition(user_scores, -100)[-100:]\n top100_sorted = top100_idx[np.argsort(-user_scores[top100_idx])]\n ranked[uid] = cat_items[top100_sorted].tolist()\n res = score(ranked, truth)\n print(f'alpha={alpha}, K={K} -> NDCG@10 = {res[\\\"quality_ndcg@10\\\"]:.6f}')\n\nfor alpha in [1.0, 1.5, 2.0, 2.5]:\n for K in [50, 100, 200]:\n test_config(alpha, K)\n\"" } ### Tool output · 2026-09-29T01:35:33.964Z Command was automatically cancelled because it exceeded the timeout of 5.0 minutes without output. There was no output before it was cancelled. ## Step 47 · agent · 2026-09-29T01:35:40.354Z ### Recorded reasoning summary Optimizing Calculation Loop I've been analyzing the computational load from the recent loop execution. It ran through 4 alphas and 3 Ks, totaling 12 iterations. Each iteration involved a significant matrix multiplication for 8456 users across 17 batches, leading to an unexpectedly long processing time exceeding 5 minutes. I'm now focused on identifying specific bottlenecks within this process. ### run_shell_command · 2026-09-29T01:35:40.354Z { "description": "Benchmark dense float32 GEMM", "command": "python3 -c \"\nimport time\nimport numpy as np\n\nA = np.random.randn(8456, 8456).astype(np.float32)\nB = np.random.randn(8456, 30000).astype(np.float32)\nt0 = time.time()\nC = A @ B\nprint(f'Dense GEMM (8456x8456 @ 8456x30000) took: {time.time() - t0:.2f}s')\n\"" } ### Tool output · 2026-09-29T01:36:06.597Z Output: Dense GEMM (8456x8456 @ 8456x30000) took: 19.82s Process Group PGID: 2406 ## Step 48 · agent · 2026-09-29T01:36:22.789Z ### Recorded reasoning summary Calculating Validation Users I've identified that dense GEMM took 19.82 seconds and realized we only need to score validation or target users. A key question now is how many users actually fall into the validation set. It appears we only need to score 4263 users, or a subset for NDCG@10 evaluation. ### Recorded reasoning summary Refining Computation Times I've realized that the bulk of the computation isn't the user scoring itself, but rather the matrix operations. SVD(64) is surprisingly fast, taking only 4 seconds for decomposition, and scoring all users is a mere 0.02 seconds. Now I'm exploring why SVD yielded 0.021702 and how to improve matrix factorization performance beyond 0.030. ### Recorded reasoning summary Testing Transformations I'm now prioritizing testing TF-IDF transformations before SVD, as this approach is known to significantly improve recommendations. I'll also be investigating the impact of PPMI, explicit preference states, distinguishing organic from non-organic interactions, exploring different rank dimensions, and incorporating item popularity priors. ### run_shell_command · 2026-09-29T01:36:22.789Z { "command": "python3 -c \"\nimport sys\nsys.path.append('/task')\nimport time\nimport numpy as np\nimport scipy.sparse as sp\nfrom sklearn.decomposition import TruncatedSVD\nfrom evaluate_public import score, validation_truth\nfrom ranking_evaluation import CandidateSet\nimport pandas as pd\nimport pyarrow.parquet as pq\nimport pyarrow.compute as pc\nimport pyarrow as pa\n\nt0 = time.time()\ntarget_users = pd.read_parquet('/task/data/target_users.parquet')\nuids = target_users.uid.to_numpy(dtype=np.int64)\nuid_to_idx = {u: i for i, u in enumerate(uids)}\n\ncat = pd.read_parquet('/task/data/candidate_catalog.parquet')\ncat_items = cat.item_id.to_numpy(dtype=np.int64)\nitem_to_idx = {it: i for i, it in enumerate(cat_items)}\n\nwith np.load('/task/data/validation_eligible_candidates.npz') as z:\n val_uids = z['uids'].astype(np.int64)\n val_items = z['item_ids'].astype(np.int64)\n val_packed = z['packed_eligible']\nval_to_cat = np.array([item_to_idx[it] for it in val_items])\n\neligibility = CandidateSet('/task/data/validation_eligible_candidates.npz')\ntruth = validation_truth(eligibility, set(val_uids))\n\nCUTOFF = 24786800.0\n\n# 1. Load likes\nlikes = pd.read_parquet('/task/data/train_likes.parquet')\nlikes = likes[likes.uid.isin(uid_to_idx) & likes.item_id.isin(item_to_idx)]\nl_u = np.fromiter((uid_to_idx[x] for x in likes.uid), dtype=np.int32, count=len(likes))\nl_it = np.fromiter((item_to_idx[x] for x in likes.item_id), dtype=np.int32, count=len(likes))\nl_tw = np.exp((likes.timestamp.to_numpy() - CUTOFF) / (60.0 * 86400.0))\nl_w = np.where(likes.is_organic.to_numpy() == 1, 5.0, 3.0) * l_tw\n\n# 2. Load listens from last 120 days\nitem_set = pc.SetLookupOptions(value_set=pa.array(cat_items))\ntable = pq.read_table('/task/data/train_listens.parquet', \n filters=[('timestamp', '>=', int(CUTOFF - 120 * 86400))],\n columns=['uid', 'item_id', 'timestamp', 'played_ratio_pct', 'is_organic'])\nfiltered = table.filter(pc.is_in(table['item_id'], options=item_set))\n\nu_arr = filtered['uid'].to_numpy()\nit_arr = filtered['item_id'].to_numpy()\npct_arr = filtered['played_ratio_pct'].to_numpy()\norg_arr = filtered['is_organic'].to_numpy()\nts_arr = filtered['timestamp'].to_numpy()\n\nu_mask = np.isin(u_arr, uids)\nu_idx = np.fromiter((uid_to_idx[x] for x in u_arr[u_mask]), dtype=np.int32, count=int(u_mask.sum()))\nit_idx = np.fromiter((item_to_idx[x] for x in it_arr[u_mask]), dtype=np.int32, count=int(u_mask.sum()))\n\ntime_weight = np.exp((ts_arr[u_mask] - CUTOFF) / (60.0 * 86400.0))\nlisten_weight = np.where(pct_arr[u_mask] >= 80, 2.0, np.where(pct_arr[u_mask] >= 50, 1.0, 0.2))\nlisten_weight += np.where(org_arr[u_mask] == 1, 1.5, 0.0)\nweights = listen_weight * time_weight\n\nX_listens = sp.csr_matrix((weights, (u_idx, it_idx)), shape=(len(uids), len(cat_items)))\nX_likes = sp.csr_matrix((l_w, (l_u, l_it)), shape=(len(uids), len(cat_items)))\nX = X_listens + X_likes\n\nprint(f'Data prepared in {time.time() - t0:.2f}s, X shape={X.shape}, nnz={X.nnz}')\n\ndef evaluate_model(pred_fn, name):\n ranked = {}\n BATCH = 1000\n for start in range(0, len(val_uids), BATCH):\n end = min(start + BATCH, len(val_uids))\n batch_uids = val_uids[start:end]\n batch_idx = [uid_to_idx[u] for u in batch_uids]\n batch_scores = pred_fn(batch_idx) # (batch_size, 30000)\n for i, (r_idx, uid) in enumerate(zip(range(start, end), batch_uids)):\n bits = np.unpackbits(val_packed[r_idx], count=len(val_items), bitorder='little')\n user_scores = batch_scores[i].copy()\n mask = np.ones(len(cat_items), dtype=bool)\n mask[val_to_cat[bits == 0]] = False\n user_scores[~mask] = -1e9\n top100_idx = np.argpartition(user_scores, -100)[-100:]\n top100_sorted = top100_idx[np.argsort(-user_scores[top100_idx])]\n ranked[uid] = cat_items[top100_sorted].tolist()\n res = score(ranked, truth)\n print(f'{name} -> NDCG@10 = {res[\\\"quality_ndcg@10\\\"]:.6f}')\n return res[\\\"quality_ndcg@10\\\"]\n\n# 1. Raw SVD\nfor n_comp in [64, 100]:\n svd = TruncatedSVD(n_components=n_comp, random_state=42)\n U = svd.fit_transform(X)\n V = svd.components_\n evaluate_model(lambda b_idx, U=U, V=V: U[b_idx] @ V, f'Raw SVD({n_comp})')\n\n# 2. Log1p(X) + SVD\nX_log = X.copy()\nX_log.data = np.log1p(X_log.data)\nfor n_comp in [64, 100]:\n svd = TruncatedSVD(n_components=n_comp, random_state=42)\n U = svd.fit_transform(X_log)\n V = svd.components_\n evaluate_model(lambda b_idx, U=U, V=V: U[b_idx] @ V, f'Log1p SVD({n_comp})')\n\n# 3. TF-IDF / BM25 + SVD\nitem_df = np.diff(X.tocsc().indptr)\nidf = np.log((len(uids) + 1.0) / (item_df + 1.0)) + 1.0\nX_tfidf = X_log.multiply(idf).tocsr()\nfor n_comp in [64, 100, 150]:\n svd = TruncatedSVD(n_components=n_comp, random_state=42)\n U = svd.fit_transform(X_tfidf)\n V = svd.components_\n evaluate_model(lambda b_idx, U=U, V=V: U[b_idx] @ V, f'TFIDF SVD({n_comp})')\n\"", "description": "Test various SVD transformations and dimensions" } ### Tool output · 2026-09-29T01:37:17.798Z Output: Data prepared in 8.27s, X shape=(8456, 30000), nnz=5114107 Raw SVD(64) -> NDCG@10 = 0.021653 Raw SVD(100) -> NDCG@10 = 0.019723 Log1p SVD(64) -> NDCG@10 = 0.032032 Log1p SVD(100) -> NDCG@10 = 0.031072 TFIDF SVD(64) -> NDCG@10 = 0.029062 TFIDF SVD(100) -> NDCG@10 = 0.029837 TFIDF SVD(150) -> NDCG@10 = 0.030893 Process Group PGID: 2416 ## Step 49 · agent · 2026-09-29T01:37:23.378Z ### Recorded reasoning summary Improving Recommendation Performance My current focus is on dramatically enhancing recommendation efficacy. I've just observed a substantial improvement with `Log1p SVD(64)`, achieving an NDCG@10 of 0.032032, a significant jump from baseline methods and completed in a remarkably short time. ### Recorded reasoning summary Exploring Transform Options I am now investigating alternative power transformations beyond `log1p`, specifically exploring square root and scaled log transforms. Simultaneously, I'm refining SVD component counts around 48, 64, and 80. Additionally, I'm considering adjustments to the listening window and time decay parameters, along with the impact of organic listen weighting. The explicit preference state data is also being re-examined to ensure all relevant user interactions are captured. ### run_shell_command · 2026-09-29T01:37:23.378Z { "description": "Check preference state overlap with train_likes and candidate catalog", "command": "python3 -c \"\nimport pandas as pd\npref = pd.read_parquet('/task/data/validation_preference_state.parquet')\nlikes = pd.read_parquet('/task/data/train_likes.parquet')\ncat = pd.read_parquet('/task/data/candidate_catalog.parquet')\ncat_set = set(cat.item_id)\n\npref_cat = pref[pref.item_id.isin(cat_set)]\nprint(f'Preference state in catalog: total={len(pref_cat)}, liked={(pref_cat.state==\\\"liked\\\").sum()}, disliked={(pref_cat.state==\\\"disliked\\\").sum()}')\n\n# Check overlap with train_likes\npref_liked_pairs = set(zip(pref_cat[pref_cat.state == 'liked'].uid, pref_cat[pref_cat.state == 'liked'].item_id))\ntrain_likes_pairs = set(zip(likes.uid, likes.item_id))\nprint(f'Liked in pref but not in train_likes: {len(pref_liked_pairs - train_likes_pairs)}')\nprint(f'Disliked in pref: {len(pref_cat[pref_cat.state == \\\"disliked\\\"])}')\n\"" } ### Tool output · 2026-09-29T01:37:25.471Z Output: Preference state in catalog: total=469124, liked=418435, disliked=50689 Liked in pref but not in train_likes: 0 Disliked in pref: 50689 Process Group PGID: 2445 ## Step 50 · agent · 2026-09-29T01:37:41.747Z ### Recorded reasoning summary Analyzing Preference Data I'm currently processing a significant volume of user preference data, specifically noting the 50,689 disliked tracks within `validation_preference_state`. My focus is on how this past preference impacts eligibility for new recommendations, as candidates must not be currently liked or disliked. ### Recorded reasoning summary Incorporating Active Preferences My thoughts are now deeply focused on integrating the active preference state. I'm considering assigning significant positive weight to actively liked tracks and substantial negative weight to actively disliked ones. SVD's capacity to handle negative entries is a key consideration, and I'm preparing to test various parameter combinations for active likes/dislikes, time decay, SVD components, and data transformations. ### run_shell_command · 2026-09-29T01:37:41.747Z { "description": "Test half-life and dislike weights with SVD", "command": "python3 -c \"\nimport sys\nsys.path.append('/task')\nimport time\nimport numpy as np\nimport scipy.sparse as sp\nfrom sklearn.decomposition import TruncatedSVD\nfrom evaluate_public import score, validation_truth\nfrom ranking_evaluation import CandidateSet\nimport pandas as pd\nimport pyarrow.parquet as pq\nimport pyarrow.compute as pc\nimport pyarrow as pa\n\ntarget_users = pd.read_parquet('/task/data/target_users.parquet')\nuids = target_users.uid.to_numpy(dtype=np.int64)\nuid_to_idx = {u: i for i, u in enumerate(uids)}\n\ncat = pd.read_parquet('/task/data/candidate_catalog.parquet')\ncat_items = cat.item_id.to_numpy(dtype=np.int64)\nitem_to_idx = {it: i for i, it in enumerate(cat_items)}\n\nwith np.load('/task/data/validation_eligible_candidates.npz') as z:\n val_uids = z['uids'].astype(np.int64)\n val_items = z['item_ids'].astype(np.int64)\n val_packed = z['packed_eligible']\nval_to_cat = np.array([item_to_idx[it] for it in val_items])\n\neligibility = CandidateSet('/task/data/validation_eligible_candidates.npz')\ntruth = validation_truth(eligibility, set(val_uids))\n\nCUTOFF = 24786800.0\n\n# Preference state:\npref = pd.read_parquet('/task/data/validation_preference_state.parquet')\npref = pref[pref.uid.isin(uid_to_idx) & pref.item_id.isin(item_to_idx)]\n\np_liked = pref[pref.state == 'liked']\np_disliked = pref[pref.state == 'disliked']\n\npref_u_liked = np.fromiter((uid_to_idx[x] for x in p_liked.uid), dtype=np.int32, count=len(p_liked))\npref_it_liked = np.fromiter((item_to_idx[x] for x in p_liked.item_id), dtype=np.int32, count=len(p_liked))\n\npref_u_dis = np.fromiter((uid_to_idx[x] for x in p_disliked.uid), dtype=np.int32, count=len(p_disliked))\npref_it_dis = np.fromiter((item_to_idx[x] for x in p_disliked.item_id), dtype=np.int32, count=len(p_disliked))\n\n# Train likes\nlikes = pd.read_parquet('/task/data/train_likes.parquet')\nlikes = likes[likes.uid.isin(uid_to_idx) & likes.item_id.isin(item_to_idx)]\nl_u = np.fromiter((uid_to_idx[x] for x in likes.uid), dtype=np.int32, count=len(likes))\nl_it = np.fromiter((item_to_idx[x] for x in likes.item_id), dtype=np.int32, count=len(likes))\n\n# Train listens from last 180 days\nitem_set = pc.SetLookupOptions(value_set=pa.array(cat_items))\ntable = pq.read_table('/task/data/train_listens.parquet', \n filters=[('timestamp', '>=', int(CUTOFF - 180 * 86400))],\n columns=['uid', 'item_id', 'timestamp', 'played_ratio_pct', 'is_organic'])\nfiltered = table.filter(pc.is_in(table['item_id'], options=item_set))\n\nu_arr = filtered['uid'].to_numpy()\nit_arr = filtered['item_id'].to_numpy()\npct_arr = filtered['played_ratio_pct'].to_numpy()\norg_arr = filtered['is_organic'].to_numpy()\nts_arr = filtered['timestamp'].to_numpy()\n\nu_mask = np.isin(u_arr, uids)\nu_idx = np.fromiter((uid_to_idx[x] for x in u_arr[u_mask]), dtype=np.int32, count=int(u_mask.sum()))\nit_idx = np.fromiter((item_to_idx[x] for x in it_arr[u_mask]), dtype=np.int32, count=int(u_mask.sum()))\n\ndef build_matrix(half_life_days=45.0, org_weight=2.0, dis_weight=2.0):\n tau = half_life_days * 86400.0\n tw_listens = np.exp((ts_arr[u_mask] - CUTOFF) / tau)\n # completion weight\n cw = np.where(pct_arr[u_mask] >= 80, 2.0, np.where(pct_arr[u_mask] >= 50, 1.0, 0.2))\n # organic bonus\n cw += np.where(org_arr[u_mask] == 1, org_weight, 0.0)\n w_listens = cw * tw_listens\n \n tw_likes = np.exp((likes.timestamp.to_numpy() - CUTOFF) / tau)\n w_likes = (np.where(likes.is_organic.to_numpy() == 1, 6.0, 4.0)) * tw_likes\n \n X1 = sp.csr_matrix((w_listens, (u_idx, it_idx)), shape=(len(uids), len(cat_items)))\n X2 = sp.csr_matrix((w_likes, (l_u, l_it)), shape=(len(uids), len(cat_items)))\n # Active preferences (at cutoff, so time weight = 1.0)\n X3 = sp.csr_matrix((np.full(len(pref_u_liked), 5.0), (pref_u_liked, pref_it_liked)), shape=(len(uids), len(cat_items)))\n \n X = X1 + X2 + X3\n # Apply log1p on positive part\n X.data = np.log1p(X.data)\n \n if dis_weight > 0:\n X_dis = sp.csr_matrix((-np.full(len(pref_u_dis), dis_weight), (pref_u_dis, pref_it_dis)), shape=(len(uids), len(cat_items)))\n X = X + X_dis\n return X\n\ndef evaluate_svd(X, n_components):\n svd = TruncatedSVD(n_components=n_components, random_state=42)\n U = svd.fit_transform(X)\n V = svd.components_\n ranked = {}\n BATCH = 1000\n for start in range(0, len(val_uids), BATCH):\n end = min(start + BATCH, len(val_uids))\n batch_uids = val_uids[start:end]\n batch_idx = [uid_to_idx[u] for u in batch_uids]\n batch_scores = U[batch_idx] @ V\n for i, (r_idx, uid) in enumerate(zip(range(start, end), batch_uids)):\n bits = np.unpackbits(val_packed[r_idx], count=len(val_items), bitorder='little')\n user_scores = batch_scores[i].copy()\n mask = np.ones(len(cat_items), dtype=bool)\n mask[val_to_cat[bits == 0]] = False\n user_scores[~mask] = -1e9\n top100_idx = np.argpartition(user_scores, -100)[-100:]\n top100_sorted = top100_idx[np.argsort(-user_scores[top100_idx])]\n ranked[uid] = cat_items[top100_sorted].tolist()\n res = score(ranked, truth)\n return res['quality_ndcg@10']\n\nfor hl in [30.0, 45.0, 60.0]:\n for dis in [0.0, 1.0, 3.0]:\n X = build_matrix(half_life_days=hl, dis_weight=dis)\n ndcg = evaluate_svd(X, n_components=64)\n print(f'hl={hl:4.1f}, dis={dis:3.1f}, n_comp=64 -> NDCG@10 = {ndcg:.6f}')\n\"" } ### Tool output · 2026-09-29T01:39:00.344Z Output: hl=30.0, dis=0.0, n_comp=64 -> NDCG@10 = 0.035042 hl=30.0, dis=1.0, n_comp=64 -> NDCG@10 = 0.035127 hl=30.0, dis=3.0, n_comp=64 -> NDCG@10 = 0.034566 hl=45.0, dis=0.0, n_comp=64 -> NDCG@10 = 0.033303 hl=45.0, dis=1.0, n_comp=64 -> NDCG@10 = 0.033362 hl=45.0, dis=3.0, n_comp=64 -> NDCG@10 = 0.032999 hl=60.0, dis=0.0, n_comp=64 -> NDCG@10 = 0.031963 hl=60.0, dis=1.0, n_comp=64 -> NDCG@10 = 0.031900 hl=60.0, dis=3.0, n_comp=64 -> NDCG@10 = 0.031689 Process Group PGID: 2463 ## Step 51 · agent · 2026-09-29T01:39:15.112Z ### Recorded reasoning summary Testing Half-Life Impacts I've observed that a shorter half-life significantly boosts NDCG@10 performance, with `hl=30` achieving `0.0351`. I'm now exploring even shorter durations like 20, 14, and 25 days to pinpoint the optimal recency setting. ### run_shell_command · 2026-09-29T01:39:15.112Z { "command": "python3 -c \"\nimport sys\nsys.path.append('/task')\nimport numpy as np\nimport scipy.sparse as sp\nfrom sklearn.decomposition import TruncatedSVD\nfrom evaluate_public import score, validation_truth\nfrom ranking_evaluation import CandidateSet\nimport pandas as pd\nimport pyarrow.parquet as pq\nimport pyarrow.compute as pc\nimport pyarrow as pa\n\ntarget_users = pd.read_parquet('/task/data/target_users.parquet')\nuids = target_users.uid.to_numpy(dtype=np.int64)\nuid_to_idx = {u: i for i, u in enumerate(uids)}\n\ncat = pd.read_parquet('/task/data/candidate_catalog.parquet')\ncat_items = cat.item_id.to_numpy(dtype=np.int64)\nitem_to_idx = {it: i for i, it in enumerate(cat_items)}\n\nwith np.load('/task/data/validation_eligible_candidates.npz') as z:\n val_uids = z['uids'].astype(np.int64)\n val_items = z['item_ids'].astype(np.int64)\n val_packed = z['packed_eligible']\nval_to_cat = np.array([item_to_idx[it] for it in val_items])\n\neligibility = CandidateSet('/task/data/validation_eligible_candidates.npz')\ntruth = validation_truth(eligibility, set(val_uids))\n\nCUTOFF = 24786800.0\n\npref = pd.read_parquet('/task/data/validation_preference_state.parquet')\npref = pref[pref.uid.isin(uid_to_idx) & pref.item_id.isin(item_to_idx)]\n\np_liked = pref[pref.state == 'liked']\np_disliked = pref[pref.state == 'disliked']\n\npref_u_liked = np.fromiter((uid_to_idx[x] for x in p_liked.uid), dtype=np.int32, count=len(p_liked))\npref_it_liked = np.fromiter((item_to_idx[x] for x in p_liked.item_id), dtype=np.int32, count=len(p_liked))\n\npref_u_dis = np.fromiter((uid_to_idx[x] for x in p_disliked.uid), dtype=np.int32, count=len(p_disliked))\npref_it_dis = np.fromiter((item_to_idx[x] for x in p_disliked.item_id), dtype=np.int32, count=len(p_disliked))\n\nlikes = pd.read_parquet('/task/data/train_likes.parquet')\nlikes = likes[likes.uid.isin(uid_to_idx) & likes.item_id.isin(item_to_idx)]\nl_u = np.fromiter((uid_to_idx[x] for x in likes.uid), dtype=np.int32, count=len(likes))\nl_it = np.fromiter((item_to_idx[x] for x in likes.item_id), dtype=np.int32, count=len(likes))\n\nitem_set = pc.SetLookupOptions(value_set=pa.array(cat_items))\ntable = pq.read_table('/task/data/train_listens.parquet', \n filters=[('timestamp', '>=', int(CUTOFF - 180 * 86400))],\n columns=['uid', 'item_id', 'timestamp', 'played_ratio_pct', 'is_organic'])\nfiltered = table.filter(pc.is_in(table['item_id'], options=item_set))\n\nu_arr = filtered['uid'].to_numpy()\nit_arr = filtered['item_id'].to_numpy()\npct_arr = filtered['played_ratio_pct'].to_numpy()\norg_arr = filtered['is_organic'].to_numpy()\nts_arr = filtered['timestamp'].to_numpy()\n\nu_mask = np.isin(u_arr, uids)\nu_idx = np.fromiter((uid_to_idx[x] for x in u_arr[u_mask]), dtype=np.int32, count=int(u_mask.sum()))\nit_idx = np.fromiter((item_to_idx[x] for x in it_arr[u_mask]), dtype=np.int32, count=int(u_mask.sum()))\n\ndef build_matrix(half_life_days=20.0, org_weight=2.0, dis_weight=1.0):\n tau = half_life_days * 86400.0\n tw_listens = np.exp((ts_arr[u_mask] - CUTOFF) / tau)\n cw = np.where(pct_arr[u_mask] >= 80, 2.0, np.where(pct_arr[u_mask] >= 50, 1.0, 0.2))\n cw += np.where(org_arr[u_mask] == 1, org_weight, 0.0)\n w_listens = cw * tw_listens\n \n tw_likes = np.exp((likes.timestamp.to_numpy() - CUTOFF) / tau)\n w_likes = (np.where(likes.is_organic.to_numpy() == 1, 6.0, 4.0)) * tw_likes\n \n X1 = sp.csr_matrix((w_listens, (u_idx, it_idx)), shape=(len(uids), len(cat_items)))\n X2 = sp.csr_matrix((w_likes, (l_u, l_it)), shape=(len(uids), len(cat_items)))\n X3 = sp.csr_matrix((np.full(len(pref_u_liked), 5.0), (pref_u_liked, pref_it_liked)), shape=(len(uids), len(cat_items)))\n \n X = X1 + X2 + X3\n X.data = np.log1p(X.data)\n if dis_weight > 0:\n X_dis = sp.csr_matrix((-np.full(len(pref_u_dis), dis_weight), (pref_u_dis, pref_it_dis)), shape=(len(uids), len(cat_items)))\n X = X + X_dis\n return X\n\ndef evaluate_svd(X, n_components):\n svd = TruncatedSVD(n_components=n_components, random_state=42)\n U = svd.fit_transform(X)\n V = svd.components_\n ranked = {}\n BATCH = 1000\n for start in range(0, len(val_uids), BATCH):\n end = min(start + BATCH, len(val_uids))\n batch_uids = val_uids[start:end]\n batch_idx = [uid_to_idx[u] for u in batch_uids]\n batch_scores = U[batch_idx] @ V\n for i, (r_idx, uid) in enumerate(zip(range(start, end), batch_uids)):\n bits = np.unpackbits(val_packed[r_idx], count=len(val_items), bitorder='little')\n user_scores = batch_scores[i].copy()\n mask = np.ones(len(cat_items), dtype=bool)\n mask[val_to_cat[bits == 0]] = False\n user_scores[~mask] = -1e9\n top100_idx = np.argpartition(user_scores, -100)[-100:]\n top100_sorted = top100_idx[np.argsort(-user_scores[top100_idx])]\n ranked[uid] = cat_items[top100_sorted].tolist()\n res = score(ranked, truth)\n return res['quality_ndcg@10']\n\nfor hl in [14.0, 21.0, 28.0]:\n X = build_matrix(half_life_days=hl, dis_weight=1.0)\n for n_comp in [48, 64, 80]:\n ndcg = evaluate_svd(X, n_components=n_comp)\n print(f'hl={hl:4.1f}, n_comp={n_comp:2d} -> NDCG@10 = {ndcg:.6f}')\n\"", "description": "Tune half-life and components around sweet spot" } ### Tool output · 2026-09-29T01:40:23.644Z Output: hl=14.0, n_comp=48 -> NDCG@10 = 0.037210 hl=14.0, n_comp=64 -> NDCG@10 = 0.037352 hl=14.0, n_comp=80 -> NDCG@10 = 0.037183 hl=21.0, n_comp=48 -> NDCG@10 = 0.036251 hl=21.0, n_comp=64 -> NDCG@10 = 0.036416 hl=21.0, n_comp=80 -> NDCG@10 = 0.036135 hl=28.0, n_comp=48 -> NDCG@10 = 0.035093 hl=28.0, n_comp=64 -> NDCG@10 = 0.035299 hl=28.0, n_comp=80 -> NDCG@10 = 0.035341 Process Group PGID: 2492 ## Step 52 · agent · 2026-09-29T01:40:39.607Z ### Recorded reasoning summary Refining Parameters and Tastes I've achieved a score of 0.037352 with `hl=14.0, n_comp=64`. I'm now investigating whether 7 or 10 days provides superior results and exploring how to best integrate short-term and medium-term user taste components within the recommender system. ### run_shell_command · 2026-09-29T01:40:39.607Z { "description": "Test fine-grained half-life values", "command": "python3 -c \"\nimport sys\nsys.path.append('/task')\nimport numpy as np\nimport scipy.sparse as sp\nfrom sklearn.decomposition import TruncatedSVD\nfrom evaluate_public import score, validation_truth\nfrom ranking_evaluation import CandidateSet\nimport pandas as pd\nimport pyarrow.parquet as pq\nimport pyarrow.compute as pc\nimport pyarrow as pa\n\ntarget_users = pd.read_parquet('/task/data/target_users.parquet')\nuids = target_users.uid.to_numpy(dtype=np.int64)\nuid_to_idx = {u: i for i, u in enumerate(uids)}\n\ncat = pd.read_parquet('/task/data/candidate_catalog.parquet')\ncat_items = cat.item_id.to_numpy(dtype=np.int64)\nitem_to_idx = {it: i for i, it in enumerate(cat_items)}\n\nwith np.load('/task/data/validation_eligible_candidates.npz') as z:\n val_uids = z['uids'].astype(np.int64)\n val_items = z['item_ids'].astype(np.int64)\n val_packed = z['packed_eligible']\nval_to_cat = np.array([item_to_idx[it] for it in val_items])\n\neligibility = CandidateSet('/task/data/validation_eligible_candidates.npz')\ntruth = validation_truth(eligibility, set(val_uids))\n\nCUTOFF = 24786800.0\n\npref = pd.read_parquet('/task/data/validation_preference_state.parquet')\npref = pref[pref.uid.isin(uid_to_idx) & pref.item_id.isin(item_to_idx)]\n\np_liked = pref[pref.state == 'liked']\np_disliked = pref[pref.state == 'disliked']\n\npref_u_liked = np.fromiter((uid_to_idx[x] for x in p_liked.uid), dtype=np.int32, count=len(p_liked))\npref_it_liked = np.fromiter((item_to_idx[x] for x in p_liked.item_id), dtype=np.int32, count=len(p_liked))\n\npref_u_dis = np.fromiter((uid_to_idx[x] for x in p_disliked.uid), dtype=np.int32, count=len(p_disliked))\npref_it_dis = np.fromiter((item_to_idx[x] for x in p_disliked.item_id), dtype=np.int32, count=len(p_disliked))\n\nlikes = pd.read_parquet('/task/data/train_likes.parquet')\nlikes = likes[likes.uid.isin(uid_to_idx) & likes.item_id.isin(item_to_idx)]\nl_u = np.fromiter((uid_to_idx[x] for x in likes.uid), dtype=np.int32, count=len(likes))\nl_it = np.fromiter((item_to_idx[x] for x in likes.item_id), dtype=np.int32, count=len(likes))\n\nitem_set = pc.SetLookupOptions(value_set=pa.array(cat_items))\ntable = pq.read_table('/task/data/train_listens.parquet', \n filters=[('timestamp', '>=', int(CUTOFF - 180 * 86400))],\n columns=['uid', 'item_id', 'timestamp', 'played_ratio_pct', 'is_organic'])\nfiltered = table.filter(pc.is_in(table['item_id'], options=item_set))\n\nu_arr = filtered['uid'].to_numpy()\nit_arr = filtered['item_id'].to_numpy()\npct_arr = filtered['played_ratio_pct'].to_numpy()\norg_arr = filtered['is_organic'].to_numpy()\nts_arr = filtered['timestamp'].to_numpy()\n\nu_mask = np.isin(u_arr, uids)\nu_idx = np.fromiter((uid_to_idx[x] for x in u_arr[u_mask]), dtype=np.int32, count=int(u_mask.sum()))\nit_idx = np.fromiter((item_to_idx[x] for x in it_arr[u_mask]), dtype=np.int32, count=int(u_mask.sum()))\n\ndef build_matrix(half_life_days=10.0, org_weight=2.0, dis_weight=1.0):\n tau = half_life_days * 86400.0\n tw_listens = np.exp((ts_arr[u_mask] - CUTOFF) / tau)\n cw = np.where(pct_arr[u_mask] >= 80, 2.0, np.where(pct_arr[u_mask] >= 50, 1.0, 0.2))\n cw += np.where(org_arr[u_mask] == 1, org_weight, 0.0)\n w_listens = cw * tw_listens\n \n tw_likes = np.exp((likes.timestamp.to_numpy() - CUTOFF) / tau)\n w_likes = (np.where(likes.is_organic.to_numpy() == 1, 6.0, 4.0)) * tw_likes\n \n X1 = sp.csr_matrix((w_listens, (u_idx, it_idx)), shape=(len(uids), len(cat_items)))\n X2 = sp.csr_matrix((w_likes, (l_u, l_it)), shape=(len(uids), len(cat_items)))\n X3 = sp.csr_matrix((np.full(len(pref_u_liked), 5.0), (pref_u_liked, pref_it_liked)), shape=(len(uids), len(cat_items)))\n \n X = X1 + X2 + X3\n X.data = np.log1p(X.data)\n if dis_weight > 0:\n X_dis = sp.csr_matrix((-np.full(len(pref_u_dis), dis_weight), (pref_u_dis, pref_it_dis)), shape=(len(uids), len(cat_items)))\n X = X + X_dis\n return X\n\ndef get_scores(X, n_components):\n svd = TruncatedSVD(n_components=n_components, random_state=42)\n U = svd.fit_transform(X)\n V = svd.components_\n return U @ V\n\nfor hl in [5.0, 7.0, 10.0, 14.0]:\n X = build_matrix(half_life_days=hl, dis_weight=1.0)\n scores_mat = get_scores(X, 64)\n # Evaluate\n ranked = {}\n for i, (r_idx, uid) in enumerate(zip(range(len(val_uids)), val_uids)):\n bits = np.unpackbits(val_packed[r_idx], count=len(val_items), bitorder='little')\n user_scores = scores_mat[uid_to_idx[uid]].copy()\n mask = np.ones(len(cat_items), dtype=bool)\n mask[val_to_cat[bits == 0]] = False\n user_scores[~mask] = -1e9\n top100_idx = np.argpartition(user_scores, -100)[-100:]\n top100_sorted = top100_idx[np.argsort(-user_scores[top100_idx])]\n ranked[uid] = cat_items[top100_sorted].tolist()\n res = score(ranked, truth)\n print(f'hl={hl:4.1f} -> NDCG@10 = {res[\\\"quality_ndcg@10\\\"]:.6f}')\n\"" } ### Tool output · 2026-09-29T01:41:19.653Z Output: hl= 5.0 -> NDCG@10 = 0.037882 hl= 7.0 -> NDCG@10 = 0.039199 hl=10.0 -> NDCG@10 = 0.039379 hl=14.0 -> NDCG@10 = 0.037352 Process Group PGID: 2521 ## Step 53 · agent · 2026-09-29T01:41:33.326Z ### Recorded reasoning summary Refining the algorithm I've achieved a `hl=10.0` score of 0.039379, nearing 0.040 which is nearly four times the baseline. I'm now exploring combinations of `hl` values (9.0, 10.0, 11.0) and integrating short-term recency (`hl=10.0`), medium-term preferences (`hl=40.0`), and item popularity priors. ### run_shell_command · 2026-09-29T01:41:33.326Z { "command": "python3 -c \"\nimport sys\nsys.path.append('/task')\nimport numpy as np\nimport scipy.sparse as sp\nfrom sklearn.decomposition import TruncatedSVD\nfrom evaluate_public import score, validation_truth\nfrom ranking_evaluation import CandidateSet\nimport pandas as pd\nimport pyarrow.parquet as pq\nimport pyarrow.compute as pc\nimport pyarrow as pa\n\ntarget_users = pd.read_parquet('/task/data/target_users.parquet')\nuids = target_users.uid.to_numpy(dtype=np.int64)\nuid_to_idx = {u: i for i, u in enumerate(uids)}\n\ncat = pd.read_parquet('/task/data/candidate_catalog.parquet')\ncat_items = cat.item_id.to_numpy(dtype=np.int64)\nitem_to_idx = {it: i for i, it in enumerate(cat_items)}\n\nwith np.load('/task/data/validation_eligible_candidates.npz') as z:\n val_uids = z['uids'].astype(np.int64)\n val_items = z['item_ids'].astype(np.int64)\n val_packed = z['packed_eligible']\nval_to_cat = np.array([item_to_idx[it] for it in val_items])\n\neligibility = CandidateSet('/task/data/validation_eligible_candidates.npz')\ntruth = validation_truth(eligibility, set(val_uids))\n\nCUTOFF = 24786800.0\n\npref = pd.read_parquet('/task/data/validation_preference_state.parquet')\npref = pref[pref.uid.isin(uid_to_idx) & pref.item_id.isin(item_to_idx)]\n\np_liked = pref[pref.state == 'liked']\np_disliked = pref[pref.state == 'disliked']\n\npref_u_liked = np.fromiter((uid_to_idx[x] for x in p_liked.uid), dtype=np.int32, count=len(p_liked))\npref_it_liked = np.fromiter((item_to_idx[x] for x in p_liked.item_id), dtype=np.int32, count=len(p_liked))\n\npref_u_dis = np.fromiter((uid_to_idx[x] for x in p_disliked.uid), dtype=np.int32, count=len(p_disliked))\npref_it_dis = np.fromiter((item_to_idx[x] for x in p_disliked.item_id), dtype=np.int32, count=len(p_disliked))\n\nlikes = pd.read_parquet('/task/data/train_likes.parquet')\nlikes = likes[likes.uid.isin(uid_to_idx) & likes.item_id.isin(item_to_idx)]\nl_u = np.fromiter((uid_to_idx[x] for x in likes.uid), dtype=np.int32, count=len(likes))\nl_it = np.fromiter((item_to_idx[x] for x in likes.item_id), dtype=np.int32, count=len(likes))\n\nitem_set = pc.SetLookupOptions(value_set=pa.array(cat_items))\ntable = pq.read_table('/task/data/train_listens.parquet', \n filters=[('timestamp', '>=', int(CUTOFF - 180 * 86400))],\n columns=['uid', 'item_id', 'timestamp', 'played_ratio_pct', 'is_organic'])\nfiltered = table.filter(pc.is_in(table['item_id'], options=item_set))\n\nu_arr = filtered['uid'].to_numpy()\nit_arr = filtered['item_id'].to_numpy()\npct_arr = filtered['played_ratio_pct'].to_numpy()\norg_arr = filtered['is_organic'].to_numpy()\nts_arr = filtered['timestamp'].to_numpy()\n\nu_mask = np.isin(u_arr, uids)\nu_idx = np.fromiter((uid_to_idx[x] for x in u_arr[u_mask]), dtype=np.int32, count=int(u_mask.sum()))\nit_idx = np.fromiter((item_to_idx[x] for x in it_arr[u_mask]), dtype=np.int32, count=int(u_mask.sum()))\n\ndef build_matrix(half_life_days=10.0, org_weight=2.0, dis_weight=1.0):\n tau = half_life_days * 86400.0\n tw_listens = np.exp((ts_arr[u_mask] - CUTOFF) / tau)\n cw = np.where(pct_arr[u_mask] >= 80, 2.0, np.where(pct_arr[u_mask] >= 50, 1.0, 0.2))\n cw += np.where(org_arr[u_mask] == 1, org_weight, 0.0)\n w_listens = cw * tw_listens\n \n tw_likes = np.exp((likes.timestamp.to_numpy() - CUTOFF) / tau)\n w_likes = (np.where(likes.is_organic.to_numpy() == 1, 6.0, 4.0)) * tw_likes\n \n X1 = sp.csr_matrix((w_listens, (u_idx, it_idx)), shape=(len(uids), len(cat_items)))\n X2 = sp.csr_matrix((w_likes, (l_u, l_it)), shape=(len(uids), len(cat_items)))\n X3 = sp.csr_matrix((np.full(len(pref_u_liked), 5.0), (pref_u_liked, pref_it_liked)), shape=(len(uids), len(cat_items)))\n \n X = X1 + X2 + X3\n X.data = np.log1p(X.data)\n if dis_weight > 0:\n X_dis = sp.csr_matrix((-np.full(len(pref_u_dis), dis_weight), (pref_u_dis, pref_it_dis)), shape=(len(uids), len(cat_items)))\n X = X + X_dis\n return X\n\ndef get_scores(X, n_components):\n svd = TruncatedSVD(n_components=n_components, random_state=42)\n U = svd.fit_transform(X)\n V = svd.components_\n return U @ V\n\nscores_10 = get_scores(build_matrix(10.0), 64)\nscores_35 = get_scores(build_matrix(35.0), 64)\n\n# Normalize scores per user by std dev\nscores_10_norm = scores_10 / (np.std(scores_10, axis=1, keepdims=True) + 1e-9)\nscores_35_norm = scores_35 / (np.std(scores_35, axis=1, keepdims=True) + 1e-9)\n\ndef test_blend(w1, w2):\n blended = w1 * scores_10_norm + w2 * scores_35_norm\n ranked = {}\n for i, (r_idx, uid) in enumerate(zip(range(len(val_uids)), val_uids)):\n bits = np.unpackbits(val_packed[r_idx], count=len(val_items), bitorder='little')\n user_scores = blended[uid_to_idx[uid]].copy()\n mask = np.ones(len(cat_items), dtype=bool)\n mask[val_to_cat[bits == 0]] = False\n user_scores[~mask] = -1e9\n top100_idx = np.argpartition(user_scores, -100)[-100:]\n top100_sorted = top100_idx[np.argsort(-user_scores[top100_idx])]\n ranked[uid] = cat_items[top100_sorted].tolist()\n res = score(ranked, truth)\n print(f'w1={w1:.2f}, w2={w2:.2f} -> NDCG@10 = {res[\\\"quality_ndcg@10\\\"]:.6f}')\n\nfor w2 in [0.0, 0.1, 0.2, 0.3, 0.5]:\n test_blend(1.0, w2)\n\"", "description": "Test multi-timescale blend (short + medium)" } ### Tool output · 2026-09-29T01:41:58.337Z Output: /usr/bin/bash: line 121: 2552 Killed python3 -c " import sys sys.path.append('/task') import numpy as np import scipy.sparse as sp from sklearn.decomposition import TruncatedSVD from evaluate_public import score, validation_truth from ranking_evaluation import CandidateSet import pandas as pd import pyarrow.parquet as pq import pyarrow.compute as pc import pyarrow as pa target_users = pd.read_parquet('/task/data/target_users.parquet') uids = target_users.uid.to_numpy(dtype=np.int64) uid_to_idx = {u: i for i, u in enumerate(uids)} cat = pd.read_parquet('/task/data/candidate_catalog.parquet') cat_items = cat.item_id.to_numpy(dtype=np.int64) item_to_idx = {it: i for i, it in enumerate(cat_items)} with np.load('/task/data/validation_eligible_candidates.npz') as z: val_uids = z['uids'].astype(np.int64) val_items = z['item_ids'].astype(np.int64) val_packed = z['packed_eligible'] val_to_cat = np.array([item_to_idx[it] for it in val_items]) eligibility = CandidateSet('/task/data/validation_eligible_candidates.npz') truth = validation_truth(eligibility, set(val_uids)) CUTOFF = 24786800.0 pref = pd.read_parquet('/task/data/validation_preference_state.parquet') pref = pref[pref.uid.isin(uid_to_idx) & pref.item_id.isin(item_to_idx)] p_liked = pref[pref.state == 'liked'] p_disliked = pref[pref.state == 'disliked'] pref_u_liked = np.fromiter((uid_to_idx[x] for x in p_liked.uid), dtype=np.int32, count=len(p_liked)) pref_it_liked = np.fromiter((item_to_idx[x] for x in p_liked.item_id), dtype=np.int32, count=len(p_liked)) pref_u_dis = np.fromiter((uid_to_idx[x] for x in p_disliked.uid), dtype=np.int32, count=len(p_disliked)) pref_it_dis = np.fromiter((item_to_idx[x] for x in p_disliked.item_id), dtype=np.int32, count=len(p_disliked)) likes = pd.read_parquet('/task/data/train_likes.parquet') likes = likes[likes.uid.isin(uid_to_idx) & likes.item_id.isin(item_to_idx)] l_u = np.fromiter((uid_to_idx[x] for x in likes.uid), dtype=np.int32, count=len(likes)) l_it = np.fromiter((item_to_idx[x] for x in likes.item_id), dtype=np.int32, count=len(likes)) item_set = pc.SetLookupOptions(value_set=pa.array(cat_items)) table = pq.read_table('/task/data/train_listens.parquet', filters=[('timestamp', '>=', int(CUTOFF - 180 * 86400))], columns=['uid', 'item_id', 'timestamp', 'played_ratio_pct', 'is_organic']) filtered = table.filter(pc.is_in(table['item_id'], options=item_set)) u_arr = filtered['uid'].to_numpy() it_arr = filtered['item_id'].to_numpy() pct_arr = filtered['played_ratio_pct'].to_numpy() org_arr = filtered['is_organic'].to_numpy() ts_arr = filtered['timestamp'].to_numpy() u_mask = np.isin(u_arr, uids) u_idx = np.fromiter((uid_to_idx[x] for x in u_arr[u_mask]), dtype=np.int32, count=int(u_mask.sum())) it_idx = np.fromiter((item_to_idx[x] for x in it_arr[u_mask]), dtype=np.int32, count=int(u_mask.sum())) def build_matrix(half_life_days=10.0, org_weight=2.0, dis_weight=1.0): tau = half_life_days * 86400.0 tw_listens = np.exp((ts_arr[u_mask] - CUTOFF) / tau) cw = np.where(pct_arr[u_mask] >= 80, 2.0, np.where(pct_arr[u_mask] >= 50, 1.0, 0.2)) cw += np.where(org_arr[u_mask] == 1, org_weight, 0.0) w_listens = cw * tw_listens tw_likes = np.exp((likes.timestamp.to_numpy() - CUTOFF) / tau) w_likes = (np.where(likes.is_organic.to_numpy() == 1, 6.0, 4.0)) * tw_likes X1 = sp.csr_matrix((w_listens, (u_idx, it_idx)), shape=(len(uids), len(cat_items))) X2 = sp.csr_matrix((w_likes, (l_u, l_it)), shape=(len(uids), len(cat_items))) X3 = sp.csr_matrix((np.full(len(pref_u_liked), 5.0), (pref_u_liked, pref_it_liked)), shape=(len(uids), len(cat_items))) X = X1 + X2 + X3 X.data = np.log1p(X.data) if dis_weight > 0: X_dis = sp.csr_matrix((-np.full(len(pref_u_dis), dis_weight), (pref_u_dis, pref_it_dis)), shape=(len(uids), len(cat_items))) X = X + X_dis return X def get_scores(X, n_components): svd = TruncatedSVD(n_components=n_components, random_state=42) U = svd.fit_transform(X) V = svd.components_ return U @ V scores_10 = get_scores(build_matrix(10.0), 64) scores_35 = get_scores(build_matrix(35.0), 64) # Normalize scores per user by std dev scores_10_norm = scores_10 / (np.std(scores_10, axis=1, keepdims=True) + 1e-9) scores_35_norm = scores_35 / (np.std(scores_35, axis=1, keepdims=True) + 1e-9) def test_blend(w1, w2): blended = w1 * scores_10_norm + w2 * scores_35_norm ranked = {} for i, (r_idx, uid) in enumerate(zip(range(len(val_uids)), val_uids)): bits = np.unpackbits(val_packed[r_idx], count=len(val_items), bitorder='little') user_scores = blended[uid_to_idx[uid]].copy() mask = np.ones(len(cat_items), dtype=bool) mask[val_to_cat[bits == 0]] = False user_scores[~mask] = -1e9 top100_idx = np.argpartition(user_scores, -100)[-100:] top100_sorted = top100_idx[np.argsort(-user_scores[top100_idx])] ranked[uid] = cat_items[top100_sorted].tolist() res = score(ranked, truth) print(f'w1={w1:.2f}, w2={w2:.2f} -> NDCG@10 = {res[\"quality_ndcg@10\"]:.6f}') for w2 in [0.0, 0.1, 0.2, 0.3, 0.5]: test_blend(1.0, w2) " Exit Code: 137 Process Group PGID: 2550 ## Step 54 · agent · 2026-09-29T01:42:12.379Z ### Recorded reasoning summary Analyzing Memory Leaks I'm encountering an "Out of Memory" error (exit code 137). It appears that intermediate matrix multiplications, specifically `scores_10 = U @ V` and `scores_35 = U @ V`, each consuming approximately 2GB of memory, are the primary culprits. I'm also seeing significant memory usage from `scores_10_norm`. ### run_shell_command · 2026-09-29T01:42:12.379Z { "command": "python3 -c \"\nimport sys\nsys.path.append('/task')\nimport numpy as np\nimport scipy.sparse as sp\nfrom sklearn.decomposition import TruncatedSVD\nfrom evaluate_public import score, validation_truth\nfrom ranking_evaluation import CandidateSet\nimport pandas as pd\nimport pyarrow.parquet as pq\nimport pyarrow.compute as pc\nimport pyarrow as pa\nimport gc\n\ntarget_users = pd.read_parquet('/task/data/target_users.parquet')\nuids = target_users.uid.to_numpy(dtype=np.int64)\nuid_to_idx = {u: i for i, u in enumerate(uids)}\n\ncat = pd.read_parquet('/task/data/candidate_catalog.parquet')\ncat_items = cat.item_id.to_numpy(dtype=np.int64)\nitem_to_idx = {it: i for i, it in enumerate(cat_items)}\n\nwith np.load('/task/data/validation_eligible_candidates.npz') as z:\n val_uids = z['uids'].astype(np.int64)\n val_items = z['item_ids'].astype(np.int64)\n val_packed = z['packed_eligible']\nval_to_cat = np.array([item_to_idx[it] for it in val_items])\n\neligibility = CandidateSet('/task/data/validation_eligible_candidates.npz')\ntruth = validation_truth(eligibility, set(val_uids))\n\nCUTOFF = 24786800.0\n\npref = pd.read_parquet('/task/data/validation_preference_state.parquet')\npref = pref[pref.uid.isin(uid_to_idx) & pref.item_id.isin(item_to_idx)]\np_liked = pref[pref.state == 'liked']\np_disliked = pref[pref.state == 'disliked']\npref_u_liked = np.fromiter((uid_to_idx[x] for x in p_liked.uid), dtype=np.int32, count=len(p_liked))\npref_it_liked = np.fromiter((item_to_idx[x] for x in p_liked.item_id), dtype=np.int32, count=len(p_liked))\npref_u_dis = np.fromiter((uid_to_idx[x] for x in p_disliked.uid), dtype=np.int32, count=len(p_disliked))\npref_it_dis = np.fromiter((item_to_idx[x] for x in p_disliked.item_id), dtype=np.int32, count=len(p_disliked))\n\nlikes = pd.read_parquet('/task/data/train_likes.parquet')\nlikes = likes[likes.uid.isin(uid_to_idx) & likes.item_id.isin(item_to_idx)]\nl_u = np.fromiter((uid_to_idx[x] for x in likes.uid), dtype=np.int32, count=len(likes))\nl_it = np.fromiter((item_to_idx[x] for x in likes.item_id), dtype=np.int32, count=len(likes))\n\nitem_set = pc.SetLookupOptions(value_set=pa.array(cat_items))\ntable = pq.read_table('/task/data/train_listens.parquet', \n filters=[('timestamp', '>=', int(CUTOFF - 120 * 86400))],\n columns=['uid', 'item_id', 'timestamp', 'played_ratio_pct', 'is_organic'])\nfiltered = table.filter(pc.is_in(table['item_id'], options=item_set))\n\nu_arr = filtered['uid'].to_numpy()\nit_arr = filtered['item_id'].to_numpy()\npct_arr = filtered['played_ratio_pct'].to_numpy()\norg_arr = filtered['is_organic'].to_numpy()\nts_arr = filtered['timestamp'].to_numpy()\n\nu_mask = np.isin(u_arr, uids)\nu_idx = np.fromiter((uid_to_idx[x] for x in u_arr[u_mask]), dtype=np.int32, count=int(u_mask.sum()))\nit_idx = np.fromiter((item_to_idx[x] for x in it_arr[u_mask]), dtype=np.int32, count=int(u_mask.sum()))\n\n# Clean up arrow table\ndel table, filtered\ngc.collect()\n\ndef fit_svd(half_life_days=10.0, n_components=64, dis_weight=1.0):\n tau = half_life_days * 86400.0\n tw_listens = np.exp((ts_arr[u_mask] - CUTOFF) / tau)\n cw = np.where(pct_arr[u_mask] >= 80, 2.0, np.where(pct_arr[u_mask] >= 50, 1.0, 0.2))\n cw += np.where(org_arr[u_mask] == 1, 2.0, 0.0)\n w_listens = cw * tw_listens\n \n tw_likes = np.exp((likes.timestamp.to_numpy() - CUTOFF) / tau)\n w_likes = (np.where(likes.is_organic.to_numpy() == 1, 6.0, 4.0)) * tw_likes\n \n X1 = sp.csr_matrix((w_listens, (u_idx, it_idx)), shape=(len(uids), len(cat_items)))\n X2 = sp.csr_matrix((w_likes, (l_u, l_it)), shape=(len(uids), len(cat_items)))\n X3 = sp.csr_matrix((np.full(len(pref_u_liked), 5.0), (pref_u_liked, pref_it_liked)), shape=(len(uids), len(cat_items)))\n \n X = X1 + X2 + X3\n X.data = np.log1p(X.data)\n if dis_weight > 0:\n X_dis = sp.csr_matrix((-np.full(len(pref_u_dis), dis_weight), (pref_u_dis, pref_it_dis)), shape=(len(uids), len(cat_items)))\n X = X + X_dis\n \n svd = TruncatedSVD(n_components=n_components, random_state=42)\n U = svd.fit_transform(X).astype(np.float32)\n V = svd.components_.astype(np.float32)\n return U, V\n\nprint('Fitting model 1 (hl=10)...')\nU1, V1 = fit_svd(10.0, 64)\nprint('Fitting model 2 (hl=35)...')\nU2, V2 = fit_svd(35.0, 64)\n\ndef evaluate_blend(w1, w2):\n ranked = {}\n BATCH = 500\n for start in range(0, len(val_uids), BATCH):\n end = min(start + BATCH, len(val_uids))\n batch_uids = val_uids[start:end]\n batch_idx = [uid_to_idx[u] for u in batch_uids]\n \n s1 = U1[batch_idx] @ V1\n s2 = U2[batch_idx] @ V2\n # Normalize each per row\n s1 = s1 / (np.std(s1, axis=1, keepdims=True) + 1e-9)\n s2 = s2 / (np.std(s2, axis=1, keepdims=True) + 1e-9)\n batch_scores = w1 * s1 + w2 * s2\n \n for i, (r_idx, uid) in enumerate(zip(range(start, end), batch_uids)):\n bits = np.unpackbits(val_packed[r_idx], count=len(val_items), bitorder='little')\n user_scores = batch_scores[i].copy()\n mask = np.ones(len(cat_items), dtype=bool)\n mask[val_to_cat[bits == 0]] = False\n user_scores[~mask] = -1e9\n top100_idx = np.argpartition(user_scores, -100)[-100:]\n top100_sorted = top100_idx[np.argsort(-user_scores[top100_idx])]\n ranked[uid] = cat_items[top100_sorted].tolist()\n res = score(ranked, truth)\n print(f'w1={w1:.2f}, w2={w2:.2f} -> NDCG@10 = {res[\\\"quality_ndcg@10\\\"]:.6f}')\n\nfor w2 in [0.0, 0.15, 0.25, 0.35, 0.5]:\n evaluate_blend(1.0, w2)\n\"", "description": "Test multi-timescale blend with safe memory footprint" } ### Tool output · 2026-09-29T01:42:49.014Z Output: Fitting model 1 (hl=10)... Fitting model 2 (hl=35)... w1=1.00, w2=0.00 -> NDCG@10 = 0.039379 w1=1.00, w2=0.15 -> NDCG@10 = 0.038790 w1=1.00, w2=0.25 -> NDCG@10 = 0.038580 w1=1.00, w2=0.35 -> NDCG@10 = 0.038450 w1=1.00, w2=0.50 -> NDCG@10 = 0.038535 Process Group PGID: 2579 ## Step 55 · agent · 2026-09-29T01:43:03.327Z ### Recorded reasoning summary Exploring SVD Components I'm currently investigating the impact of increasing SVD component counts while keeping `hl=10.0` constant. My focus is on how these higher counts influence NDCG@10, and I'm also considering the roles of `org_weight` and likes weight in our objective. ### run_shell_command · 2026-09-29T01:43:03.327Z { "description": "Test n_components and popularity gamma", "command": "python3 -c \"\nimport sys\nsys.path.append('/task')\nimport numpy as np\nimport scipy.sparse as sp\nfrom sklearn.decomposition import TruncatedSVD\nfrom evaluate_public import score, validation_truth\nfrom ranking_evaluation import CandidateSet\nimport pandas as pd\nimport pyarrow.parquet as pq\nimport pyarrow.compute as pc\nimport pyarrow as pa\nimport gc\n\ntarget_users = pd.read_parquet('/task/data/target_users.parquet')\nuids = target_users.uid.to_numpy(dtype=np.int64)\nuid_to_idx = {u: i for i, u in enumerate(uids)}\n\ncat = pd.read_parquet('/task/data/candidate_catalog.parquet')\ncat_items = cat.item_id.to_numpy(dtype=np.int64)\nitem_to_idx = {it: i for i, it in enumerate(cat_items)}\n\n# Popularity feature\norg_pop = cat.organic_positive_listens.to_numpy(dtype=np.float32)\nlog_org_pop = np.log1p(org_pop)\nlog_org_pop = (log_org_pop - log_org_pop.mean()) / (log_org_pop.std() + 1e-9)\n\nwith np.load('/task/data/validation_eligible_candidates.npz') as z:\n val_uids = z['uids'].astype(np.int64)\n val_items = z['item_ids'].astype(np.int64)\n val_packed = z['packed_eligible']\nval_to_cat = np.array([item_to_idx[it] for it in val_items])\n\neligibility = CandidateSet('/task/data/validation_eligible_candidates.npz')\ntruth = validation_truth(eligibility, set(val_uids))\n\nCUTOFF = 24786800.0\n\npref = pd.read_parquet('/task/data/validation_preference_state.parquet')\npref = pref[pref.uid.isin(uid_to_idx) & pref.item_id.isin(item_to_idx)]\np_liked = pref[pref.state == 'liked']\np_disliked = pref[pref.state == 'disliked']\npref_u_liked = np.fromiter((uid_to_idx[x] for x in p_liked.uid), dtype=np.int32, count=len(p_liked))\npref_it_liked = np.fromiter((item_to_idx[x] for x in p_liked.item_id), dtype=np.int32, count=len(p_liked))\npref_u_dis = np.fromiter((uid_to_idx[x] for x in p_disliked.uid), dtype=np.int32, count=len(p_disliked))\npref_it_dis = np.fromiter((item_to_idx[x] for x in p_disliked.item_id), dtype=np.int32, count=len(p_disliked))\n\nlikes = pd.read_parquet('/task/data/train_likes.parquet')\nlikes = likes[likes.uid.isin(uid_to_idx) & likes.item_id.isin(item_to_idx)]\nl_u = np.fromiter((uid_to_idx[x] for x in likes.uid), dtype=np.int32, count=len(likes))\nl_it = np.fromiter((item_to_idx[x] for x in likes.item_id), dtype=np.int32, count=len(likes))\n\nitem_set = pc.SetLookupOptions(value_set=pa.array(cat_items))\ntable = pq.read_table('/task/data/train_listens.parquet', \n filters=[('timestamp', '>=', int(CUTOFF - 120 * 86400))],\n columns=['uid', 'item_id', 'timestamp', 'played_ratio_pct', 'is_organic'])\nfiltered = table.filter(pc.is_in(table['item_id'], options=item_set))\n\nu_arr = filtered['uid'].to_numpy()\nit_arr = filtered['item_id'].to_numpy()\npct_arr = filtered['played_ratio_pct'].to_numpy()\norg_arr = filtered['is_organic'].to_numpy()\nts_arr = filtered['timestamp'].to_numpy()\n\nu_mask = np.isin(u_arr, uids)\nu_idx = np.fromiter((uid_to_idx[x] for x in u_arr[u_mask]), dtype=np.int32, count=int(u_mask.sum()))\nit_idx = np.fromiter((item_to_idx[x] for x in it_arr[u_mask]), dtype=np.int32, count=int(u_mask.sum()))\n\ndel table, filtered\ngc.collect()\n\ndef build_and_evaluate(half_life_days=10.0, n_components=64, org_weight=2.0, pop_gamma=0.0):\n tau = half_life_days * 86400.0\n tw_listens = np.exp((ts_arr[u_mask] - CUTOFF) / tau)\n cw = np.where(pct_arr[u_mask] >= 80, 2.0, np.where(pct_arr[u_mask] >= 50, 1.0, 0.2))\n cw += np.where(org_arr[u_mask] == 1, org_weight, 0.0)\n w_listens = cw * tw_listens\n \n tw_likes = np.exp((likes.timestamp.to_numpy() - CUTOFF) / tau)\n w_likes = (np.where(likes.is_organic.to_numpy() == 1, 6.0, 4.0)) * tw_likes\n \n X1 = sp.csr_matrix((w_listens, (u_idx, it_idx)), shape=(len(uids), len(cat_items)))\n X2 = sp.csr_matrix((w_likes, (l_u, l_it)), shape=(len(uids), len(cat_items)))\n X3 = sp.csr_matrix((np.full(len(pref_u_liked), 5.0), (pref_u_liked, pref_it_liked)), shape=(len(uids), len(cat_items)))\n \n X = X1 + X2 + X3\n X.data = np.log1p(X.data)\n X_dis = sp.csr_matrix((-np.full(len(pref_u_dis), 1.0), (pref_u_dis, pref_it_dis)), shape=(len(uids), len(cat_items)))\n X = X + X_dis\n \n svd = TruncatedSVD(n_components=n_components, random_state=42)\n U = svd.fit_transform(X).astype(np.float32)\n V = svd.components_.astype(np.float32)\n \n ranked = {}\n BATCH = 500\n for start in range(0, len(val_uids), BATCH):\n end = min(start + BATCH, len(val_uids))\n batch_uids = val_uids[start:end]\n batch_idx = [uid_to_idx[u] for u in batch_uids]\n \n batch_scores = U[batch_idx] @ V\n if pop_gamma > 0:\n batch_scores = batch_scores + pop_gamma * log_org_pop\n \n for i, (r_idx, uid) in enumerate(zip(range(start, end), batch_uids)):\n bits = np.unpackbits(val_packed[r_idx], count=len(val_items), bitorder='little')\n user_scores = batch_scores[i].copy()\n mask = np.ones(len(cat_items), dtype=bool)\n mask[val_to_cat[bits == 0]] = False\n user_scores[~mask] = -1e9\n top100_idx = np.argpartition(user_scores, -100)[-100:]\n top100_sorted = top100_idx[np.argsort(-user_scores[top100_idx])]\n ranked[uid] = cat_items[top100_sorted].tolist()\n res = score(ranked, truth)\n return res['quality_ndcg@10']\n\nfor n_c in [64, 96, 128]:\n for gamma in [0.0, 0.05, 0.1]:\n ndcg = build_and_evaluate(half_life_days=10.0, n_components=n_c, pop_gamma=gamma)\n print(f'n_comp={n_c:3d}, gamma={gamma:4.2f} -> NDCG@10 = {ndcg:.6f}')\n\"" } ### Tool output · 2026-09-29T01:44:25.682Z Output: n_comp= 64, gamma=0.00 -> NDCG@10 = 0.039379 n_comp= 64, gamma=0.05 -> NDCG@10 = 0.033986 n_comp= 64, gamma=0.10 -> NDCG@10 = 0.030370 n_comp= 96, gamma=0.00 -> NDCG@10 = 0.038020 n_comp= 96, gamma=0.05 -> NDCG@10 = 0.034237 n_comp= 96, gamma=0.10 -> NDCG@10 = 0.030247 n_comp=128, gamma=0.00 -> NDCG@10 = 0.038432 n_comp=128, gamma=0.05 -> NDCG@10 = 0.034034 n_comp=128, gamma=0.10 -> NDCG@10 = 0.030205 Process Group PGID: 2608 ## Step 56 · agent · 2026-09-29T01:44:39.777Z ### Recorded reasoning summary Refining Popularity Logic I've discovered the popularity bonus is counterproductive, likely because SVD inherently captures item affinities. Including global popularity seems to dilute individual user preferences by boosting generic tracks. My next step is to investigate further, starting with the `half` parameter. ### Recorded reasoning summary Exploring Parameter Variations I'm currently investigating the impact of different `life_days` values on model performance, specifically testing integers between 8.0 and 12.0. Concurrently, I'm evaluating alternative power transformations beyond `log1p`, like `log1p(c*X)` with varying `c` and `power(X, p)` with `p` as 0.4 or 0.5. ### run_shell_command · 2026-09-29T01:44:39.777Z { "description": "Test fine half-lives and power transformations", "command": "python3 -c \"\nimport sys\nsys.path.append('/task')\nimport numpy as np\nimport scipy.sparse as sp\nfrom sklearn.decomposition import TruncatedSVD\nfrom evaluate_public import score, validation_truth\nfrom ranking_evaluation import CandidateSet\nimport pandas as pd\nimport pyarrow.parquet as pq\nimport pyarrow.compute as pc\nimport pyarrow as pa\nimport gc\n\ntarget_users = pd.read_parquet('/task/data/target_users.parquet')\nuids = target_users.uid.to_numpy(dtype=np.int64)\nuid_to_idx = {u: i for i, u in enumerate(uids)}\n\ncat = pd.read_parquet('/task/data/candidate_catalog.parquet')\ncat_items = cat.item_id.to_numpy(dtype=np.int64)\nitem_to_idx = {it: i for i, it in enumerate(cat_items)}\n\nwith np.load('/task/data/validation_eligible_candidates.npz') as z:\n val_uids = z['uids'].astype(np.int64)\n val_items = z['item_ids'].astype(np.int64)\n val_packed = z['packed_eligible']\nval_to_cat = np.array([item_to_idx[it] for it in val_items])\n\neligibility = CandidateSet('/task/data/validation_eligible_candidates.npz')\ntruth = validation_truth(eligibility, set(val_uids))\n\nCUTOFF = 24786800.0\n\npref = pd.read_parquet('/task/data/validation_preference_state.parquet')\npref = pref[pref.uid.isin(uid_to_idx) & pref.item_id.isin(item_to_idx)]\np_liked = pref[pref.state == 'liked']\np_disliked = pref[pref.state == 'disliked']\npref_u_liked = np.fromiter((uid_to_idx[x] for x in p_liked.uid), dtype=np.int32, count=len(p_liked))\npref_it_liked = np.fromiter((item_to_idx[x] for x in p_liked.item_id), dtype=np.int32, count=len(p_liked))\npref_u_dis = np.fromiter((uid_to_idx[x] for x in p_disliked.uid), dtype=np.int32, count=len(p_disliked))\npref_it_dis = np.fromiter((item_to_idx[x] for x in p_disliked.item_id), dtype=np.int32, count=len(p_disliked))\n\nlikes = pd.read_parquet('/task/data/train_likes.parquet')\nlikes = likes[likes.uid.isin(uid_to_idx) & likes.item_id.isin(item_to_idx)]\nl_u = np.fromiter((uid_to_idx[x] for x in likes.uid), dtype=np.int32, count=len(likes))\nl_it = np.fromiter((item_to_idx[x] for x in likes.item_id), dtype=np.int32, count=len(likes))\n\nitem_set = pc.SetLookupOptions(value_set=pa.array(cat_items))\ntable = pq.read_table('/task/data/train_listens.parquet', \n filters=[('timestamp', '>=', int(CUTOFF - 120 * 86400))],\n columns=['uid', 'item_id', 'timestamp', 'played_ratio_pct', 'is_organic'])\nfiltered = table.filter(pc.is_in(table['item_id'], options=item_set))\n\nu_arr = filtered['uid'].to_numpy()\nit_arr = filtered['item_id'].to_numpy()\npct_arr = filtered['played_ratio_pct'].to_numpy()\norg_arr = filtered['is_organic'].to_numpy()\nts_arr = filtered['timestamp'].to_numpy()\n\nu_mask = np.isin(u_arr, uids)\nu_idx = np.fromiter((uid_to_idx[x] for x in u_arr[u_mask]), dtype=np.int32, count=int(u_mask.sum()))\nit_idx = np.fromiter((item_to_idx[x] for x in it_arr[u_mask]), dtype=np.int32, count=int(u_mask.sum()))\n\ndel table, filtered\ngc.collect()\n\ndef run_test(half_life_days, transform_type, param, n_comp=64):\n tau = half_life_days * 86400.0\n tw_listens = np.exp((ts_arr[u_mask] - CUTOFF) / tau)\n cw = np.where(pct_arr[u_mask] >= 80, 2.0, np.where(pct_arr[u_mask] >= 50, 1.0, 0.2))\n cw += np.where(org_arr[u_mask] == 1, 2.0, 0.0)\n w_listens = cw * tw_listens\n \n tw_likes = np.exp((likes.timestamp.to_numpy() - CUTOFF) / tau)\n w_likes = (np.where(likes.is_organic.to_numpy() == 1, 6.0, 4.0)) * tw_likes\n \n X1 = sp.csr_matrix((w_listens, (u_idx, it_idx)), shape=(len(uids), len(cat_items)))\n X2 = sp.csr_matrix((w_likes, (l_u, l_it)), shape=(len(uids), len(cat_items)))\n X3 = sp.csr_matrix((np.full(len(pref_u_liked), 5.0), (pref_u_liked, pref_it_liked)), shape=(len(uids), len(cat_items)))\n \n X = X1 + X2 + X3\n if transform_type == 'log1p':\n X.data = np.log1p(param * X.data)\n elif transform_type == 'power':\n X.data = np.power(X.data, param)\n \n X_dis = sp.csr_matrix((-np.full(len(pref_u_dis), 1.0), (pref_u_dis, pref_it_dis)), shape=(len(uids), len(cat_items)))\n X = X + X_dis\n \n svd = TruncatedSVD(n_components=n_comp, random_state=42)\n U = svd.fit_transform(X).astype(np.float32)\n V = svd.components_.astype(np.float32)\n \n ranked = {}\n BATCH = 500\n for start in range(0, len(val_uids), BATCH):\n end = min(start + BATCH, len(val_uids))\n batch_uids = val_uids[start:end]\n batch_idx = [uid_to_idx[u] for u in batch_uids]\n batch_scores = U[batch_idx] @ V\n for i, (r_idx, uid) in enumerate(zip(range(start, end), batch_uids)):\n bits = np.unpackbits(val_packed[r_idx], count=len(val_items), bitorder='little')\n user_scores = batch_scores[i].copy()\n mask = np.ones(len(cat_items), dtype=bool)\n mask[val_to_cat[bits == 0]] = False\n user_scores[~mask] = -1e9\n top100_idx = np.argpartition(user_scores, -100)[-100:]\n top100_sorted = top100_idx[np.argsort(-user_scores[top100_idx])]\n ranked[uid] = cat_items[top100_sorted].tolist()\n res = score(ranked, truth)\n return res['quality_ndcg@10']\n\nfor hl in [8.0, 9.0, 10.0, 11.0, 12.0]:\n ndcg = run_test(hl, 'log1p', 1.0, 64)\n print(f'hl={hl:4.1f}, log1p(1.0) -> NDCG@10 = {ndcg:.6f}')\n\nfor p in [0.3, 0.4, 0.5, 0.6]:\n ndcg = run_test(9.0, 'power', p, 64)\n print(f'hl=9.0, power({p:.1f}) -> NDCG@10 = {ndcg:.6f}')\n\"" } ### Tool output · 2026-09-29T01:45:52.379Z Output: hl= 8.0, log1p(1.0) -> NDCG@10 = 0.039347 hl= 9.0, log1p(1.0) -> NDCG@10 = 0.039269 hl=10.0, log1p(1.0) -> NDCG@10 = 0.039379 hl=11.0, log1p(1.0) -> NDCG@10 = 0.038408 hl=12.0, log1p(1.0) -> NDCG@10 = 0.037942 hl=9.0, power(0.3) -> NDCG@10 = 0.037865 hl=9.0, power(0.4) -> NDCG@10 = 0.038516 hl=9.0, power(0.5) -> NDCG@10 = 0.039264 hl=9.0, power(0.6) -> NDCG@10 = 0.037893 Process Group PGID: 2637 ## Step 57 · agent · 2026-09-29T01:48:41.837Z ### Recorded reasoning summary Exploring Unlikes/Undislikes I'm currently investigating the impact of unlikes and undislikes. My initial thought was to directly add these, but I've paused to confirm if `validation_preference_state.parquet` already accounts for them, as the "active explicit" label suggests. ### run_shell_command · 2026-09-29T01:48:41.837Z { "command": "python3 -c \"\nimport sys\nsys.path.append('/task')\nimport numpy as np\nimport scipy.sparse as sp\nfrom sklearn.decomposition import TruncatedSVD\nfrom evaluate_public import score, validation_truth\nfrom ranking_evaluation import CandidateSet\nimport pandas as pd\nimport pyarrow.parquet as pq\nimport pyarrow.compute as pc\nimport pyarrow as pa\nimport gc\n\ntarget_users = pd.read_parquet('/task/data/target_users.parquet')\nuids = target_users.uid.to_numpy(dtype=np.int64)\nuid_to_idx = {u: i for i, u in enumerate(uids)}\n\ncat = pd.read_parquet('/task/data/candidate_catalog.parquet')\ncat_items = cat.item_id.to_numpy(dtype=np.int64)\nitem_to_idx = {it: i for i, it in enumerate(cat_items)}\n\nwith np.load('/task/data/validation_eligible_candidates.npz') as z:\n val_uids = z['uids'].astype(np.int64)\n val_items = z['item_ids'].astype(np.int64)\n val_packed = z['packed_eligible']\nval_to_cat = np.array([item_to_idx[it] for it in val_items])\n\neligibility = CandidateSet('/task/data/validation_eligible_candidates.npz')\ntruth = validation_truth(eligibility, set(val_uids))\n\nCUTOFF = 24786800.0\n\npref = pd.read_parquet('/task/data/validation_preference_state.parquet')\npref = pref[pref.uid.isin(uid_to_idx) & pref.item_id.isin(item_to_idx)]\np_liked = pref[pref.state == 'liked']\np_disliked = pref[pref.state == 'disliked']\npref_u_liked = np.fromiter((uid_to_idx[x] for x in p_liked.uid), dtype=np.int32, count=len(p_liked))\npref_it_liked = np.fromiter((item_to_idx[x] for x in p_liked.item_id), dtype=np.int32, count=len(p_liked))\npref_u_dis = np.fromiter((uid_to_idx[x] for x in p_disliked.uid), dtype=np.int32, count=len(p_disliked))\npref_it_dis = np.fromiter((item_to_idx[x] for x in p_disliked.item_id), dtype=np.int32, count=len(p_disliked))\n\nlikes = pd.read_parquet('/task/data/train_likes.parquet')\nlikes = likes[likes.uid.isin(uid_to_idx) & likes.item_id.isin(item_to_idx)]\nl_u = np.fromiter((uid_to_idx[x] for x in likes.uid), dtype=np.int32, count=len(likes))\nl_it = np.fromiter((item_to_idx[x] for x in likes.item_id), dtype=np.int32, count=len(likes))\n\nitem_set = pc.SetLookupOptions(value_set=pa.array(cat_items))\ntable = pq.read_table('/task/data/train_listens.parquet', \n filters=[('timestamp', '>=', int(CUTOFF - 120 * 86400))],\n columns=['uid', 'item_id', 'timestamp', 'played_ratio_pct', 'is_organic'])\nfiltered = table.filter(pc.is_in(table['item_id'], options=item_set))\n\nu_arr = filtered['uid'].to_numpy()\nit_arr = filtered['item_id'].to_numpy()\npct_arr = filtered['played_ratio_pct'].to_numpy()\norg_arr = filtered['is_organic'].to_numpy()\nts_arr = filtered['timestamp'].to_numpy()\n\nu_mask = np.isin(u_arr, uids)\nu_idx = np.fromiter((uid_to_idx[x] for x in u_arr[u_mask]), dtype=np.int32, count=int(u_mask.sum()))\nit_idx = np.fromiter((item_to_idx[x] for x in it_arr[u_mask]), dtype=np.int32, count=int(u_mask.sum()))\n\ndel table, filtered\ngc.collect()\n\ndef test_weights(org_listen_bonus, org_like_w, pref_like_w):\n tau = 10.0 * 86400.0\n tw_listens = np.exp((ts_arr[u_mask] - CUTOFF) / tau)\n cw = np.where(pct_arr[u_mask] >= 80, 2.0, np.where(pct_arr[u_mask] >= 50, 1.0, 0.2))\n cw += np.where(org_arr[u_mask] == 1, org_listen_bonus, 0.0)\n w_listens = cw * tw_listens\n \n tw_likes = np.exp((likes.timestamp.to_numpy() - CUTOFF) / tau)\n w_likes = (np.where(likes.is_organic.to_numpy() == 1, org_like_w, 4.0)) * tw_likes\n \n X1 = sp.csr_matrix((w_listens, (u_idx, it_idx)), shape=(len(uids), len(cat_items)))\n X2 = sp.csr_matrix((w_likes, (l_u, l_it)), shape=(len(uids), len(cat_items)))\n X3 = sp.csr_matrix((np.full(len(pref_u_liked), pref_like_w), (pref_u_liked, pref_it_liked)), shape=(len(uids), len(cat_items)))\n \n X = X1 + X2 + X3\n X.data = np.log1p(X.data)\n X_dis = sp.csr_matrix((-np.full(len(pref_u_dis), 1.0), (pref_u_dis, pref_it_dis)), shape=(len(uids), len(cat_items)))\n X = X + X_dis\n \n svd = TruncatedSVD(n_components=64, random_state=42)\n U = svd.fit_transform(X).astype(np.float32)\n V = svd.components_.astype(np.float32)\n \n ranked = {}\n BATCH = 500\n for start in range(0, len(val_uids), BATCH):\n end = min(start + BATCH, len(val_uids))\n batch_uids = val_uids[start:end]\n batch_idx = [uid_to_idx[u] for u in batch_uids]\n batch_scores = U[batch_idx] @ V\n for i, (r_idx, uid) in enumerate(zip(range(start, end), batch_uids)):\n bits = np.unpackbits(val_packed[r_idx], count=len(val_items), bitorder='little')\n user_scores = batch_scores[i].copy()\n mask = np.ones(len(cat_items), dtype=bool)\n mask[val_to_cat[bits == 0]] = False\n user_scores[~mask] = -1e9\n top100_idx = np.argpartition(user_scores, -100)[-100:]\n top100_sorted = top100_idx[np.argsort(-user_scores[top100_idx])]\n ranked[uid] = cat_items[top100_sorted].tolist()\n res = score(ranked, truth)\n return res['quality_ndcg@10']\n\nfor org_b in [1.0, 2.0, 3.0, 4.0]:\n for org_l in [6.0, 10.0]:\n for pref_l in [3.0, 5.0, 8.0]:\n ndcg = test_weights(org_b, org_l, pref_l)\n print(f'org_b={org_b:.1f}, org_l={org_l:.1f}, pref_l={pref_l:.1f} -> NDCG@10 = {ndcg:.6f}')\n\"", "description": "Test interaction event weights grid" } ### Tool output · 2026-09-29T01:51:39.985Z Output: org_b=1.0, org_l=6.0, pref_l=3.0 -> NDCG@10 = 0.038661 org_b=1.0, org_l=6.0, pref_l=5.0 -> NDCG@10 = 0.038202 org_b=1.0, org_l=6.0, pref_l=8.0 -> NDCG@10 = 0.036613 org_b=1.0, org_l=10.0, pref_l=3.0 -> NDCG@10 = 0.038810 org_b=1.0, org_l=10.0, pref_l=5.0 -> NDCG@10 = 0.038652 org_b=1.0, org_l=10.0, pref_l=8.0 -> NDCG@10 = 0.036688 org_b=2.0, org_l=6.0, pref_l=3.0 -> NDCG@10 = 0.038739 org_b=2.0, org_l=6.0, pref_l=5.0 -> NDCG@10 = 0.039379 org_b=2.0, org_l=6.0, pref_l=8.0 -> NDCG@10 = 0.037763 org_b=2.0, org_l=10.0, pref_l=3.0 -> NDCG@10 = 0.038921 org_b=2.0, org_l=10.0, pref_l=5.0 -> NDCG@10 = 0.039163 org_b=2.0, org_l=10.0, pref_l=8.0 -> NDCG@10 = 0.037839 org_b=3.0, org_l=6.0, pref_l=3.0 -> NDCG@10 = 0.038768 org_b=3.0, org_l=6.0, pref_l=5.0 -> NDCG@10 = 0.038589 org_b=3.0, org_l=6.0, pref_l=8.0 -> NDCG@10 = 0.038598 org_b=3.0, org_l=10.0, pref_l=3.0 -> NDCG@10 = 0.038939 org_b=3.0, org_l=10.0, pref_l=5.0 -> NDCG@10 = 0.038756 org_b=3.0, org_l=10.0, pref_l=8.0 -> NDCG@10 = 0.038819 org_b=4.0, org_l=6.0, pref_l=3.0 -> NDCG@10 = 0.039789 org_b=4.0, org_l=6.0, pref_l=5.0 -> NDCG@10 = 0.038557 org_b=4.0, org_l=6.0, pref_l=8.0 -> NDCG@10 = 0.039228 org_b=4.0, org_l=10.0, pref_l=3.0 -> NDCG@10 = 0.039756 org_b=4.0, org_l=10.0, pref_l=5.0 -> NDCG@10 = 0.038666 org_b=4.0, org_l=10.0, pref_l=8.0 -> NDCG@10 = 0.039095 Process Group PGID: 2666 ## Step 58 · agent · 2026-09-29T01:51:51.064Z ### Recorded reasoning summary Testing Organic Boost My focus is on optimizing the 'org_b' parameter to maximize NDCG@10. I've observed that increasing it from 4.0 to 5.0 and then to 6.0 shows promise. My next step is to thoroughly evaluate these higher values, as organic listens are the critical metric. ### run_shell_command · 2026-09-29T01:51:51.064Z { "description": "Test higher organic listen bonus", "command": "python3 -c \"\nimport sys\nsys.path.append('/task')\nimport numpy as np\nimport scipy.sparse as sp\nfrom sklearn.decomposition import TruncatedSVD\nfrom evaluate_public import score, validation_truth\nfrom ranking_evaluation import CandidateSet\nimport pandas as pd\nimport pyarrow.parquet as pq\nimport pyarrow.compute as pc\nimport pyarrow as pa\nimport gc\n\ntarget_users = pd.read_parquet('/task/data/target_users.parquet')\nuids = target_users.uid.to_numpy(dtype=np.int64)\nuid_to_idx = {u: i for i, u in enumerate(uids)}\n\ncat = pd.read_parquet('/task/data/candidate_catalog.parquet')\ncat_items = cat.item_id.to_numpy(dtype=np.int64)\nitem_to_idx = {it: i for i, it in enumerate(cat_items)}\n\nwith np.load('/task/data/validation_eligible_candidates.npz') as z:\n val_uids = z['uids'].astype(np.int64)\n val_items = z['item_ids'].astype(np.int64)\n val_packed = z['packed_eligible']\nval_to_cat = np.array([item_to_idx[it] for it in val_items])\n\neligibility = CandidateSet('/task/data/validation_eligible_candidates.npz')\ntruth = validation_truth(eligibility, set(val_uids))\n\nCUTOFF = 24786800.0\n\npref = pd.read_parquet('/task/data/validation_preference_state.parquet')\npref = pref[pref.uid.isin(uid_to_idx) & pref.item_id.isin(item_to_idx)]\np_liked = pref[pref.state == 'liked']\np_disliked = pref[pref.state == 'disliked']\npref_u_liked = np.fromiter((uid_to_idx[x] for x in p_liked.uid), dtype=np.int32, count=len(p_liked))\npref_it_liked = np.fromiter((item_to_idx[x] for x in p_liked.item_id), dtype=np.int32, count=len(p_liked))\npref_u_dis = np.fromiter((uid_to_idx[x] for x in p_disliked.uid), dtype=np.int32, count=len(p_disliked))\npref_it_dis = np.fromiter((item_to_idx[x] for x in p_disliked.item_id), dtype=np.int32, count=len(p_disliked))\n\nlikes = pd.read_parquet('/task/data/train_likes.parquet')\nlikes = likes[likes.uid.isin(uid_to_idx) & likes.item_id.isin(item_to_idx)]\nl_u = np.fromiter((uid_to_idx[x] for x in likes.uid), dtype=np.int32, count=len(likes))\nl_it = np.fromiter((item_to_idx[x] for x in likes.item_id), dtype=np.int32, count=len(likes))\n\nitem_set = pc.SetLookupOptions(value_set=pa.array(cat_items))\ntable = pq.read_table('/task/data/train_listens.parquet', \n filters=[('timestamp', '>=', int(CUTOFF - 120 * 86400))],\n columns=['uid', 'item_id', 'timestamp', 'played_ratio_pct', 'is_organic'])\nfiltered = table.filter(pc.is_in(table['item_id'], options=item_set))\n\nu_arr = filtered['uid'].to_numpy()\nit_arr = filtered['item_id'].to_numpy()\npct_arr = filtered['played_ratio_pct'].to_numpy()\norg_arr = filtered['is_organic'].to_numpy()\nts_arr = filtered['timestamp'].to_numpy()\n\nu_mask = np.isin(u_arr, uids)\nu_idx = np.fromiter((uid_to_idx[x] for x in u_arr[u_mask]), dtype=np.int32, count=int(u_mask.sum()))\nit_idx = np.fromiter((item_to_idx[x] for x in it_arr[u_mask]), dtype=np.int32, count=int(u_mask.sum()))\n\ndel table, filtered\ngc.collect()\n\ndef test_weights(org_listen_bonus):\n tau = 10.0 * 86400.0\n tw_listens = np.exp((ts_arr[u_mask] - CUTOFF) / tau)\n cw = np.where(pct_arr[u_mask] >= 80, 2.0, np.where(pct_arr[u_mask] >= 50, 1.0, 0.2))\n cw += np.where(org_arr[u_mask] == 1, org_listen_bonus, 0.0)\n w_listens = cw * tw_listens\n \n tw_likes = np.exp((likes.timestamp.to_numpy() - CUTOFF) / tau)\n w_likes = (np.where(likes.is_organic.to_numpy() == 1, 6.0, 4.0)) * tw_likes\n \n X1 = sp.csr_matrix((w_listens, (u_idx, it_idx)), shape=(len(uids), len(cat_items)))\n X2 = sp.csr_matrix((w_likes, (l_u, l_it)), shape=(len(uids), len(cat_items)))\n X3 = sp.csr_matrix((np.full(len(pref_u_liked), 3.0), (pref_u_liked, pref_it_liked)), shape=(len(uids), len(cat_items)))\n \n X = X1 + X2 + X3\n X.data = np.log1p(X.data)\n X_dis = sp.csr_matrix((-np.full(len(pref_u_dis), 1.0), (pref_u_dis, pref_it_dis)), shape=(len(uids), len(cat_items)))\n X = X + X_dis\n \n svd = TruncatedSVD(n_components=64, random_state=42)\n U = svd.fit_transform(X).astype(np.float32)\n V = svd.components_.astype(np.float32)\n \n ranked = {}\n BATCH = 500\n for start in range(0, len(val_uids), BATCH):\n end = min(start + BATCH, len(val_uids))\n batch_uids = val_uids[start:end]\n batch_idx = [uid_to_idx[u] for u in batch_uids]\n batch_scores = U[batch_idx] @ V\n for i, (r_idx, uid) in enumerate(zip(range(start, end), batch_uids)):\n bits = np.unpackbits(val_packed[r_idx], count=len(val_items), bitorder='little')\n user_scores = batch_scores[i].copy()\n mask = np.ones(len(cat_items), dtype=bool)\n mask[val_to_cat[bits == 0]] = False\n user_scores[~mask] = -1e9\n top100_idx = np.argpartition(user_scores, -100)[-100:]\n top100_sorted = top100_idx[np.argsort(-user_scores[top100_idx])]\n ranked[uid] = cat_items[top100_sorted].tolist()\n res = score(ranked, truth)\n return res['quality_ndcg@10']\n\nfor ob in [3.0, 4.0, 5.0, 6.0, 8.0]:\n ndcg = test_weights(ob)\n print(f'org_listen_bonus={ob:.1f} -> NDCG@10 = {ndcg:.6f}')\n\"" } ### Tool output · 2026-09-29T01:52:35.739Z Output: org_listen_bonus=3.0 -> NDCG@10 = 0.038768 org_listen_bonus=4.0 -> NDCG@10 = 0.039789 org_listen_bonus=5.0 -> NDCG@10 = 0.039753 org_listen_bonus=6.0 -> NDCG@10 = 0.039323 org_listen_bonus=8.0 -> NDCG@10 = 0.039805 Process Group PGID: 2695 ## Recorded session usage [ { "messageId": "738bdd11-9913-4860-9a0e-fc33ddd96506", "timestamp": "2026-09-29T01:22:44.243Z", "model": "gemini-3.8-flash", "tokens": { "input": 9722, "output": 127, "cached": 0, "thoughts": 350, "tool": 0, "total": 10199 } }, { "messageId": "d43aab01-0797-4923-8cda-89b53c858e34", "timestamp": "2026-09-29T01:22:46.258Z", "model": "gemini-3.8-flash", "tokens": { "input": 11101, "output": 69, "cached": 7650, "thoughts": 33, "tool": 0, "total": 11203 } }, { "messageId": "6ee159b0-273c-4d70-85e2-bd60f2d24ecb", 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