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Two-tower retriever gemini-3-8-flash-high-05

The full recorded conversation, expanded from start to finish: instructions, agent messages, and tool inputs and responses.

This recording includes 182 readable reasoning summaries, shown in full below.

Agent
gemini-3.8-flash · Gemini CLI · high effort
Recorded conversation
59:32 · 86 steps
Private reward
0.009092 · Failed
Attempt cost
$1.79
  • Thinking
  • Model text
  • Tool call
  • Tool response
01

User context

+00:00
User context

<session_context> This is the Gemini CLI. We are setting up the context for our chat. Today's date is Tuesday, September 29, 2026 (formatted according to the user's locale). My operating system is: linux The project's temporary directory is: /home/agent/.gemini/tmp/app

  • Workspace Directories:
  • /app
  • /task
  • Directory Structure:

Showing up to 200 items (files + folders).

/app/ └───solution/ Showing up to 200 items (files + folders).

/task/ ├───.keep ├───evaluate_public.py ├───ranking_evaluation.py ├───requirements.txt └───data/ ├───candidate_catalog.parquet ├───target_users.parquet ├───test_eligible_candidates.npz ├───test_preference_state.parquet ├───train_dislikes.parquet ├───train_likes.parquet ├───train_listens.parquet ├───train_undislikes.parquet ├───train_unlikes.parquet ├───val_dislikes.parquet ├───val_likes.parquet ├───val_listens.parquet ├───val_undislikes.parquet ├───val_unlikes.parquet ├───validation_eligible_candidates.npz └───validation_preference_state.parquet

</session_context>

02

Task instructions

+00:00
Task instructions

Build a latency-efficient PyTorch two-tower neural-network ranker

Build a two-tower neural network in CPU PyTorch for the "Made for You" shelf of a music streaming service. Although the supplied benchmark is small, it represents a production system with millions of users and millions of items; the training approach must scale to that setting and therefore must not construct or decompose a global user-item interaction matrix. The production shelf has a strict request-time latency budget. Candidate item embeddings are computed offline and stored in a nearest-neighbor index. At request time, the system computes one user embedding and retrieves items by maximum inner-product search.

The model must therefore decompose exactly into a user vector and an independently precomputable item vector of the same dimensionality, with the final relevance score equal to their dot product. Both vectors must be produced by separate trainable user and item towers implemented as PyTorch neural networks. The user tower must derive its representation from interaction history rather than a user-ID lookup. Per-user candidate eligibility, cutoff-specific explicit preference state, and a temporal backtest are provided. Candidate eligibility is platform-owned; all modeling within that candidate boundary is in scope.

Data

All inputs are under /task/data/. Training data ends at timestamp 24786800. A 30-minute gap precedes a seven-day public validation window. The hidden test is the following seven-day window after another 30-minute gap.

For each event family there is a train_*.parquet and val_*.parquet file:

FamilyMeaningColumns
listensPlayback eventsuid, timestamp, item_id, is_organic, played_ratio_pct, track_length_seconds
likesExplicit positive actionsuid, timestamp, item_id, is_organic
dislikesExplicit negative actionsuid, timestamp, item_id, is_organic
unlikesReversal of a prior likeuid, timestamp, item_id, is_organic
undislikesReversal of a prior dislikeuid, 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:

python /task/evaluate_public.py solution/validation.parquet

Stage-independent evaluation helpers are available in /task/ranking_evaluation.py. The builder installs the same checked-in module in the public agent image and private verifier image so scoring mechanics cannot drift. The public copy contains no hidden labels, evaluator-owned data paths, or pass thresholds.

The public evaluator uses validation_eligible_candidates.npz; it is not a validator for the final artifact. No model implementation, fitted representation, or example submission is supplied. Design and tune the required PyTorch neural two-tower model, then refit the selected approach as described above.

Deliverable

Write /app/solution/submission.parquet or /app/solution/submission.csv, with exactly 100 rows for every target user and columns uid, item_id, rank. Ranks must be the integers 1-100 with no ties or duplicate items. Every submitted item must be present in that user's test_eligible_candidates.npz candidate set.

Leave the code that defines, trains, and applies both towers, along with a concise /app/solution/README.md, in /app/solution/. The README should state the model architecture, training objective, public validation score, and final submission command. Keep the best valid submission saved throughout the attempt so it survives the deadline.

Constraints

  • Write deliverables and intermediate artifacts only under /app/solution/.
  • Treat /task/ and the installed Python environment as read-only.
  • Do not invoke nested agents or attempt to access verifier files, credentials, other trials, or host paths.
  • Do not install packages or use network access except for the model provider connection managed by Harbor.
  • The environment includes CPU PyTorch 2.8.0. No accelerator is available.
  • You have up to 60 minutes. Improve the model as much as possible within that limit. Structural validity alone is not sufficient; the final submission is also evaluated for ranking quality.
03

update_topic command

+00:06
Recorded reasoning summary 44 words

Establishing Initial Thoughts

I'm currently focusing on grasping the core requirements for building a low-latency PyTorch two-tower ranker for the "Made for You" music shelf. Before diving deeper, I'm making sure to thoroughly review the prompt, available tools, and all stated rules and constraints.

Recorded reasoning summary 50 words

Defining Core Structure

My current focus is on understanding the foundational aspects of this project. I'm carefully dissecting the prompt to ensure a clear grasp of all requirements and constraints, particularly concerning the PyTorch two-tower ranker, the "Made for You" music shelf, and the specific operational guidelines and deliverable formats.

Recorded reasoning summary 47 words

Analyzing Architectural Constraints

I'm now zeroing in on the specific architectural mandates for this PyTorch two-tower ranker. My thoughts are centered on how to design the user tower to leverage interaction history, ensuring offline computation of item embeddings for MIPS, and maintaining the strict request-time latency budget.

Tool callupdate_topicTool call Returned

Tool call · Input 

{
  "strategic_intent": "Inspect data schemas, evaluation scripts, target user counts, and candidate shapes.",
  "title": "Research and Environment Exploration",
  "summary": "Starting the investigation into the task requirements, inspecting dataset sizes, schemas, evaluation tools, and system specs. This will lay the groundwork for designing an efficient PyTorch two-tower architecture."
}

Tool response 

Current topic: "Research and Environment Exploration"
Topic summary: Starting the investigation into the task requirements, inspecting dataset sizes, schemas, evaluation tools, and system specs. This will lay the groundwork for designing an efficient PyTorch two-tower architecture.

Strategic Intent: Inspect data schemas, evaluation scripts, target user counts, and candidate shapes.
04

read_file requirements.txt

+00:09
Tool callread_file/task/requirements.txt Returned

Tool call · Input 

{
  "file_path": "/task/requirements.txt"
}

Tool response 

# Exact distribution inventory for the Yambda two-tower task family.
#
# This is an input to `uv pip sync`, not a list of minimum requirements. A
# conforming task environment contains exactly these third-party Python
# distributions and no others. Python itself is pinned by task.toml and both
# Harbor image Dockerfiles.
attrs==26.1.0
bleach==6.4.0
certifi==2026.7.22
chardet==7.6.0
charset-normalizer==3.5.1
fastjsonschema==2.22.2
filelock==3.32.3
fsspec==2026.7.0
idna==3.18
Jinja2==3.1.6
joblib==1.5.3
jsonschema==4.26.0
jsonschema-specifications==2025.9.1
jupyter-core==5.9.1
jupytext==1.19.5
kaggle==2.2.4
kagglesdk==0.1.37
markdown-it-py==4.2.0
MarkupSafe==3.0.3
mdit-py-plugins==0.6.1
mdurl==0.1.2
mpmath==1.3.0
nbformat==5.11.1
networkx==3.6.1
numpy==2.2.6
packaging==26.3
pandas==2.3.3
pillow==12.3.0
pip==26.0.1
platformdirs==4.11.3
protobuf==7.35.1
pyarrow==18.1.0
python-dateutil==2.9.0.post0
python-dotenv==1.2.3
python-slugify==8.0.4
pytz==2026.3.post1
PyYAML==6.0.3
referencing==0.37.0
reportlab==4.2.5
requests==2.34.2
rpds-py==2026.6.3
scikit-learn==1.7.2
scipy==1.15.3
setuptools==84.0.0
six==1.17.0
sympy==1.14.0
text-unidecode==1.3
threadpoolctl==3.6.0
torch==2.8.0
tqdm==4.70.0
traitlets==5.16.1
typing-extensions==4.16.0
tzdata==2026.3
urllib3==2.7.0
webencodings==0.6.1
Tool callread_file/task/evaluate_public.py Returned

Tool call · Input 

{
  "file_path": "/task/evaluate_public.py"
}

Tool response 

#!/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 <validation-ranking.parquet|csv>
"""
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 <validation-ranking.parquet|csv>"
        )
    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()
Tool callread_file/task/ranking_evaluation.py Returned

Tool call · Input 

{
  "file_path": "/task/ranking_evaluation.py"
}

Tool response 

"""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),
    }
05

Inspect system specifications and dataset dimensions

+00:13
Tool callrun_shell_commandInspect system specifications and dataset dimensions Returned

Tool call · Input 

{
  "description": "Inspect system specifications and dataset dimensions",
  "command": "python3 -c \"\nimport os, psutil, torch, glob\nimport pandas as pd\nimport numpy as np\n\nprint(f'CPU count: {os.cpu_count()}')\nprint(f'RAM (GB): {psutil.virtual_memory().total / (1024**3):.2f}')\nprint(f'PyTorch version: {torch.__version__}')\n\ndata_files = sorted(glob.glob('/task/data/*'))\nfor f in data_files:\n    size_mb = os.path.getsize(f) / (1024**2)\n    if f.endswith('.parquet'):\n        df = pd.read_parquet(f)\n        print(f'{os.path.basename(f):35s}: {size_mb:6.2f} MB, {len(df):8d} rows, cols: {list(df.columns)}')\n    elif f.endswith('.npz'):\n        npz = np.load(f)\n        keys = list(npz.keys())\n        print(f'{os.path.basename(f):35s}: {size_mb:6.2f} MB, npz keys: {keys}')\n        for k in keys:\n            print(f'    {k}: shape {npz[k].shape}, dtype {npz[k].dtype}')\n    else:\n        print(f'{os.path.basename(f):35s}: {size_mb:6.2f} MB')\n\""
}

Tool response 

<untrusted_context>
Output: Traceback (most recent call last):
  File "<string>", line 2, in <module>
ModuleNotFoundError: No module named 'psutil'
Exit Code: 1
Process Group PGID: 1618
</untrusted_context>
06

Inspect system specifications and data files

+00:17
Tool callrun_shell_commandInspect system specifications and data files Returned

Tool call · Input 

{
  "description": "Inspect system specifications and data files",
  "command": "python3 -c \"\nimport os, glob\nimport pandas as pd\nimport numpy as np\nimport torch\n\nprint(f'CPU count: {os.cpu_count()}')\nwith open('/proc/meminfo') as f:\n    for line in f:\n        if 'MemTotal' in line or 'MemAvailable' in line:\n            print(line.strip())\nprint(f'PyTorch version: {torch.__version__}')\n\ndata_files = sorted(glob.glob('/task/data/*'))\nfor f in data_files:\n    size_mb = os.path.getsize(f) / (1024**2)\n    if f.endswith('.parquet'):\n        df = pd.read_parquet(f)\n        print(f'{os.path.basename(f):35s}: {size_mb:6.2f} MB, {len(df):8d} rows, cols: {list(df.columns)}')\n    elif f.endswith('.npz'):\n        npz = np.load(f)\n        keys = list(npz.keys())\n        print(f'{os.path.basename(f):35s}: {size_mb:6.2f} MB, npz keys: {keys}')\n        for k in keys:\n            print(f'    {k}: shape {npz[k].shape}, dtype {npz[k].dtype}')\n    else:\n        print(f'{os.path.basename(f):35s}: {size_mb:6.2f} MB')\n\""
}

Tool response 

<untrusted_context>
Output: CPU count: 8
MemTotal:       15988964 kB
MemAvailable:   14370644 kB
PyTorch version: 2.8.0+cpu
candidate_catalog.parquet          :   0.33 MB,    30000 rows, cols: ['item_id', 'positive_listens', 'organic_positive_listens', 'popularity_rank']
target_users.parquet               :   0.08 MB,     8456 rows, cols: ['uid', 'recent_positive_listens', 'history_positive_listens']
test_eligible_candidates.npz       :   7.38 MB, npz keys: ['uids', 'item_ids', 'packed_eligible']
    uids: shape (8456,), dtype uint32
    item_ids: shape (30000,), dtype uint32
    packed_eligible: shape (8456, 3750), dtype uint8
test_preference_state.parquet      :   1.04 MB,   484776 rows, cols: ['uid', 'item_id', 'state']
train_dislikes.parquet             :   0.82 MB,   101075 rows, cols: ['uid', 'timestamp', 'item_id', 'is_organic']
train_likes.parquet                :   5.13 MB,   814831 rows, cols: ['uid', 'timestamp', 'item_id', 'is_organic']
train_listens.parquet              : 304.40 MB, 43484159 rows, cols: ['uid', 'timestamp', 'item_id', 'is_organic', 'played_ratio_pct', 'track_length_seconds']
train_undislikes.parquet           :   0.15 MB,    19960 rows, cols: ['uid', 'timestamp', 'item_id', 'is_organic']
train_unlikes.parquet              :   1.77 MB,   292665 rows, cols: ['uid', 'timestamp', 'item_id', 'is_organic']
val_dislikes.parquet               :   0.03 MB,     3563 rows, cols: ['uid', 'timestamp', 'item_id', 'is_organic']
val_likes.parquet                  :   0.28 MB,    37795 rows, cols: ['uid', 'timestamp', 'item_id', 'is_organic']
val_listens.parquet                :   9.49 MB,  1501697 rows, cols: ['uid', 'timestamp', 'item_id', 'is_organic', 'played_ratio_pct', 'track_length_seconds']
val_undislikes.parquet             :   0.01 MB,      606 rows, cols: ['uid', 'timestamp', 'item_id', 'is_organic']
val_unlikes.parquet                :   0.07 MB,     9346 rows, cols: ['uid', 'timestamp', 'item_id', 'is_organic']
validation_eligible_candidates.npz :   7.22 MB, npz keys: ['uids', 'item_ids', 'packed_eligible']
    uids: shape (8456,), dtype uint32
    item_ids: shape (30000,), dtype uint32
    packed_eligible: shape (8456, 3750), dtype uint8
validation_preference_state.parquet:   1.01 MB,   469124 rows, cols: ['uid', 'item_id', 'state']
Process Group PGID: 1621
</untrusted_context>
07

Check target users and catalog items coverage in train and val

+00:29
Recorded reasoning summary 48 words

Evaluating System Resources

My thoughts are currently focused on assessing the hardware and software environment, specifically the 8 CPUs and 16GB of RAM supporting PyTorch 2.8.0+cpu. I'm also considering the scale of the user base (8,456) and the candidate catalog (30,000 items), noting its ordered nature by popularity.

Tool callrun_shell_commandCheck target users and catalog items coverage in train and val Returned

Tool call · Input 

{
  "command": "python3 -c \"\nimport pandas as pd\nimport numpy as np\n\ntargets = pd.read_parquet('/task/data/target_users.parquet')\ntarget_uids = set(targets.uid)\nprint(f'Num target users: {len(target_uids)}')\n\ncat = pd.read_parquet('/task/data/candidate_catalog.parquet')\ncat_items = set(cat.item_id)\nprint(f'Num catalog items: {len(cat_items)}')\nprint(f'Catalog items min: {cat.item_id.min()}, max: {cat.item_id.max()}')\n\nval_listens = pd.read_parquet('/task/data/val_listens.parquet')\nprint(f'Val listens uids count: {val_listens.uid.nunique()}, in targets: {val_listens.uid.isin(target_uids).sum()} / {len(val_listens)}')\nprint(f'Val listens timestamp range: {val_listens.timestamp.min()} to {val_listens.timestamp.max()}')\n\nval_likes = pd.read_parquet('/task/data/val_likes.parquet')\nprint(f'Val likes uids count: {val_likes.uid.nunique()}, in targets: {val_likes.uid.isin(target_uids).sum()} / {len(val_likes)}')\n\n# Check train timestamps\ntrain_likes = pd.read_parquet('/task/data/train_likes.parquet')\nprint(f'Train likes timestamp range: {train_likes.timestamp.min()} to {train_likes.timestamp.max()}')\nprint(f'Train likes uids: {train_likes.uid.nunique()}, in targets: {train_likes.uid.isin(target_uids).sum()} / {len(train_likes)}')\n\n# Check target_users stats\nprint(targets.describe())\n\"",
  "description": "Check target users and catalog items coverage in train and val"
}

Tool response 

<untrusted_context>
Output: Num target users: 8456
Num catalog items: 30000
Catalog items min: 206, max: 9390294
Val listens uids count: 8196, in targets: 1482553 / 1501697
Val listens timestamp range: 24788600 to 25393395
Val likes uids count: 4114, in targets: 33177 / 37795
Train likes timestamp range: 80 to 24786790
Train likes uids: 8144, in targets: 690968 / 814831
                  uid  recent_positive_listens  history_positive_listens
count     8456.000000              8456.000000               8456.000000
mean    502289.108325              1251.076750               3236.440752
std     287953.395912              1414.340904               3697.575286
min        100.000000                20.000000                 20.000000
25%     254375.000000               281.000000                630.000000
50%     503850.000000               784.500000               1920.500000
75%     751725.000000              1706.250000               4494.250000
max    1000000.000000             19592.000000              26597.000000
Process Group PGID: 1641
</untrusted_context>
08

Check candidate eligibility matrix and properties

+00:38
Recorded reasoning summary 43 words

Exploring Baseline Performance

I'm currently investigating baseline performance by evaluating evaluate_public.py against trivial and popularity metrics. My focus is on understanding validation set label distribution and how a candidate popularity rank performs. I'm also developing a script to facilitate this baseline score computation.

Tool callrun_shell_commandCheck candidate eligibility matrix and properties Returned

Tool call · Input 

{
  "description": "Check candidate eligibility matrix and properties",
  "command": "python3 -c \"\nimport numpy as np\nimport pandas as pd\nfrom pathlib import Path\nfrom ranking_evaluation import CandidateSet\n\nnpz_path = Path('/task/data/validation_eligible_candidates.npz')\ncand_set = CandidateSet(npz_path)\nprint(f'uids len: {len(cand_set.uids)}, item_ids len: {len(cand_set.item_ids)}')\n\n# For user row 0:\nr = 0\nunpacked = np.unpackbits(cand_set.packed[r], count=len(cand_set.item_ids), bitorder='little')\nprint(f'User {cand_set.uids[r]}: num eligible items: {unpacked.sum()}')\n\n# Check average eligible items per user\n# packed is (8456, 3750)\ntotal_eligible = np.unpackbits(cand_set.packed, axis=1, count=len(cand_set.item_ids), bitorder='little')\nprint(f'Eligible shape: {total_eligible.shape}')\nprint(f'Eligible per user: min={total_eligible.sum(axis=1).min()}, mean={total_eligible.sum(axis=1).mean():.1f}, max={total_eligible.sum(axis=1).max()}')\n\""
}

Tool response 

<untrusted_context>
Output: Traceback (most recent call last):
  File "<string>", line 5, in <module>
ModuleNotFoundError: No module named 'ranking_evaluation'
Exit Code: 1
Process Group PGID: 1661
</untrusted_context>
09

Check candidate eligibility matrix properties with PYTHONPATH=/task

+00:43
Tool callrun_shell_commandCheck candidate eligibility matrix properties with PYTHONPATH=/task Returned

Tool call · Input 

{
  "description": "Check candidate eligibility matrix properties with PYTHONPATH=/task",
  "command": "PYTHONPATH=/task python3 -c \"\nimport sys\nimport numpy as np\nimport pandas as pd\nfrom pathlib import Path\nfrom ranking_evaluation import CandidateSet\n\nnpz_path = Path('/task/data/validation_eligible_candidates.npz')\ncand_set = CandidateSet(npz_path)\nprint(f'uids len: {len(cand_set.uids)}, item_ids len: {len(cand_set.item_ids)}')\n\n# For user row 0:\nr = 0\nunpacked = np.unpackbits(cand_set.packed[r], count=len(cand_set.item_ids), bitorder='little')\nprint(f'User {cand_set.uids[r]}: num eligible items: {unpacked.sum()}')\n\n# Check average eligible items per user\ntotal_eligible = np.unpackbits(cand_set.packed, axis=1, count=len(cand_set.item_ids), bitorder='little')\nprint(f'Eligible shape: {total_eligible.shape}')\nprint(f'Eligible per user: min={total_eligible.sum(axis=1).min()}, mean={total_eligible.sum(axis=1).mean():.1f}, max={total_eligible.sum(axis=1).max()}')\n\""
}

Tool response 

<untrusted_context>
Output: uids len: 8456, item_ids len: 30000
User 100: num eligible items: 29318
Eligible shape: (8456, 30000)
Eligible per user: min=24657, mean=29067.3, max=30000
Process Group PGID: 1678
</untrusted_context>
10

list_directory command

+00:50
Recorded reasoning summary 49 words

Filtering Candidates

I've confirmed that nearly all candidates, around 29,000, meet the user's eligibility criteria. My next step is to investigate the small number of exclusions, likely due to prior interactions or user feedback. I'll also cross-reference this with test_eligible_candidates.npz and then proceed to implement the logic in Python.

Tool calllist_directoryTool call Returned

Tool call · Input 

{
  "dir_path": "/app/solution"
}

Tool response 

Directory /app/solution is empty.
11

Check catalog ordering and alignment with item_ids in candidate sets

+00:54
Tool callrun_shell_commandCheck catalog ordering and alignment with item_ids in candidate sets Returned

Tool call · Input 

{
  "description": "Check catalog ordering and alignment with item_ids in candidate sets",
  "command": "PYTHONPATH=/task python3 -c \"\nimport numpy as np\nimport pandas as pd\nfrom pathlib import Path\nfrom ranking_evaluation import CandidateSet, validate_ranking\n\n# Check candidate_catalog\ncatalog = pd.read_parquet('/task/data/candidate_catalog.parquet')\nprint('Catalog head:')\nprint(catalog.head())\n\n# Validation candidate set\nval_cand = CandidateSet(Path('/task/data/validation_eligible_candidates.npz'))\nval_unpacked = np.unpackbits(val_cand.packed, axis=1, count=len(val_cand.item_ids), bitorder='little')\n\n# Test candidate set\ntest_cand = CandidateSet(Path('/task/data/test_eligible_candidates.npz'))\ntest_unpacked = np.unpackbits(test_cand.packed, axis=1, count=len(test_cand.item_ids), bitorder='little')\n\ntargets = set(pd.read_parquet('/task/data/target_users.parquet').uid)\n\n# Let's generate a simple popularity ranking for val\nrows = []\nfor i, uid in enumerate(val_cand.uids):\n    # catalog is sorted by popularity\n    # find top 100 eligible item indices\n    # val_cand.item_ids is aligned with candidate_catalog?\n    # Let's check alignment\n    pass\n\""
}

Tool response 

<untrusted_context>
Output: Catalog head:
   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: 1689
</untrusted_context>
12

Check item_ids alignment between catalog and candidate sets

+00:59
Tool callrun_shell_commandCheck item_ids alignment between catalog and candidate sets Returned

Tool call · Input 

{
  "command": "PYTHONPATH=/task python3 -c \"\nimport numpy as np\nimport pandas as pd\nfrom pathlib import Path\nfrom ranking_evaluation import CandidateSet\n\ncatalog = pd.read_parquet('/task/data/candidate_catalog.parquet')\nval_cand = CandidateSet(Path('/task/data/validation_eligible_candidates.npz'))\ntest_cand = CandidateSet(Path('/task/data/test_eligible_candidates.npz'))\n\nprint('val_cand.item_ids equal catalog.item_id:', np.array_equal(val_cand.item_ids, catalog.item_id.to_numpy()))\nprint('test_cand.item_ids equal catalog.item_id:', np.array_equal(test_cand.item_ids, catalog.item_id.to_numpy()))\nprint('test_cand.uids equal val_cand.uids:', np.array_equal(test_cand.uids, val_cand.uids))\n\"",
  "description": "Check item_ids alignment between catalog and candidate sets"
}

Tool response 

<untrusted_context>
Output: val_cand.item_ids equal catalog.item_id: True
test_cand.item_ids equal catalog.item_id: True
test_cand.uids equal val_cand.uids: True
Process Group PGID: 1706
</untrusted_context>
13

Generate popularity baseline for val and initial submission.parquet

+01:05
Recorded reasoning summary 26 words

Confirming Data Alignment

I've confirmed that val_cand.item_ids, test_cand.item_ids, and catalog.item_id are precisely aligned, as are the respective uids. This matches expectations given catalog's ordering by popularity.

Tool callrun_shell_commandGenerate popularity baseline for val and initial submission.parquet Returned

Tool call · Input 

{
  "command": "PYTHONPATH=/task python3 -c \"\nimport numpy as np\nimport pandas as pd\nfrom pathlib import Path\nfrom ranking_evaluation import CandidateSet\n\nval_cand = CandidateSet(Path('/task/data/validation_eligible_candidates.npz'))\nval_unpacked = np.unpackbits(val_cand.packed, axis=1, count=len(val_cand.item_ids), bitorder='little')\n\nuids_out = []\nitems_out = []\nranks_out = []\n\nfor r, uid in enumerate(val_cand.uids):\n    # indices of eligible items\n    elig_indices = np.where(val_unpacked[r])[0][:100]\n    uids_out.extend([uid] * 100)\n    items_out.extend(val_cand.item_ids[elig_indices])\n    ranks_out.extend(range(1, 101))\n\ndf_val = pd.DataFrame({'uid': uids_out, 'item_id': items_out, 'rank': ranks_out})\ndf_val.to_parquet('/app/solution/val_baseline.parquet')\n\n# Do the same for test to create the emergency submission\ntest_cand = CandidateSet(Path('/task/data/test_eligible_candidates.npz'))\ntest_unpacked = np.unpackbits(test_cand.packed, axis=1, count=len(test_cand.item_ids), bitorder='little')\n\nuids_test = []\nitems_test = []\nranks_test = []\nfor r, uid in enumerate(test_cand.uids):\n    elig_indices = np.where(test_unpacked[r])[0][:100]\n    uids_test.extend([uid] * 100)\n    items_test.extend(test_cand.item_ids[elig_indices])\n    ranks_test.extend(range(1, 101))\n\ndf_test = pd.DataFrame({'uid': uids_test, 'item_id': items_test, 'rank': ranks_test})\ndf_test.to_parquet('/app/solution/submission.parquet')\nprint('Baseline generated successfully!')\n\"",
  "description": "Generate popularity baseline for val and initial submission.parquet"
}

Tool response 

<untrusted_context>
Output: Baseline generated successfully!
Process Group PGID: 1723
</untrusted_context>
14

Evaluate the popularity baseline on validation set

+01:17
Tool callrun_shell_commandEvaluate the popularity baseline on validation set Returned

Tool call · Input 

{
  "command": "python3 /task/evaluate_public.py /app/solution/val_baseline.parquet",
  "description": "Evaluate the popularity baseline on validation set"
}

Tool response 

<untrusted_context>
Output: {
  "model": "/app/solution/val_baseline.parquet",
  "scores": {
    "quality_ndcg@10": 0.01071,
    "quality_users": 4263
  }
}
Process Group PGID: 1734
</untrusted_context>
15

Validate initial submission against test eligibility

+01:24
Recorded reasoning summary 34 words

Confirming Evaluation Metrics

I've successfully run the evaluation script. The popularity baseline has yielded an NDCG@10 score of 0.01071, with a user count of 4263. This confirms the initial performance against a simple baseline.

Tool callrun_shell_commandValidate initial submission against test eligibility Returned

Tool call · Input 

{
  "description": "Validate initial submission against test eligibility",
  "command": "PYTHONPATH=/task python3 -c \"\nimport pandas as pd\nfrom pathlib import Path\nfrom ranking_evaluation import CandidateSet, validate_ranking, RankingValidationError\n\ntargets_frame = pd.read_parquet('/task/data/target_users.parquet')\ntargets = set(targets_frame.uid.astype(int))\neligibility = CandidateSet(Path('/task/data/test_eligible_candidates.npz'))\n\nframe = validate_ranking(pd.read_parquet('/app/solution/submission.parquet'), targets)\nassert all(\n    eligibility.is_eligible(uid, item)\n    for uid, item in frame[['uid', 'item_id']].itertuples(index=False, name=None)\n)\nprint('submission.parquet is 100% valid!')\n\""
}

Tool response 

<untrusted_context>
Output: submission.parquet is 100% valid!
Process Group PGID: 1754
</untrusted_context>
16

Inspect preference state and candidate catalog overlap in val

+01:39
Recorded reasoning summary 33 words

Analyzing Prompt Specifics

My current focus is on thoroughly dissecting the prompt's intricacies. I'm meticulously examining the requirements, constraints, available data, and architectural considerations to ensure complete alignment with the initial submission's validity.

Recorded reasoning summary 56 words

Developing Tower Architectures

I'm now concentrating on building the neural network architectures for both the user and item towers. The key challenge is ensuring they produce compatible, independently precomputable vectors that can be efficiently combined via a dot product at request time, adhering to strict latency budgets and scalability needs for a large-scale music streaming service.

Recorded reasoning summary 37 words

Processing User Data

I'm now focused on integrating the provided data, particularly the per-user candidate eligibility and explicit preference state. Understanding the temporal aspects and event families like listens, likes, and dislikes is crucial for effective modeling.

Recorded reasoning summary 50 words

Evaluating Candidate Relevance

My focus has shifted to defining and evaluating candidate relevance based on organic user behavior in the subsequent window, specifically grading based on likes or significant listens. I'm working to optimize for NDCG@10, ensuring that validation events are strictly used for performance measurement and not feature engineering.

Recorded reasoning summary 62 words

Exploring Data Inclusions

I'm now delving into the specifics of the data, particularly the train_listens.parquet and preference_state files. My immediate task is to ascertain the volume of data per user, examine timestamps, and determine the precise scope of the item catalog, cross-referencing it with candidate lists and validation/training sets. I'm also writing a Python script to systematically address these data exploration questions.

Tool callrun_shell_commandInspect preference state and candidate catalog overlap in val Returned

Tool call · Input 

{
  "command": "PYTHONPATH=/task python3 -c \"\nimport pandas as pd\nimport numpy as np\n\ncat = pd.read_parquet('/task/data/candidate_catalog.parquet')\ncat_items = set(cat.item_id)\n\ntargets = pd.read_parquet('/task/data/target_users.parquet')\ntarget_uids = set(targets.uid)\n\npref_val = pd.read_parquet('/task/data/validation_preference_state.parquet')\nprint('Val preference state sample:')\nprint(pref_val.head())\nprint('Val preference state unique states:', pref_val['state'].value_counts())\nprint('Val pref items in catalog:', pref_val.item_id.isin(cat_items).mean())\nprint('Val pref uids in targets:', pref_val.uid.isin(target_uids).mean())\n\n# Check val_listens\nval_listens = pd.read_parquet('/task/data/val_listens.parquet')\nprint('Val listens items in catalog:', val_listens.item_id.isin(cat_items).mean())\nval_likes = pd.read_parquet('/task/data/val_likes.parquet')\nprint('Val likes items in catalog:', val_likes.item_id.isin(cat_items).mean())\n\n# Check how many validation positive events (grade 1 or 3) are in candidate catalog\nval_strong = val_listens[(val_listens.is_organic == 1) & (val_listens.played_ratio_pct >= 80) & (val_listens.uid.isin(target_uids))]\nprint('Target organic strong listens in val:', len(val_strong), 'in catalog:', val_strong.item_id.isin(cat_items).mean())\n\nval_org_likes = val_likes[(val_likes.is_organic == 1) & (val_likes.uid.isin(target_uids))]\nprint('Target organic likes in val:', len(val_org_likes), 'in catalog:', val_org_likes.item_id.isin(cat_items).mean())\n\"",
  "description": "Inspect preference state and candidate catalog overlap in val"
}

Tool response 

<untrusted_context>
Output: Val preference state sample:
   uid  item_id  state
0  100   410868  liked
1  100   732449  liked
2  100   982903  liked
3  100  1730273  liked
4  100  2621343  liked
Val preference state unique states: state
liked       418435
disliked     50689
Name: count, dtype: int64
Val pref items in catalog: 1.0
Val pref uids in targets: 1.0
Val listens items in catalog: 0.6737517621730615
Val likes items in catalog: 0.5570842704061384
Target organic strong listens in val: 421615 in catalog: 0.6850515280528444
Target organic likes in val: 20702 in catalog: 0.5026567481402763
Process Group PGID: 1772
</untrusted_context>
17

Check if preference state items are eligible in candidate sets

+01:47
Recorded reasoning summary 49 words

Analyzing Preference State

My analysis of val_preference_state reveals a significant volume of user preferences. I'm noting that all 100% of these items are accounted for within the candidate catalog and are linked to target users, which is a critical confirmation. Now I’m pivoting to examine the contents of validation_eligible_candidates.npz.

Tool callrun_shell_commandCheck if preference state items are eligible in candidate sets Returned

Tool call · Input 

{
  "description": "Check if preference state items are eligible in candidate sets",
  "command": "PYTHONPATH=/task python3 -c \"\nimport pandas as pd\nimport numpy as np\nfrom pathlib import Path\nfrom ranking_evaluation import CandidateSet\n\nval_cand = CandidateSet(Path('/task/data/validation_eligible_candidates.npz'))\npref_val = pd.read_parquet('/task/data/validation_preference_state.parquet')\n\n# Check if disliked items are eligible\ndisliked = pref_val[pref_val.state == 'disliked']\ndisliked_elig = [val_cand.is_eligible(uid, item) for uid, item in disliked[['uid', 'item_id']].head(1000).itertuples(index=False, name=None)]\nprint('Fraction of disliked items eligible:', np.mean(disliked_elig))\n\n# Check if liked items are eligible\nliked = pref_val[pref_val.state == 'liked']\nliked_elig = [val_cand.is_eligible(uid, item) for uid, item in liked[['uid', 'item_id']].head(1000).itertuples(index=False, name=None)]\nprint('Fraction of liked items eligible:', np.mean(liked_elig))\n\""
}

Tool response 

<untrusted_context>
Output: Fraction of disliked items eligible: 0.0
Fraction of liked items eligible: 0.0
Process Group PGID: 1792
</untrusted_context>
18

Examine ineligible items for a user

+01:58
Recorded reasoning summary 49 words

Refining Recommendations

I'm currently re-evaluating how liked and disliked items influence recommendations. It seems my current approach is overly strict, completely excluding items I've already engaged with from new track suggestions on the "Made for You" shelf. I need to adjust this to allow for more relevant new discoveries.

Tool callrun_shell_commandExamine ineligible items for a user Returned

Tool call · Input 

{
  "command": "PYTHONPATH=/task python3 -c \"\nimport pandas as pd\nimport numpy as np\nfrom pathlib import Path\nfrom ranking_evaluation import CandidateSet\n\nval_cand = CandidateSet(Path('/task/data/validation_eligible_candidates.npz'))\npref_val = pd.read_parquet('/task/data/validation_preference_state.parquet')\n\n# For user 100:\nuid = 100\nrow = val_cand.uid_row[uid]\nunpacked = np.unpackbits(val_cand.packed[row], count=len(val_cand.item_ids), bitorder='little')\nineligible_items = val_cand.item_ids[~unpacked.astype(bool)]\nprint(f'User {uid} has {len(ineligible_items)} ineligible items.')\n\npref_user_items = pref_val[pref_val.uid == uid].item_id.to_numpy()\nprint(f'User {uid} has {len(pref_user_items)} items in pref state.')\nprint('Are all pref state items ineligible?', set(pref_user_items).issubset(set(ineligible_items)))\nprint(f'Remaining ineligible items: {len(ineligible_items) - len(pref_user_items)}')\n\"",
  "description": "Examine ineligible items for a user"
}

Tool response 

<untrusted_context>
Output: User 100 has 682 ineligible items.
User 100 has 15 items in pref state.
Are all pref state items ineligible? True
Remaining ineligible items: 667
Process Group PGID: 1815
</untrusted_context>
19

Benchmark MIPS scoring and topk latency on CPU

+02:13
Recorded reasoning summary 34 words

Clarifying Eligibility Masks

I've confirmed that the candidate eligibility masks are directly available within the validation_eligible_candidates.npz and test_eligible_candidates.npz files. This means I don't need to derive them, as they are provided by the platform.

Recorded reasoning summary 39 words

Refining Model Structure

I'm now focusing on the model's architecture. The production environment requires a user and item vector decomposition for latency. I'll implement separate PyTorch towers, ensuring the user tower uses interaction history, not just an ID lookup.

Recorded reasoning summary 47 words

Designing Item & User Towers

I'm currently focused on designing the item and user towers for the model. The item tower will precompute item vectors using embeddings or features, while the user tower will derive its representation strictly from interaction history, avoiding any direct user ID lookups.

Recorded reasoning summary 65 words

Developing User Vector Computation

My current focus is on determining the precise methodology for computing the user vector. I'm exploring various pooling strategies, such as weighted averages or attention mechanisms, to derive the vector from the embeddings of interacted items. The goal is to condition this vector on explicit preferences and interaction history, ultimately producing the final $u \in \mathbb{R}^d$ for dot product relevance scoring.

Recorded reasoning summary 41 words

Implementing Scalable Training

I've determined that global user-item matrix factorization is not a viable training approach due to scalability constraints. I will proceed with a two-tower neural network architecture, employing a sampled softmax or in-batch negative cross-entropy loss for efficient training.

Recorded reasoning summary 44 words

Evaluating Inference Speed

My current focus is on the inference phase. I've confirmed that the dot product of user and item embeddings, followed by masking ineligible items and selecting top candidates, can be achieved within acceptable latency constraints using CPU-based PyTorch or NumPy operations.

Tool callrun_shell_commandBenchmark MIPS scoring and topk latency on CPU Returned

Tool call · Input 

{
  "description": "Benchmark MIPS scoring and topk latency on CPU",
  "command": "python3 -c \"\nimport torch\nimport time\n\nU = torch.randn(8456, 128)\nV = torch.randn(30000, 128)\n\nt0 = time.time()\nscores = torch.matmul(U, V.T)\nt1 = time.time()\ntopk = torch.topk(scores, 100, dim=1)\nt2 = time.time()\n\nprint(f'Matmul time: {(t1 - t0)*1000:.2f} ms')\nprint(f'Topk time: {(t2 - t1)*1000:.2f} ms')\n\""
}

Tool response 

<untrusted_context>
Output: Matmul time: 333.86 ms
Topk time: 164.92 ms
Process Group PGID: 1833
</untrusted_context>
20

Check timestamps and time windows

+02:22
Recorded reasoning summary 45 words

Analyzing Performance Metrics

The initial performance of ~500ms across 8,456 users is excellent, well within production budgets. My focus now shifts to a deeper data exploration. I'm particularly interested in understanding the timestamp structure within train_listens to inform architecture, feature representation, and the training pipeline.

Tool callrun_shell_commandCheck timestamps and time windows Returned

Tool call · Input 

{
  "command": "python3 -c \"\nimport pandas as pd\nimport numpy as np\n\n# Check timestamps\nt_end_train = 24786800\ngap = 30 * 60 # 1800 s\nt_start_val = t_end_train + gap\nseven_days = 7 * 86400 # 604800 s\nt_end_val = t_start_val + seven_days\n\nprint(f'Train end: {t_end_train}')\nprint(f'Val start: {t_start_val}, Val end: {t_end_val}')\n\n# Check actual min/max in val\nval_listens = pd.read_parquet('/task/data/val_listens.parquet')\nprint(f'Actual val listens min: {val_listens.timestamp.min()}, max: {val_listens.timestamp.max()}')\n\n# Check recent train data: e.g. last 30 days, last 90 days of train\nt_30d = t_end_train - 30 * 86400\nt_90d = t_end_train - 90 * 86400\nprint(f'Train 30d start: {t_30d}, 90d start: {t_90d}')\n\"",
  "description": "Check timestamps and time windows"
}

Tool response 

<untrusted_context>
Output: Train end: 24786800
Val start: 24788600, Val end: 25393400
Actual val listens min: 24788600, max: 25393395
Train 30d start: 22194800, 90d start: 17010800
Process Group PGID: 1846
</untrusted_context>
21

Inspect temporal distribution of train listens

+02:36
Recorded reasoning summary 45 words

Analyzing Recent Listen Data

I'm currently investigating the volume of data within train_listens across various recent timeframes: 90 days, 30 days, 14 days, and 7 days. This involves quantifying the number of rows relevant to each of these periods from the total of 43,484,159 entries.

Tool callrun_shell_commandInspect temporal distribution of train listens Returned

Tool call · Input 

{
  "command": "python3 -c \"\nimport pyarrow.parquet as pq\nimport pyarrow.compute as pc\n\nt_end_train = 24786800\nt_90d = t_end_train - 90 * 86400\nt_30d = t_end_train - 30 * 86400\nt_7d = t_end_train - 7 * 86400\n\ntable = pq.read_table('/task/data/train_listens.parquet', columns=['timestamp'])\nts = table['timestamp'].to_numpy()\n\nprint(f'Total train listens: {len(ts)}')\nprint(f'Listens in last 90d: {(ts >= t_90d).sum():,} ({100*(ts >= t_90d).mean():.1f}%)')\nprint(f'Listens in last 30d: {(ts >= t_30d).sum():,} ({100*(ts >= t_30d).mean():.1f}%)')\nprint(f'Listens in last 7d:  {(ts >= t_7d).sum():,} ({100*(ts >= t_7d).mean():.1f}%)')\n\"",
  "description": "Inspect temporal distribution of train listens"
}

Tool response 

<untrusted_context>
Output: Total train listens: 43484159
Listens in last 90d: 16,769,274 (38.6%)
Listens in last 30d: 6,126,103 (14.1%)
Listens in last 7d:  1,491,699 (3.4%)
Process Group PGID: 1866
</untrusted_context>
22

Check target users in recent train listens

+02:47
Recorded reasoning summary 31 words

Observing listen trends

My analysis reveals a striking similarity between the 7-day pre-cutoff listen count (1.49M) and the val_listens figure (1.50M). The 30-day pre-cutoff total stands at 6.12M. Next, I'll investigate...

Tool callrun_shell_commandCheck target users in recent train listens Returned

Tool call · Input 

{
  "description": "Check target users in recent train listens",
  "command": "python3 -c \"\nimport pyarrow.parquet as pq\nimport pandas as pd\nimport numpy as np\n\ntargets = set(pd.read_parquet('/task/data/target_users.parquet').uid)\nt_end_train = 24786800\nt_30d = t_end_train - 30 * 86400\n\ntable = pq.read_table('/task/data/train_listens.parquet', columns=['uid', 'timestamp'])\nuids = table['uid'].to_numpy()\nts = table['timestamp'].to_numpy()\n\nmask_30d = ts >= t_30d\nuids_30d = uids[mask_30d]\n\nprint(f'Total unique users in train_listens: {len(np.unique(uids))}')\nprint(f'Unique users in last 30d: {len(np.unique(uids_30d))}')\nprint(f'Target users count: {len(targets)}')\nprint(f'Target users present in last 30d: {len(targets.intersection(set(uids_30d)))}')\nprint(f'Fraction of last 30d listens from target users: {np.isin(uids_30d, list(targets)).mean():.4f}')\n\""
}

Tool response 

<untrusted_context>
Output: Total unique users in train_listens: 9238
Unique users in last 30d: 8602
Target users count: 8456
Target users present in last 30d: 8237
Fraction of last 30d listens from target users: 0.9979
Process Group PGID: 1888
</untrusted_context>
23

Check unique items in train listens vs catalog

+02:53
Recorded reasoning summary 37 words

Calculating Unique Item Counts

I'm currently investigating the number of unique items within the train_listens dataset, cross-referencing this with the total number of unique items present in the catalog. This step is crucial for understanding item coverage.

Tool callrun_shell_commandCheck unique items in train listens vs catalog Returned

Tool call · Input 

{
  "command": "python3 -c \"\nimport pyarrow.parquet as pq\nimport pandas as pd\nimport numpy as np\n\ncat = pd.read_parquet('/task/data/candidate_catalog.parquet')\ncat_items = set(cat.item_id)\n\ntable = pq.read_table('/task/data/train_listens.parquet', columns=['item_id', 'timestamp'])\nitems = table['item_id'].to_numpy()\nts = table['timestamp'].to_numpy()\n\nt_end_train = 24786800\nt_30d = t_end_train - 30 * 86400\n\nmask_30d = ts >= t_30d\nitems_30d = items[mask_30d]\n\nprint(f'Total unique items in train_listens: {len(np.unique(items))}')\nprint(f'Unique items in last 30d: {len(np.unique(items_30d))}')\nprint(f'Catalog items: {len(cat_items)}')\nprint(f'Catalog items present in last 30d: {len(cat_items.intersection(set(items_30d)))}')\nprint(f'Catalog items present in all train: {len(cat_items.intersection(set(items)))}')\n\"",
  "description": "Check unique items in train listens vs catalog"
}

Tool response 

<untrusted_context>
Output: Total unique items in train_listens: 845395
Unique items in last 30d: 354855
Catalog items: 30000
Catalog items present in last 30d: 29710
Catalog items present in all train: 30000
Process Group PGID: 1910
</untrusted_context>
24

Analyze ground truth validation distribution

+03:11
Recorded reasoning summary 57 words

Confirming Catalog and Candidates

My current focus is on verifying the scope of our data. All 30,000 catalog items have successfully been incorporated into the training set. Simultaneously, I'm confirming that the candidate catalog also contains 30,000 items, and I'm now investigating which of these candidates are eligible for ranking within the validation set, cross-referencing with validation_eligible_candidates.npz.

Recorded reasoning summary 47 words

Verifying Candidate Sets

I've confirmed that the 30,000 catalog items are fully integrated. My attention is now on the candidate sets for testing. I'm verifying that the test_eligible_candidates.npz contains precisely the same 30,000 catalog items, ensuring all items to be ranked are indeed within the primary catalog.

Recorded reasoning summary 52 words

Calculating Graded NDCG

I'm now calculating the graded NDCG@10, using a schema where a user organically liking a track and listening to at least 80% earns a grade of 3. Otherwise, it's grade 0. I'm referencing validation_truth in evaluate_public.py and focusing on val_listens.parquet and val_likes.parquet to determine these grades for relevant users.

Tool callrun_shell_commandAnalyze ground truth validation distribution Returned

Tool call · Input 

{
  "description": "Analyze ground truth validation distribution",
  "command": "PYTHONPATH=/task python3 -c \"\nimport pandas as pd\nfrom pathlib import Path\nfrom evaluate_public import validation_truth\nfrom ranking_evaluation import CandidateSet\n\ntargets = set(pd.read_parquet('/task/data/target_users.parquet').uid)\nelig = CandidateSet(Path('/task/data/validation_eligible_candidates.npz'))\ntruth = validation_truth(elig, targets)\n\ntotal_pairs = sum(len(items) for items in truth.values())\ngrade1 = sum(sum(1 for g in items.values() if g == 1) for items in truth.values())\ngrade3 = sum(sum(1 for g in items.values() if g == 3) for items in truth.values())\nprint(f'Number of users with at least one eligible positive: {len(truth)}')\nprint(f'Total positive pairs: {total_pairs}')\nprint(f'Grade 1 pairs: {grade1}, Grade 3 pairs: {grade3}')\nitems_per_user = [len(items) for items in truth.values()]\nimport numpy as np\nprint(f'Relevant items per user: mean={np.mean(items_per_user):.2f}, median={np.median(items_per_user)}, min={np.min(items_per_user)}, max={np.max(items_per_user)}')\n\""
}

Tool response 

<untrusted_context>
Output: Number of users with at least one eligible positive: 4263
Total positive pairs: 37769
Grade 1 pairs: 33352, Grade 3 pairs: 4417
Relevant items per user: mean=8.86, median=4.0, min=1, max=442
Process Group PGID: 1938
</untrusted_context>
25

Check target user activity distributions in train

+03:21
Recorded reasoning summary 64 words

Analyzing User Engagement Metrics

My current focus is on dissecting user interaction within a 7-day window. I've identified 4,263 users who have engaged positively, contributing to a total of 37,769 positive pairs, with a significant portion (33,352) being grade 1 and 4,417 being grade 3. I'm also evaluating the discovery objective's potential gains for both grade 3 (a gain of 7) and grade 1.

Tool callrun_shell_commandCheck target user activity distributions in train Returned

Tool call · Input 

{
  "description": "Check target user activity distributions in train",
  "command": "python3 -c \"\nimport pyarrow.parquet as pq\nimport pandas as pd\nimport numpy as np\n\ntargets = set(pd.read_parquet('/task/data/target_users.parquet').uid)\nt_end_train = 24786800\nt_30d = t_end_train - 30 * 86400\n\ntable = pq.read_table('/task/data/train_listens.parquet', columns=['uid', 'timestamp', 'item_id', 'is_organic', 'played_ratio_pct'])\ndf = table.to_pandas()\ndf_target = df[df.uid.isin(targets)]\n\nprint(f'Total target listens: {len(df_target)}')\nprint(f'Organic fraction: {df_target.is_organic.mean():.4f}')\nprint(f'Played ratio pct >= 80%: {(df_target.played_ratio_pct >= 80).mean():.4f}')\nprint(f'Organic AND >= 80%: {((df_target.is_organic == 1) & (df_target.played_ratio_pct >= 80)).mean():.4f}')\n\n# In last 30d\ndf_30d = df_target[df_target.timestamp >= t_30d]\nprint(f'Last 30d target listens: {len(df_30d)}')\nprint(f'Last 30d organic AND >= 80%: {((df_30d.is_organic == 1) & (df_30d.played_ratio_pct >= 80)).sum()}')\n\n# Train likes\ntrain_likes = pd.read_parquet('/task/data/train_likes.parquet')\ntrain_likes_target = train_likes[train_likes.uid.isin(targets)]\nprint(f'Total target likes: {len(train_likes_target)}')\nprint(f'Organic fraction in likes: {train_likes_target.is_organic.mean():.4f}')\nprint(f'Last 30d target organic likes: {len(train_likes_target[(train_likes_target.timestamp >= t_30d) & (train_likes_target.is_organic == 1)])}')\n\""
}

Tool response 

<untrusted_context>
Output: Total target listens: 43222297
Organic fraction: 0.5171
Played ratio pct >= 80%: 0.5930
Organic AND >= 80%: 0.2805
Last 30d target listens: 6113327
Last 30d organic AND >= 80%: 1744924
Total target likes: 690968
Organic fraction in likes: 0.4959
Last 30d target organic likes: 49301
Process Group PGID: 1958
</untrusted_context>
26

Check last 7 days of training data statistics

+03:36
Recorded reasoning summary 52 words

Analyzing Recent User Engagement

I'm currently reviewing user activity in the most recent 30-day training window. My focus is on high engagement metrics: over 1.7 million organic listens with an 80% play ratio and nearly 50,000 organic likes. I'm now cross-referencing this with data from the seven days immediately preceding the cutoff.

Tool callrun_shell_commandCheck last 7 days of training data statistics Returned

Tool call · Input 

{
  "command": "python3 -c \"\nimport pyarrow.parquet as pq\nimport pandas as pd\n\ntargets = set(pd.read_parquet('/task/data/target_users.parquet').uid)\nt_end_train = 24786800\nt_7d = t_end_train - 7 * 86400\n\ntable = pq.read_table('/task/data/train_listens.parquet', columns=['uid', 'timestamp', 'item_id', 'is_organic', 'played_ratio_pct'])\ndf = table.to_pandas()\ndf_7d = df[(df.uid.isin(targets)) & (df.timestamp >= t_7d)]\nprint('Last 7d listens:', len(df_7d))\nprint('Last 7d organic >= 80%:', len(df_7d[(df_7d.is_organic == 1) & (df_7d.played_ratio_pct >= 80)]))\n\ntrain_likes = pd.read_parquet('/task/data/train_likes.parquet')\nlikes_7d = train_likes[(train_likes.uid.isin(targets)) & (train_likes.timestamp >= t_7d) & (train_likes.is_organic == 1)]\nprint('Last 7d organic likes:', len(likes_7d))\n\"",
  "description": "Check last 7 days of training data statistics"
}

Tool response 

<untrusted_context>
Output: Last 7d listens: 1486465
Last 7d organic >= 80%: 428209
Last 7d organic likes: 12022
Process Group PGID: 1980
</untrusted_context>
27

Check repeat consumption vs new discovery in validation truth

+04:00
Recorded reasoning summary 43 words

Analyzing Listen & Like Trends

I'm observing a slight increase in strong listens over the last week, though likes have significantly dipped compared to the validation period. I'm digging into what might be driving this disparity to understand the underlying user engagement dynamics.

Recorded reasoning summary 43 words

Exploring Two-Tower Architecture

I'm now focusing on the specific requirements of a two-tower neural network for music recommendations. My current thought process involves dissecting the prompt's details about the system's scale, latency constraints, and the necessity for independently precomputable user and item embeddings.

Recorded reasoning summary 56 words

Refining User Tower Logic

I'm now considering how to represent user interactions within the tower. Instead of a simple user ID, I'm exploring using a sequential representation derived from their interaction history. This involves handling explicit preference states and incorporating temporal backtesting. Candidate generation is platform-managed, allowing me to focus on the modeling within those boundaries.

Recorded reasoning summary 40 words

Developing User Embeddings

I'm now focused on deriving user representations directly from their interaction history, specifically avoiding user ID lookups. My thought process involves exploring how to aggregate item embeddings based on user sequence data, incorporating interaction types and weights.

Recorded reasoning summary 53 words

Synthesizing Embedding Strategies

I'm now synthesizing the previous thoughts on user embedding generation. My focus is on the aggregation of item embeddings using various pooling mechanisms like attention or weighted sums, followed by MLP layers to derive the final user vector. This approach aims to efficiently capture user preferences from their interaction sequences.

Recorded reasoning summary 53 words

Training Two-Tower Models

I'm now analyzing how to effectively train the two-tower network using the provided training windows. My focus is on understanding how to leverage train_* data for initial model tuning and then refit on the combined train_* + val_* data, ensuring val_* events are treated as history, not features, during validation.

Recorded reasoning summary 44 words

Validating Temporal Backtesting

I'm now considering how to effectively validate the model's predictive capability. My thoughts are coalescing around using temporal backtesting within the train_* data, specifically by creating cutoff points and evaluating positive interactions in the subsequent 7-day window, mirroring the prediction objective.

Tool callrun_shell_commandCheck repeat consumption vs new discovery in validation truth Returned

Tool call · Input 

{
  "description": "Check repeat consumption vs new discovery in validation truth",
  "command": "PYTHONPATH=/task python3 -c \"\nimport pandas as pd\nimport numpy as np\nfrom pathlib import Path\nfrom evaluate_public import validation_truth\nfrom ranking_evaluation import CandidateSet\n\ntargets = set(pd.read_parquet('/task/data/target_users.parquet').uid)\nelig = CandidateSet(Path('/task/data/validation_eligible_candidates.npz'))\ntruth = validation_truth(elig, targets)\n\n# Let's check how many (uid, item) in truth were in train_listens for that user!\nimport pyarrow.parquet as pq\n\ntable = pq.read_table('/task/data/train_listens.parquet', columns=['uid', 'item_id'])\ndf_train = table.to_pandas()\ndf_train_target = df_train[df_train.uid.isin(targets)]\n\n# Build set of past (uid, item) pairs\ntrain_user_items = set(zip(df_train_target.uid, df_train_target.item_id))\n\ntruth_pairs = [(uid, item) for uid, items in truth.items() for item in items]\nin_train = [pair in train_user_items for pair in truth_pairs]\n\nprint(f'Total truth pairs: {len(truth_pairs)}')\nprint(f'Truth pairs that were already listened to by the user in train: {sum(in_train)} ({100*np.mean(in_train):.2f}%)')\n\""
}

Tool response 

<untrusted_context>
Output: Total truth pairs: 37769
Truth pairs that were already listened to by the user in train: 0 (0.00%)
Process Group PGID: 2002
</untrusted_context>
28

Check if any past listened items are eligible in validation

+04:38
Recorded reasoning summary 41 words

Investigating Data Discrepancy

I'm noticing a complete absence of previously listened-to truth pairs in the training data, which is unexpected. My current focus is on meticulously examining the validation_eligible_candidates.npz file to confirm if all user-interacted items are being correctly filtered out.

Tool callrun_shell_commandCheck if any past listened items are eligible in validation Returned

Tool call · Input 

{
  "description": "Check if any past listened items are eligible in validation",
  "command": "PYTHONPATH=/task python3 -c \"\nimport pandas as pd\nimport numpy as np\nfrom pathlib import Path\nfrom ranking_evaluation import CandidateSet\nimport pyarrow.parquet as pq\n\nelig = CandidateSet(Path('/task/data/validation_eligible_candidates.npz'))\ntargets = set(pd.read_parquet('/task/data/target_users.parquet').uid)\n\ntable = pq.read_table('/task/data/train_listens.parquet', columns=['uid', 'item_id'])\ndf_train = table.to_pandas()\ndf_train_target = df_train[df_train.uid.isin(targets)].drop_duplicates()\n\n# Check sample of train listens: is any of them eligible?\nsample = df_train_target.sample(10000, random_state=42)\nis_elig = [elig.is_eligible(uid, item) for uid, item in sample.itertuples(index=False, name=None)]\nprint('Fraction of past listened items that are eligible:', np.mean(is_elig))\n\""
}

Tool response 

<untrusted_context>
Output: Fraction of past listened items that are eligible: 0.0
Process Group PGID: 2032
</untrusted_context>
29

Verify val items are ineligible in test candidates

+04:53
Recorded reasoning summary 39 words

Discovering New Music

My current focus is on understanding the strict discovery nature of the "Made for You" shelf. It's a crucial realization that any previously consumed content is entirely excluded, ensuring a pure discovery experience for the user.

Tool callrun_shell_commandVerify val items are ineligible in test candidates Returned

Tool call · Input 

{
  "description": "Verify val items are ineligible in test candidates",
  "command": "PYTHONPATH=/task python3 -c \"\nimport pandas as pd\nimport numpy as np\nfrom pathlib import Path\nfrom ranking_evaluation import CandidateSet\nimport pyarrow.parquet as pq\n\ntest_cand = CandidateSet(Path('/task/data/test_eligible_candidates.npz'))\ntargets = set(pd.read_parquet('/task/data/target_users.parquet').uid)\n\nval_listens = pd.read_parquet('/task/data/val_listens.parquet')\nval_target = val_listens[val_listens.uid.isin(targets)].drop_duplicates(['uid', 'item_id'])\nsample = val_target.sample(min(10000, len(val_target)), random_state=42)\n\nis_elig = [test_cand.is_eligible(uid, item) for uid, item in sample.itertuples(index=False, name=None)]\nprint('Fraction of val listened items eligible in test:', np.mean(is_elig))\n\""
}

Tool response 

<untrusted_context>
Output: Traceback (most recent call last):
  File "<string>", line 15, in <module>
ValueError: too many values to unpack (expected 2)
Exit Code: 1
Process Group PGID: 2055
</untrusted_context>
30

Verify val items are ineligible in test candidates with fixed tuple

+05:02
Tool callrun_shell_commandVerify val items are ineligible in test candidates with fixed tuple Returned

Tool call · Input 

{
  "description": "Verify val items are ineligible in test candidates with fixed tuple",
  "command": "PYTHONPATH=/task python3 -c \"\nimport pandas as pd\nimport numpy as np\nfrom pathlib import Path\nfrom ranking_evaluation import CandidateSet\n\ntest_cand = CandidateSet(Path('/task/data/test_eligible_candidates.npz'))\ntargets = set(pd.read_parquet('/task/data/target_users.parquet').uid)\n\nval_listens = pd.read_parquet('/task/data/val_listens.parquet')\nval_target = val_listens[val_listens.uid.isin(targets)][['uid', 'item_id']].drop_duplicates()\nsample = val_target.sample(min(10000, len(val_target)), random_state=42)\n\nis_elig = [test_cand.is_eligible(uid, item) for uid, item in sample.itertuples(index=False, name=None)]\nprint('Fraction of val listened items eligible in test:', np.mean(is_elig))\n\""
}

Tool response 

<untrusted_context>
Output: Fraction of val listened items eligible in test: 0.0
Process Group PGID: 2075
</untrusted_context>
31

Inspect user history overlap with candidate catalog in last 30 days

+05:17
Recorded reasoning summary 43 words

Confirming Validation Rules

I've confirmed that all items a user listened to in the training set are excluded from validation. Similarly, any items from both training and validation sets are ineligible for testing. This clarifies the item exclusion logic for the upcoming tasks.

Recorded reasoning summary 35 words

Exploring Candidate Catalog

I've started by examining the candidate_catalog.parquet file to understand its contents. I've identified that it contains item_id, positive_listens, organic_positive_listens, and popularity_rank, which seems like a good starting point for identifying eligible items.

Recorded reasoning summary 40 words

Synthesizing Data Sources

I'm now integrating information from candidate_catalog.parquet with interaction data. My focus is on understanding how user and item vectors can be independently derived, keeping in mind the production constraints of scalability and low latency for nearest-neighbor lookups.

Recorded reasoning summary 48 words

Designing Two-Tower Networks

I'm designing a two-tower neural network architecture. User representations will stem from interaction history, not ID lookups. All vectors will be PyTorch-generated, with relevance determined by their dot product, and I'm considering how to handle user listening history that includes items outside the candidate catalog.

Tool callrun_shell_commandInspect user history overlap with candidate catalog in last 30 days Returned

Tool call · Input 

{
  "description": "Inspect user history overlap with candidate catalog in last 30 days",
  "command": "PYTHONPATH=/task python3 -c \"\nimport pyarrow.parquet as pq\nimport pandas as pd\nimport numpy as np\n\ncat = pd.read_parquet('/task/data/candidate_catalog.parquet')\ncat_items = set(cat.item_id)\n\ntargets = set(pd.read_parquet('/task/data/target_users.parquet').uid)\npref_val = pd.read_parquet('/task/data/validation_preference_state.parquet')\nprint('Preference state items in catalog:', pref_val.item_id.isin(cat_items).mean())\n\n# Check recent train listens of target users\nt_end_train = 24786800\nt_30d = t_end_train - 30 * 86400\n\ntable = pq.read_table('/task/data/train_listens.parquet', columns=['uid', 'timestamp', 'item_id', 'is_organic', 'played_ratio_pct'])\ndf = table.to_pandas()\ndf_target_30d = df[(df.uid.isin(targets)) & (df.timestamp >= t_30d)]\n\nprint(f'Target 30d listens total: {len(df_target_30d)}')\nprint(f'Fraction in catalog: {df_target_30d.item_id.isin(cat_items).mean():.4f}')\n\n# How many catalog listens per target user in last 30d?\ndf_cat = df_target_30d[df_target_30d.item_id.isin(cat_items)]\nlistens_per_user = df_cat.groupby('uid').size()\nprint(f'Catalog listens per user in 30d: mean={listens_per_user.mean():.1f}, min={listens_per_user.min()}, median={listens_per_user.median()}')\n\""
}

Tool response 

<untrusted_context>
Output: Preference state items in catalog: 1.0
Target 30d listens total: 6113327
Fraction in catalog: 0.7098
Catalog listens per user in 30d: mean=529.5, min=1, median=338.0
Process Group PGID: 2101
</untrusted_context>
32

Benchmark full candidate softmax forward and backward on CPU

+05:43
Recorded reasoning summary 46 words

Confirming Catalog Completeness

It appears my preference state items are fully represented within the candidate catalog. I've also confirmed that over 71% of recent listens are now included, and each target user demonstrates robust engagement with the catalog, showing substantial listen counts within the last month.

Recorded reasoning summary 36 words

Designing Item Tower

I'm exploring the Item Tower's architecture, considering how item embeddings and catalog statistics like listen counts and popularity ranks will feed into an MLP. The goal is to precompute item embeddings for efficiency.

Recorded reasoning summary 46 words

Developing User Tower

My focus has shifted to the User Tower. I'm considering how to represent user interactions, analyzing signals like recent listens, completion rates, organic playback, and explicit likes/dislikes. I aim to develop an efficient neural architecture to combine these into a robust user embedding.

Recorded reasoning summary 54 words

Refining User History Representation

I'm now delving into the specifics of the User Tower, particularly how to best pool item embeddings from a user's history. I'm considering weighted pooling strategies that incorporate played ratios, recency decay, and interaction types, exploring whether multi-head self-attention or GRU/Transformer models might offer superior representations for user engagement signals.

Recorded reasoning summary 62 words

Synthesizing User Representation

I'm now integrating distinct user signal channels – organic listens, general listens, likes, and dislikes – into a unified representation. I'm exploring concatenating pooled embeddings or employing self-attention/MLP for fusion, followed by a refined MLP structure. The possibility of using sequential models like Transformers or GRUs for user history is also under active consideration to capture temporal dynamics effectively.

Recorded reasoning summary 128 words

Evaluating Training Speed

My training on 8 CPU cores is remarkably swift. I'm exploring the potential for equally fast vectorized training with GRU/Transformer over sequences of 50-100 items, intending to test both approaches to ascertain their relative performance.

Defining Training Task

I'm currently analyzing the most critical design decision: the training task and loss function. My objective is to maximize graded NDCG@10, with relevance grades assigned to organic likes (3), organic listens over 80% (1), and unobserved items (0).

Analyzing Training Objectives

I'm investigating standard two-tower retrieval training objectives. Specifically, I'm examining the in-batch sampled softmax (Cross-Entropy with logQ popularity correction) and its application with a batch of user and positive item representations, considering the resulting logits matrix $S = U V^T / \tau \in \mathbb{R}^{B \times B}$.

Recorded reasoning summary 36 words

Refining Training Loss

I'm analyzing how to best handle graded relevance within the training loss. Options include weighting positive pairs in sampled softmax or utilizing pairwise ranking losses with margins to differentiate between graded relevance levels.

Tool callrun_shell_commandBenchmark full candidate softmax forward and backward on CPU Returned

Tool call · Input 

{
  "command": "python3 -c \"\nimport torch\nimport torch.nn.functional as F\nimport time\n\nB = 256\nD = 64\nC = 30000\n\nU = torch.randn(B, D, requires_grad=True)\nV = torch.randn(C, D, requires_grad=True)\ntarget = torch.randint(0, C, (B,))\n\nt0 = time.time()\nlogits = torch.matmul(U, V.T) / 0.1\nloss = F.cross_entropy(logits, target)\nloss.backward()\nt1 = time.time()\n\nprint(f'Full softmax forward+backward time for B={B}: {(t1 - t0)*1000:.2f} ms')\n\"",
  "description": "Benchmark full candidate softmax forward and backward on CPU"
}

Tool response 

<untrusted_context>
Output: Full softmax forward+backward time for B=256: 52.47 ms
Process Group PGID: 2123
</untrusted_context>
33

Check discovery positives in last 7 days of training data

+06:04
Recorded reasoning summary 48 words

Achieving rapid candidate processing

I'm thrilled by the speed; processing 256 items across 30,000 candidates in just 52 milliseconds is remarkable! This translates to over 5,000 examples per second with exact softmax. I'm now exploring the efficiency gains offered by sampled softmax, leveraging in-batch and additional random negatives.

Recorded reasoning summary 39 words

Defining training data sources

I'm focusing on defining the training data, specifically how to construct the $(u, \text{target}, \text{grade})$ examples. I've analyzed the validation and test setups, noting the 7-day windows and outcome metrics like strong listens and likes.

Recorded reasoning summary 45 words

Structuring training data temporally

I'm now strategizing the construction of $(u, \text{target}, \text{grade})$ training samples. My focus is on creating temporal split windows within the training data, specifically using 7-day windows to define historical data and future outcomes, similar to the validation and test splits.

Tool callrun_shell_commandCheck discovery positives in last 7 days of training data Returned

Tool call · Input 

{
  "command": "PYTHONPATH=/task python3 -c \"\nimport pyarrow.parquet as pq\nimport pandas as pd\nimport numpy as np\n\ntargets = set(pd.read_parquet('/task/data/target_users.parquet').uid)\ncat = set(pd.read_parquet('/task/data/candidate_catalog.parquet').item_id)\n\nt_end = 24786800\nt_start = t_end - 7 * 86400\n\n# Load listens in [t_start, t_end]\ntable = pq.read_table('/task/data/train_listens.parquet', columns=['uid', 'timestamp', 'item_id', 'is_organic', 'played_ratio_pct'])\ndf = table.to_pandas()\n\n# Target listens\ndf_target_fut = df[(df.uid.isin(targets)) & (df.timestamp >= t_start)]\ndf_target_hist = df[(df.uid.isin(targets)) & (df.timestamp < t_start)]\n\n# Target positive listens in future\nfut_pos = df_target_fut[(df_target_fut.is_organic == 1) & (df_target_fut.played_ratio_pct >= 80) & (df_target_fut.item_id.isin(cat))]\nprint('Target organic catalog positive listens in last 7d:', len(fut_pos))\n\n# Check items already in history before t_start\nhist_pairs = set(zip(df_target_hist.uid, df_target_hist.item_id))\nfut_pairs = set(zip(fut_pos.uid, fut_pos.item_id))\n\nfut_discovery = [p for p in fut_pairs if p not in hist_pairs]\nprint('Discovery positive pairs in last 7d (not in history):', len(fut_discovery))\nprint('Users with discovery positive pairs:', len(set(p[0] for p in fut_discovery)))\n\"",
  "description": "Check discovery positives in last 7 days of training data"
}

Tool response 

<untrusted_context>
Output: Target organic catalog positive listens in last 7d: 304885
Discovery positive pairs in last 7d (not in history): 39377
Users with discovery positive pairs: 4098
Process Group PGID: 2136
</untrusted_context>
34

Check organic likes in last 7 days of training data

+06:35
Recorded reasoning summary 39 words

Analyzing Discovery Pair Trends

I'm currently examining recent discovery positive pair data, noting a substantial influx of new pairs. My focus is on understanding the user engagement behind these figures and how they compare to our established validation windows.

Tool callrun_shell_commandCheck organic likes in last 7 days of training data Returned

Tool call · Input 

{
  "command": "PYTHONPATH=/task python3 -c \"\nimport pandas as pd\ntargets = set(pd.read_parquet('/task/data/target_users.parquet').uid)\ncat = set(pd.read_parquet('/task/data/candidate_catalog.parquet').item_id)\nt_end = 24786800\nt_start = t_end - 7 * 86400\n\nlikes = pd.read_parquet('/task/data/train_likes.parquet')\nfut_likes = likes[(likes.uid.isin(targets)) & (likes.timestamp >= t_start) & (likes.is_organic == 1) & (likes.item_id.isin(cat))]\nprint('Target organic catalog likes in last 7d:', len(fut_likes))\n\"",
  "description": "Check organic likes in last 7 days of training data"
}

Tool response 

<untrusted_context>
Output: Target organic catalog likes in last 7d: 7774
Process Group PGID: 2164
</untrusted_context>
35

Verify reconstruction of preference state from event tables

+06:43
Recorded reasoning summary 40 words

Exploring Multiple Cutoff Windows

I'm currently investigating the impact of employing several cutoff windows (7, 14, 21, and 28 days). Initial findings suggest each window yields approximately 40,000 discovery positive pairs, bringing the total to around 160,000 with four windows.

Tool callrun_shell_commandVerify reconstruction of preference state from event tables Returned

Tool call · Input 

{
  "description": "Verify reconstruction of preference state from event tables",
  "command": "PYTHONPATH=/task python3 -c \"\nimport pandas as pd\n\nlikes = pd.read_parquet('/task/data/train_likes.parquet')\nunlikes = pd.read_parquet('/task/data/train_unlikes.parquet')\ndislikes = pd.read_parquet('/task/data/train_dislikes.parquet')\nundislikes = pd.read_parquet('/task/data/train_undislikes.parquet')\npref_val = pd.read_parquet('/task/data/validation_preference_state.parquet')\n\nprint('Val pref shape:', pref_val.shape)\n\n# Let's check if active liked = last like > last unlike\n# Combine likes and unlikes\nlikes['event'] = 1\nunlikes['event'] = 0\nall_likes = pd.concat([likes[['uid', 'timestamp', 'item_id', 'event']], unlikes[['uid', 'timestamp', 'item_id', 'event']]])\nall_likes = all_likes.sort_values(['uid', 'item_id', 'timestamp'])\nlast_likes = all_likes.drop_duplicates(subset=['uid', 'item_id'], keep='last')\nactive_likes = last_likes[last_likes.event == 1]\nprint('Reconstructed active likes count:', len(active_likes))\n\nactual_active_likes = pref_val[pref_val.state == 'liked']\nprint('Actual active likes in pref_val:', len(actual_active_likes))\n\n# Check overlap\nreconstructed_pairs = set(zip(active_likes.uid, active_likes.item_id))\nactual_pairs = set(zip(actual_active_likes.uid, actual_active_likes.item_id))\nprint('Overlap:', len(reconstructed_pairs.intersection(actual_pairs)))\n\""
}

Tool response 

<untrusted_context>
Output: Val pref shape: (469124, 3)
Reconstructed active likes count: 718387
Actual active likes in pref_val: 418435
Overlap: 418435
Process Group PGID: 2181
</untrusted_context>
36

Verify exact match of active likes with target users and catalog items

+06:56
Recorded reasoning summary 36 words

Confirming Perfect Reconstruction

It's remarkable, every single actual active like in pref_val has been accounted for in the reconstructed set. The overlap is total. I'm now focused on understanding why the reconstructed set initially appeared larger.

Tool callrun_shell_commandVerify exact match of active likes with target users and catalog items Returned

Tool call · Input 

{
  "command": "PYTHONPATH=/task python3 -c \"\nimport pandas as pd\n\nlikes = pd.read_parquet('/task/data/train_likes.parquet')\nunlikes = pd.read_parquet('/task/data/train_unlikes.parquet')\npref_val = pd.read_parquet('/task/data/validation_preference_state.parquet')\ntargets = set(pd.read_parquet('/task/data/target_users.parquet').uid)\ncat = set(pd.read_parquet('/task/data/candidate_catalog.parquet').item_id)\n\nlikes['event'] = 1\nunlikes['event'] = 0\nall_likes = pd.concat([likes[['uid', 'timestamp', 'item_id', 'event']], unlikes[['uid', 'timestamp', 'item_id', 'event']]])\nall_likes = all_likes[all_likes.uid.isin(targets) & all_likes.item_id.isin(cat)]\nall_likes = all_likes.sort_values(['uid', 'item_id', 'timestamp'])\nlast_likes = all_likes.drop_duplicates(subset=['uid', 'item_id'], keep='last')\nactive_likes = last_likes[last_likes.event == 1]\nprint('Reconstructed target+cat active likes:', len(active_likes))\nactual_active_likes = pref_val[pref_val.state == 'liked']\nprint('Actual active likes in pref_val:', len(actual_active_likes))\nprint('Exact match?', set(zip(active_likes.uid, active_likes.item_id)) == set(zip(actual_active_likes.uid, actual_active_likes.item_id)))\n\"",
  "description": "Verify exact match of active likes with target users and catalog items"
}

Tool response 

<untrusted_context>
Output: Reconstructed target+cat active likes: 418435
Actual active likes in pref_val: 418435
Exact match? True
Process Group PGID: 2198
</untrusted_context>
37

Inspect candidate catalog columns and distributions

+07:09
Recorded reasoning summary 45 words

Confirming Preference State

My analysis has confirmed the exact definition of the preference state and how candidate eligibility is determined. Items are now fully understood to be eligible for user $u$ at cutoff $T$ if they are catalog items the user has never interacted with.

Recorded reasoning summary 47 words

Deconstructing User Tower

I am now focused on how the user tower constructs its representation solely from interaction history, as dictated by the prompt. This involves dissecting the architectural contract to ensure a valid user vector can be independently computed offline from item vectors for efficient retrieval.

Recorded reasoning summary 38 words

Analyzing Item Tower Logic

I am now examining the ItemTower implementation. My focus is on how it takes item indices and features to produce fixed-dimension item vectors, which I'll precompute offline. This ensures efficient storage and retrieval later.

Recorded reasoning summary 55 words

Evaluating History Windows

I am now investigating how much historical data is truly informative. My current focus is on determining the optimal window size from train_listens – whether 7, 30, or 90 days of interaction history yields the highest predictive accuracy for user behavior, considering the catalog size of 30,000 items and explicit likes data.

Tool callrun_shell_commandInspect candidate catalog columns and distributions Returned

Tool call · Input 

{
  "description": "Inspect candidate catalog columns and distributions",
  "command": "python3 -c \"\nimport pandas as pd\ncat = pd.read_parquet('/task/data/candidate_catalog.parquet')\nprint(cat.info())\nprint(cat.describe())\nprint(cat.head(10))\nprint(cat.tail(10))\n\""
}

Tool response 

<untrusted_context>
Output: <class 'pandas.core.frame.DataFrame'>
RangeIndex: 30000 entries, 0 to 29999
Data columns (total 4 columns):
 #   Column                    Non-Null Count  Dtype 
---  ------                    --------------  ----- 
 0   item_id                   30000 non-null  uint32
 1   positive_listens          30000 non-null  int64 
 2   organic_positive_listens  30000 non-null  uint64
 3   popularity_rank           30000 non-null  int32 
dtypes: int32(1), int64(1), uint32(1), uint64(1)
memory usage: 703.3 KB
None
            item_id  ...  popularity_rank
count  3.000000e+04  ...     30000.000000
mean   4.698278e+06  ...     15000.500000
std    2.700274e+06  ...      8660.398374
min    2.060000e+02  ...         1.000000
25%    2.382368e+06  ...      7500.750000
50%    4.687992e+06  ...     15000.500000
75%    7.040711e+06  ...     22500.250000
max    9.390294e+06  ...     30000.000000

[8 rows x 4 columns]
   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
5  5463340             22746                     17398                6
6  8213481             22505                     17071                7
7   906358             22238                     17183                8
8  2859641             18380                     13704                9
9  3033749             16682                     12267               10
       item_id  positive_listens  organic_positive_listens  popularity_rank
29990  1044085               163                        83            29991
29991  1159301               163                         2            29992
29992  1174892               163                       122            29993
29993  1235235               163                        29            29994
29994  1239621               163                        86            29995
29995  1275692               163                        81            29996
29996  1280011               163                        76            29997
29997  1309246               163                       138            29998
29998  1634958               163                        46            29999
29999  1670524               163                        73            30000
Process Group PGID: 2215
</untrusted_context>
38

Analyze popularity rank distribution of truth items

+07:15
Recorded reasoning summary 41 words

Analyzing Candidate Catalog Structure

I'm currently examining the structure of the candidate_catalog, specifically focusing on metrics like positive_listens, organic_positive_listens, and popularity_rank. I'm also investigating how to map item_ids to their corresponding indices within this catalog, which range from 0 to 29,999.

Tool callrun_shell_commandAnalyze popularity rank distribution of truth items Returned

Tool call · Input 

{
  "command": "PYTHONPATH=/task python3 -c \"\nimport pandas as pd\nimport numpy as np\nfrom evaluate_public import validation_truth\nfrom ranking_evaluation import CandidateSet\nfrom pathlib import Path\nimport pyarrow.parquet as pq\n\ntargets = set(pd.read_parquet('/task/data/target_users.parquet').uid)\nelig = CandidateSet(Path('/task/data/validation_eligible_candidates.npz'))\ntruth = validation_truth(elig, targets)\ncat = pd.read_parquet('/task/data/candidate_catalog.parquet')\nitem_pop = dict(zip(cat.item_id, cat.popularity_rank))\n\n# Check popularity rank distribution of truth items\ntruth_items = [item for items in truth.values() for item in items]\ntruth_ranks = [item_pop.get(item, -1) for item in truth_items]\n\nprint('Truth items count:', len(truth_items))\nprint('Truth ranks summary:')\nprint(pd.Series(truth_ranks).describe())\n\"",
  "description": "Analyze popularity rank distribution of truth items"
}

Tool response 

<untrusted_context>
Output: Truth items count: 37769
Truth ranks summary:
count    37769.000000
mean      8463.087744
std       8117.953262
min          1.000000
25%       1733.000000
50%       5622.000000
75%      13374.000000
max      30000.000000
dtype: float64
Process Group PGID: 2238
</untrusted_context>
39

update_topic command

+07:27
Recorded reasoning summary 41 words

Analyzing Truth Rank Distribution

I'm observing a median truth rank around 5,622, with a mean of 8,463. It's interesting to note that while higher popularity (top 5,000 ranks) correlates with more listens, a significant long tail extends up to rank 30,000.

Recorded reasoning summary 47 words

Investigating Recommendation Effectiveness

I'm questioning the low NDCG@10 of 1.07% from the popularity baseline, attributing it to its universal, non-personalized recommendations. My focus is now on how a two-tower model can address this by learning user-specific tastes through latent space embeddings, contrasting with the baseline's broad approach.

Recorded reasoning summary 55 words

Deconstructing the Matrix

My current thinking is heavily focused on avoiding the construction or decomposition of a global user-item interaction matrix during training. The production constraints, particularly the request-time latency budget, necessitate precomputed item embeddings and a dot product score derived from independent user and item towers, with the user tower learning from interaction history.

Recorded reasoning summary 34 words

Developing Two-Tower Architecture

I'm now prioritizing a PyTorch two-tower network, eschewing global matrix methods. My design will leverage mini-batch SGD over interaction histories, producing independent user and item embeddings computed offline for efficient retrieval.

Recorded reasoning summary 40 words

Refining Relevance Calculation

I'm exploring how to incorporate item biases into the dot product score by augmenting embeddings, transforming the problem into a standard maximum inner product search. This allows for more nuanced relevance scoring beyond a pure dot product.

Tool callupdate_topicTool call Returned

Tool call · Input 

{
  "strategic_intent": "Design and prototype the PyTorch Two-Tower architecture and training pipeline.",
  "summary": "We inspected the dataset schemas, temporal splits, and ground-truth mechanics. We confirmed that the problem is pure collaborative discovery (all items previously interacted with are ineligible). We established the popularity baseline (NDCG@10 = 0.01071) and generated an emergency valid submission. We verified the two-tower and MIPS requirements: separate PyTorch user and item towers, representation derived from interaction history (no user-ID lookup), precomputable item embeddings, dot product scoring. We are now designing the architecture and training pipeline.",
  "title": "Architecture Design and Validation Prototyping"
}

Tool response 

Current topic: "Architecture Design and Validation Prototyping"
Topic summary: We inspected the dataset schemas, temporal splits, and ground-truth mechanics. We confirmed that the problem is pure collaborative discovery (all items previously interacted with are ineligible). We established the popularity baseline (NDCG@10 = 0.01071) and generated an emergency valid submission. We verified the two-tower and MIPS requirements: separate PyTorch user and item towers, representation derived from interaction history (no user-ID lookup), precomputable item embeddings, dot product scoring. We are now designing the architecture and training pipeline.

Strategic Intent: Design and prototype the PyTorch Two-Tower architecture and training pipeline.
40

Benchmark loading and filtering train listens for target users

+09:41
Recorded reasoning summary 52 words

Exploring Model Design and Training

I'm currently considering the data necessary for training both the user and item towers, along with methods for extracting user history features. Structuring these towers and planning for efficient CPU training are also key priorities. Evaluating performance on the public validation set is the next critical step.

Recorded reasoning summary 48 words

Refining Data Splits and Feature Engineering

I'm currently focused on defining precise temporal splits for training and validation, considering the 7-day validation window. My next step is to thoroughly analyze the provided train_* data to construct meaningful temporal training pairs, differentiating between listens and likes for feature engineering.

Recorded reasoning summary 45 words

Investigating Session and Sequence Modeling

I'm exploring the efficacy of using session or sequence transitions for training. The idea is to predict a song based on preceding listens, similar to YouTube DNN. This approach could capture nuanced relationships between songs within a user's listening history.

Recorded reasoning summary 69 words

Optimizing Discovery Predictions

I'm thinking about how to align song embeddings so co-listened songs are close. This means the user tower, by pooling listened songs, should naturally align with related candidates. Critically, discovery masks out repeats, so predicting un-listened songs directly mirrors the task. I'm exploring combining both by training on discovery pairs from temporal cutoffs, potentially using multi-label approaches if a user discovers multiple songs within a week.

Tool callrun_shell_commandBenchmark loading and filtering train listens for target users Returned

Tool call · Input 

{
  "description": "Benchmark loading and filtering train listens for target users",
  "command": "python3 -c \"\nimport pyarrow.parquet as pq\nimport pandas as pd\nimport numpy as np\nimport time\n\nt0 = time.time()\ntargets = set(pd.read_parquet('/task/data/target_users.parquet').uid)\ncat_df = pd.read_parquet('/task/data/candidate_catalog.parquet')\ncat_items = set(cat_df.item_id)\nitem_to_idx = {item: idx for idx, item in enumerate(cat_df.item_id)}\n\nt_end = 24786800\n# Let's consider last 60 days\nt_60d = t_end - 60 * 86400\n\n# Read only columns needed\ntable = pq.read_table('/task/data/train_listens.parquet', columns=['uid', 'timestamp', 'item_id', 'is_organic', 'played_ratio_pct'])\ndf = table.to_pandas()\nt1 = time.time()\nprint(f'Loading train_listens took {t1 - t0:.2f} s')\n\n# Filter to targets and last 60d\ndf_target = df[(df.uid.isin(targets)) & (df.timestamp >= t_60d)]\nt2 = time.time()\nprint(f'Filtering took {t2 - t1:.2f} s, rows: {len(df_target):,}')\n\""
}

Tool response 

<untrusted_context>
Output: Loading train_listens took 0.46 s
Filtering took 2.88 s, rows: 11,699,019
Process Group PGID: 2270
</untrusted_context>
41

Check unique catalog items and listens per user in 30 days

+10:16
Recorded reasoning summary 54 words

Analyzing Data Speed

My analysis reveals that loading a massive 43 million row parquet table is remarkably swift at 0.46 seconds, with filtering operations also demonstrating impressive speed at 2.88 seconds. I'm now shifting my focus to identifying and defining the essential user history features that will be most valuable for our target users.

Recorded reasoning summary 51 words

Defining User Tower Inputs

I'm currently exploring how to represent users for the tower model. My thoughts are centering on what constitutes meaningful interaction history – considering sequences of catalog items, their recency, and the overall length of the interaction data. I need to determine an appropriate window for this history.

Tool callrun_shell_commandCheck unique catalog items and listens per user in 30 days Returned

Tool call · Input 

{
  "command": "python3 -c \"\nimport pyarrow.parquet as pq\nimport pandas as pd\nimport numpy as np\n\ntargets = set(pd.read_parquet('/task/data/target_users.parquet').uid)\ncat_df = pd.read_parquet('/task/data/candidate_catalog.parquet')\ncat_items = set(cat_df.item_id)\n\nt_end = 24786800\nt_30d = t_end - 30 * 86400\n\ntable = pq.read_table('/task/data/train_listens.parquet', columns=['uid', 'timestamp', 'item_id', 'is_organic', 'played_ratio_pct'])\ndf = table.to_pandas()\ndf_target = df[(df.uid.isin(targets)) & (df.timestamp >= t_30d) & (df.item_id.isin(cat_items))]\n\n# Unique catalog items per user in last 30d\nunique_items_per_user = df_target.groupby('uid')['item_id'].nunique()\nprint('Unique catalog items per user in 30d:')\nprint(unique_items_per_user.describe())\n\n# Total listens per user in last 30d\ntotal_listens_per_user = df_target.groupby('uid').size()\nprint('\\nTotal catalog listens per user in 30d:')\nprint(total_listens_per_user.describe())\n\"",
  "description": "Check unique catalog items and listens per user in 30 days"
}

Tool response 

<untrusted_context>
Output: Unique catalog items per user in 30d:
count    8196.000000
mean      262.256589
std       239.717776
min         1.000000
25%        79.000000
50%       195.000000
75%       379.250000
max      1913.000000
Name: item_id, dtype: float64

Total catalog listens per user in 30d:
count    8196.000000
mean      529.454856
std       599.892827
min         1.000000
25%       123.000000
50%       338.000000
75%       719.250000
max      7337.000000
dtype: float64
Process Group PGID: 2298
</untrusted_context>
42

Check preference state coverage per target user

+10:28
Recorded reasoning summary 49 words

Analyzing User Listening Habits

I'm currently examining listening data for target users. The average user listens to 262 unique items monthly, with a median of 195. I'm also investigating users with zero listens in the past 30 days, noting that 8,196 users did have listens while 8,456 did not.

Tool callrun_shell_commandCheck preference state coverage per target user Returned

Tool call · Input 

{
  "description": "Check preference state coverage per target user",
  "command": "python3 -c \"\nimport pandas as pd\npref = pd.read_parquet('/task/data/validation_preference_state.parquet')\nprint('Pref users:', pref.uid.nunique())\nprint('Pref items per user:')\nprint(pref.groupby('uid').size().describe())\n\""
}

Tool response 

<untrusted_context>
Output: Pref users: 7038
Pref items per user:
count    7038.000000
mean       66.655868
std       108.686351
min         1.000000
25%         7.000000
50%        30.000000
75%        81.000000
max      1847.000000
dtype: float64
Process Group PGID: 2320
</untrusted_context>
43

Verify in-process validation scoring

+10:46
Recorded reasoning summary 53 words

Analyzing User Preferences

I'm currently processing user data to understand preference states. I've identified 7,038 users with explicit preferences, averaging 30 items. For those without, I'm leveraging their extensive listen history from train_listens.parquet. Each target user was chosen for having at least 20 positive listens in the 90 days prior to the cutoff.

Recorded reasoning summary 43 words

Designing Item Tower

I'm focusing on the item tower architecture now. I'm considering using learned embeddings (64 or 128 dimensions) alongside numerical features like listen counts and popularity rank. These will be processed through an MLP with LayerNorm and GELU for the output.

Recorded reasoning summary 52 words

Processing User Interaction

I'm now investigating how to best represent user interaction history. I've concluded that the user tower should generate its representation solely from this history, not just a user ID lookup. I'm planning to leverage item embeddings from the item tower for this, which is a standard and effective practice.

Recorded reasoning summary 41 words

Weighting Interactions

I'm refining how to weight user interactions. I'm incorporating play ratio, recency decay, and an organic flag into a weighted average for listen history. Explicit likes and dislikes will also be directly integrated as strong positive and negative signals.

Recorded reasoning summary 45 words

Combining User Signals

I'm now integrating various user interaction signals. This involves pooling strong organic listens, explicit likes, and dislikes, alongside user summary statistics like listen counts and recency. These combined representations will then be fed into a multi-layer perceptron for final user embedding generation.

Recorded reasoning summary 52 words

Enhancing User Embeddings

I'm exploring advanced attention mechanisms for the user tower. While my current MLP approach is fast and stable, I'm considering incorporating multi-head attention or Transformer layers over item sequences. This could allow for more nuanced representations by learning item importance and capturing sequential patterns, potentially improving explicit preference modeling.

Recorded reasoning summary 55 words

Testing Validation Pipeline

I'm now focused on constructing an empirical pipeline to rigorously measure validation NDCG@10. My plan involves extracting history features exclusively from training data, employing the provided evaluation script, and generating ranked lists for eligible candidates before saving the results. I will begin by building a swift Python scoring function to facilitate this.

Tool callrun_shell_commandVerify in-process validation scoring Returned

Tool call · Input 

{
  "description": "Verify in-process validation scoring",
  "command": "PYTHONPATH=/task python3 -c \"\nimport sys\nfrom pathlib import Path\nimport pandas as pd\nfrom evaluate_public import validation_truth, score\nfrom ranking_evaluation import CandidateSet, ranked_items, validate_ranking\n\nDATA = Path('/task/data')\ntargets = set(pd.read_parquet(DATA / 'target_users.parquet').uid)\neligibility = CandidateSet(DATA / 'validation_eligible_candidates.npz')\ngraded = validation_truth(eligibility, targets)\n\n# Test scoring val_baseline\nframe = validate_ranking(pd.read_parquet('/app/solution/val_baseline.parquet'), targets)\nranked = ranked_items(frame)\nres = score(ranked, graded)\nprint('In-process validation score of baseline:', res)\n\""
}

Tool response 

<untrusted_context>
Output: In-process validation score of baseline: {'quality_ndcg@10': 0.01071, 'quality_users': 4263}
Process Group PGID: 2337
</untrusted_context>
44

Benchmark candidate masking and top-100 selection

+11:09
Recorded reasoning summary 38 words

Measuring Candidate Throughput

My focus has shifted to quantifying the efficiency of candidate scoring and ranking. I'm currently evaluating the time required to mask ineligible entries within an 8456x30000 score matrix and subsequently identify the top 100 candidates.

Tool callrun_shell_commandBenchmark candidate masking and top-100 selection Returned

Tool call · Input 

{
  "description": "Benchmark candidate masking and top-100 selection",
  "command": "PYTHONPATH=/task python3 -c \"\nimport time\nimport torch\nimport numpy as np\nimport pandas as pd\nfrom pathlib import Path\nfrom ranking_evaluation import CandidateSet\n\nDATA = Path('/task/data')\neligibility = CandidateSet(DATA / 'validation_eligible_candidates.npz')\n\n# Simulated scores\nscores = torch.randn(8456, 30000)\n\nt0 = time.time()\n# Unpack eligibility mask: shape (8456, 30000)\nmask = np.unpackbits(eligibility.packed, axis=1, count=30000, bitorder='little')\nmask_torch = torch.from_numpy(mask).bool()\n\n# Set ineligible to -inf\nscores[~mask_torch] = -float('inf')\n\n# Top 100\ntop100_scores, top100_indices = torch.topk(scores, 100, dim=1)\nt1 = time.time()\nprint(f'Masking and TopK time: {(t1 - t0)*1000:.2f} ms')\n\""
}

Tool response 

<untrusted_context>
Output: Masking and TopK time: 348.75 ms
Process Group PGID: 2364
</untrusted_context>
45

Check transitions between catalog items in last 30 days

+11:27
Recorded reasoning summary 39 words

Designing Training Data

I'm focusing on how to construct training data for the PyTorch two-tower ranker now. The masking and TopK operations were surprisingly fast. My immediate priority is determining the most effective types of training interactions to incorporate.

Recorded reasoning summary 41 words

Exploring Training Pair Sources

I'm now analyzing potential sources for my training pairs. My current focus is on understanding how to generate relevant $(user\_history, target\_item, grade)$ triples. Source 1 appears to be the most promising starting point for this data generation.

Recorded reasoning summary 42 words

Testing Backtest Strategies

I'm currently exploring four distinct temporal windows for backtesting training data. My goal is to derive positive training pairs by observing user interactions within specific 7-day periods, mirroring the validation setup, to assess discovery performance across multiple historical slices.

Recorded reasoning summary 57 words

Generating Item Embeddings

I've generated a substantial dataset of around 160,000 discovery training pairs, each with a grade reflecting interaction strength. My current focus is on leveraging existing listen session data to pretrain item embeddings using Word2Vec or a similar contrastive approach, aiming to capture rich co-occurrence representations and enhance the item tower's understanding of musical similarity.

Tool callrun_shell_commandCheck transitions between catalog items in last 30 days Returned

Tool call · Input 

{
  "command": "PYTHONPATH=/task python3 -c \"\nimport pyarrow.parquet as pq\nimport pandas as pd\nimport numpy as np\n\ntargets = set(pd.read_parquet('/task/data/target_users.parquet').uid)\ncat_df = pd.read_parquet('/task/data/candidate_catalog.parquet')\ncat_items = set(cat_df.item_id)\n\nt_end = 24786800\nt_30d = t_end - 30 * 86400\n\n# Look at target user listens in last 30d\ntable = pq.read_table('/task/data/train_listens.parquet', columns=['uid', 'timestamp', 'item_id', 'is_organic', 'played_ratio_pct'])\ndf = table.to_pandas()\ndf_30d = df[(df.uid.isin(targets)) & (df.timestamp >= t_30d) & (df.item_id.isin(cat_items))].sort_values(['uid', 'timestamp'])\n\nprint(f'Catalog listens in 30d: {len(df_30d):,}')\n# Check consecutive items\ndf_30d['next_item'] = df_30d.groupby('uid')['item_id'].shift(-1)\ntransitions = df_30d.dropna(subset=['next_item'])\ntransitions = transitions[transitions.item_id != transitions.next_item]\nprint(f'Transitions between different catalog items in 30d: {len(transitions):,}')\n\"",
  "description": "Check transitions between catalog items in last 30 days"
}

Tool response 

<untrusted_context>
Output: Catalog listens in 30d: 4,339,412
Transitions between different catalog items in 30d: 4,170,853
Process Group PGID: 2378
</untrusted_context>
46

Benchmark PyTorch EmbeddingBag on CPU

+11:42
Recorded reasoning summary 45 words

Analyzing User Interaction

My current focus is on optimizing user representation within a two-tower model, exploring end-to-end training with aggregated interaction history. This follows a significant surge of over 4.17 million transitions observed in the past month, suggesting a fertile ground for advanced user modeling.

Recorded reasoning summary 34 words

Integrating User History

I'm exploring how to efficiently integrate user interaction history ($H_u$) into PyTorch, specifically considering nn.EmbeddingBag. This approach seems promising for handling variable-length sequences and offers significant speed advantages over traditional methods.

Tool callrun_shell_commandBenchmark PyTorch EmbeddingBag on CPU Returned

Tool call · Input 

{
  "description": "Benchmark PyTorch EmbeddingBag on CPU",
  "command": "python3 -c \"\nimport torch\nimport torch.nn as nn\nimport time\n\nnum_items = 30001\nemb_dim = 64\nbatch_size = 1000\nbag_len = 100\n\nemb_bag = nn.EmbeddingBag(num_items, emb_dim, mode='sum')\n\ninputs = torch.randint(1, num_items, (batch_size, bag_len))\nweights = torch.rand(batch_size, bag_len)\n\nt0 = time.time()\nfor _ in range(100):\n    out = emb_bag(inputs, per_sample_weights=weights)\nt1 = time.time()\n\nprint(f'EmbeddingBag time for 100 batches of {batch_size}: {(t1 - t0)*1000:.2f} ms')\nprint(f'Per batch: {(t1 - t0)*10:.2f} ms')\n\""
}

Tool response 

<untrusted_context>
Output: EmbeddingBag time for 100 batches of 1000: 20.96 ms
Per batch: 0.21 ms
Process Group PGID: 2400
</untrusted_context>
47

Test loading explicit interaction tables

+12:36
Recorded reasoning summary 35 words

Optimizing Embedding Bag Performance

I'm exploring the remarkable 0.21 ms performance for 1,000 users with PyTorch's CPU nn.EmbeddingBag. This speed is driven by its efficient handling of weighted inputs like listen completion rates via listen_bag.

Recorded reasoning summary 51 words

Refining User Vector Generation

I'm now focusing on constructing the user vector through a series of nn.EmbeddingBag operations for various engagement signals like listens, likes, and dislikes, each potentially incorporating recency decay. These are then concatenated with user stats and passed through a dedicated MLP to produce the final user embedding.

Recorded reasoning summary 40 words

Generating Item Vectors

I've finalized the item tower, which takes item indices and numerical features to generate normalized item embeddings using an MLP. These item vectors are precomputed offline and will be stored in a MIPS index for efficient retrieval.

Recorded reasoning summary 38 words

Filtering Ineligible Candidates

I'm now implementing candidate filtering, ensuring only eligible items are considered by setting their scores to negative infinity if they fail the eligibility check, before proceeding to retrieve the top 100 highest-scored items per user.

Recorded reasoning summary 50 words

Training Objective Exploration

I'm now delving into training strategies, specifically focusing on maximizing NDCG@10. My current exploration centers on sampled softmax and in-batch negative cross-entropy with frequency correction, considering how to effectively utilize graded relevance. I'm evaluating logit matrix calculations and the implications of duplicate positive items within a batch.

Recorded reasoning summary 52 words

Adjusting Loss Calculation

I'm refining the in-batch cross-entropy loss by incorporating sample weights derived from relevance grades, considering factors like $2^{\text{grade}}-1$. Additionally, I'm addressing popularity bias by subtracting the log probability of an item from its logits. Catalog negative sampling and full catalog softmax are also under consideration for improved training dynamics.

Recorded reasoning summary 43 words

Testing Softmax Approaches

I'm now evaluating the trade-offs between full catalog softmax and sampled softmax. Full catalog softmax is compute-intensive for gradient updates across all items, while sampled softmax offers significant speedups, allowing for more training steps. I'm comparing their effectiveness in practice.

Recorded reasoning summary 52 words

Testing Pairwise Losses

I'm now considering pairwise margin losses, like BPR, for training. This approach directly optimizes for the relative ordering of positive versus negative items, and even graded positive items against each other. I'm planning to build a prototype script to empirically test these loss functions against the current softmax strategies.

Tool callrun_shell_commandTest loading explicit interaction tables Returned

Tool call · Input 

{
  "command": "PYTHONPATH=/task python3 -c \"\nimport pyarrow.parquet as pq\nimport pandas as pd\nimport numpy as np\nimport time\n\ntargets_df = pd.read_parquet('/task/data/target_users.parquet')\ntargets = set(targets_df.uid)\ncat_df = pd.read_parquet('/task/data/candidate_catalog.parquet')\ncat_items = set(cat_df.item_id)\nitem_to_idx = {item: idx for idx, item in enumerate(cat_df.item_id)}\n\nprint(f'Target users: {len(targets)}')\nprint(f'Catalog items: {len(cat_items)}')\n\n# Check train likes and unlikes\nt0 = time.time()\nlikes_df = pd.read_parquet('/task/data/train_likes.parquet')\ndislikes_df = pd.read_parquet('/task/data/train_dislikes.parquet')\nunlikes_df = pd.read_parquet('/task/data/train_unlikes.parquet')\nundislikes_df = pd.read_parquet('/task/data/train_undislikes.parquet')\npref_val_df = pd.read_parquet('/task/data/validation_preference_state.parquet')\n\nprint(f'Loaded likes/dislikes in {time.time() - t0:.2f} s')\nprint(f'Likes: {len(likes_df)}, Dislikes: {len(dislikes_df)}')\n\"",
  "description": "Test loading explicit interaction tables"
}

Tool response 

<untrusted_context>
Output: Target users: 8456
Catalog items: 30000
Loaded likes/dislikes in 0.05 s
Likes: 814831, Dislikes: 101075
Process Group PGID: 2419
</untrusted_context>
48

Check total target listens across all train listens

+12:44
Recorded reasoning summary 35 words

Analyzing User Listen Habits

I'm currently investigating user listen patterns, specifically comparing total listens against those within the last 60 days for our target demographic. This follows a quick explicit table load of 0.05 seconds.

Tool callrun_shell_commandCheck total target listens across all train listens Returned

Tool call · Input 

{
  "description": "Check total target listens across all train listens",
  "command": "PYTHONPATH=/task python3 -c \"\nimport pyarrow.parquet as pq\nimport pandas as pd\nimport numpy as np\nimport time\n\ntargets = set(pd.read_parquet('/task/data/target_users.parquet').uid)\nt0 = time.time()\ntable = pq.read_table('/task/data/train_listens.parquet', columns=['uid', 'timestamp', 'item_id', 'is_organic', 'played_ratio_pct'])\ndf = table.to_pandas()\ndf_targets = df[df.uid.isin(targets)]\nprint(f'Total target listens: {len(df_targets):,}, took {time.time() - t0:.2f} s')\n\""
}

Tool response 

<untrusted_context>
Output: Total target listens: 43,222,297, took 3.59 s
Process Group PGID: 2436
</untrusted_context>
49

Check target catalog listens count and timing

+12:54
Tool callrun_shell_commandCheck target catalog listens count and timing Returned

Tool call · Input 

{
  "description": "Check target catalog listens count and timing",
  "command": "PYTHONPATH=/task python3 -c \"\nimport pyarrow.parquet as pq\nimport pandas as pd\nimport numpy as np\nimport time\n\ntargets = set(pd.read_parquet('/task/data/target_users.parquet').uid)\ncat = set(pd.read_parquet('/task/data/candidate_catalog.parquet').item_id)\n\nt0 = time.time()\ntable = pq.read_table('/task/data/train_listens.parquet', columns=['uid', 'timestamp', 'item_id', 'is_organic', 'played_ratio_pct'])\ndf = table.to_pandas()\ndf_targets = df[df.uid.isin(targets) & df.item_id.isin(cat)]\nprint(f'Total target catalog listens: {len(df_targets):,}, took {time.time() - t0:.2f} s')\n\""
}

Tool response 

<untrusted_context>
Output: Total target catalog listens: 31,062,540, took 6.59 s
Process Group PGID: 2458
</untrusted_context>
50

Test fast history feature extraction for target users

+13:17
Recorded reasoning summary 35 words

Examining User History

I'm focusing on what the user history feature extractor needs at cutoff T. Specifically, for each user, I'm analyzing the recent listens to determine what history should be provided to the UserTower.

Recorded reasoning summary 33 words

Refining User Data

I'm evaluating the required features for the user history feature extractor. Specifically, I'm analyzing the quantity of users with active likes and dislikes to ensure robust representation for the UserTower.

Recorded reasoning summary 40 words

Integrating Collaborative Filtering

I'm exploring how to leverage shared item embeddings within the User Tower. This approach, utilizing EmbeddingBag with shared weights, should provide immediate collaborative filtering capabilities by aligning user vectors with item latent spaces based on listening history.

Tool callrun_shell_commandTest fast history feature extraction for target users Returned

Tool call · Input 

{
  "description": "Test fast history feature extraction for target users",
  "command": "PYTHONPATH=/task python3 -c \"\nimport pyarrow.parquet as pq\nimport pandas as pd\nimport numpy as np\nimport time\n\nt0 = time.time()\nDATA = '/task/data'\ntargets_df = pd.read_parquet(f'{DATA}/target_users.parquet')\ntargets = set(targets_df.uid)\ncat_df = pd.read_parquet(f'{DATA}/candidate_catalog.parquet')\n# 1-based indexing: 1 to 30000, 0 is padding\nitem_to_idx = {item: idx + 1 for idx, item in enumerate(cat_df.item_id)}\nuids_list = sorted(list(targets))\nuid_to_idx = {uid: idx for idx, uid in enumerate(uids_list)}\n\nt_end = 24786800\n# Load last 90 days of listens\nt_90d = t_end - 90 * 86400\n\nprint(f'Starting data load at {time.time() - t0:.2f} s')\ntable = pq.read_table(f'{DATA}/train_listens.parquet', columns=['uid', 'timestamp', 'item_id', 'is_organic', 'played_ratio_pct'])\ndf = table.to_pandas()\ndf_target = df[(df.uid.isin(targets)) & (df.timestamp >= t_90d) & (df.item_id.isin(item_to_idx))].copy()\nprint(f'Target listens in 90d: {len(df_target):,}, elapsed: {time.time() - t0:.2f} s')\n\n# Map item_id to index 1..30000\ndf_target['item_idx'] = df_target.item_id.map(item_to_idx)\n# Sort by uid and timestamp\ndf_target = df_target.sort_values(['uid', 'timestamp'])\nprint(f'Sorted in {time.time() - t0:.2f} s')\n\""
}

Tool response 

<untrusted_context>
Output: Starting data load at 0.04 s
Target listens in 90d: 12,099,707, elapsed: 6.46 s
Sorted in 7.73 s
Process Group PGID: 2480
</untrusted_context>
51

Benchmark tail(100) for recent listens

+13:34
Recorded reasoning summary 54 words

Optimizing Data Extraction

I'm pleased to report the completion of a 90-day data window analysis for all target users, which concluded remarkably fast at just 7.73 seconds. My current focus is on refining the extraction of the last N items per user, a process I've found to be very efficient using pandas' groupby('uid').tail(N) functionality.

Tool callrun_shell_commandBenchmark tail(100) for recent listens Returned

Tool call · Input 

{
  "description": "Benchmark tail(100) for recent listens",
  "command": "PYTHONPATH=/task python3 -c \"\nimport pyarrow.parquet as pq\nimport pandas as pd\nimport numpy as np\nimport time\n\nt0 = time.time()\nDATA = '/task/data'\ntargets_df = pd.read_parquet(f'{DATA}/target_users.parquet')\ntargets = set(targets_df.uid)\ncat_df = pd.read_parquet(f'{DATA}/candidate_catalog.parquet')\nitem_to_idx = {item: idx + 1 for idx, item in enumerate(cat_df.item_id)}\n\nt_end = 24786800\nt_90d = t_end - 90 * 86400\n\ntable = pq.read_table(f'{DATA}/train_listens.parquet', columns=['uid', 'timestamp', 'item_id', 'is_organic', 'played_ratio_pct'])\ndf = table.to_pandas()\ndf_target = df[(df.uid.isin(targets)) & (df.timestamp >= t_90d) & (df.item_id.isin(item_to_idx))].copy()\ndf_target['item_idx'] = df_target.item_id.map(item_to_idx)\ndf_target = df_target.sort_values(['uid', 'timestamp'])\n\nt1 = time.time()\nrecent_100 = df_target.groupby('uid').tail(100)\nprint(f'tail(100) took {time.time() - t1:.2f} s, rows: {len(recent_100):,}')\n\""
}

Tool response 

<untrusted_context>
Output: tail(100) took 0.50 s, rows: 812,324
Process Group PGID: 2508
</untrusted_context>
52

Benchmark complete two-tower training step on CPU

+19:42
Recorded reasoning summary 43 words

Analyzing User Data Performance

I'm currently processing recent listen data for all 8,456 users, averaging around 96 items per user. My next step involves exploring efficient methods for generating training pairs, specifically focusing on how to effectively utilize backtest cutoffs for this process.

Recorded reasoning summary 44 words

Generating Training Pairs

I've been evaluating several cutoffs to construct training data, finding that using a 7-day window post-cutoff yields around 40-45k pairs per cutoff. I’m particularly pleased that this directly mirrors our evaluation window. Now I’m considering incorporating in-session transitions as auxiliary data.

Recorded reasoning summary 45 words

Testing End-to-End Training

I'm exploring end-to-end training for the Two-Tower model on our discovery pairs, aiming to learn item representations directly. Initial estimates suggest a single epoch could be incredibly fast, around 1.65 seconds with a batch size of 512. I'm eager to benchmark this.

Tool callrun_shell_commandBenchmark complete two-tower training step on CPU Returned

Tool call · Input 

{
  "command": "python3 -c \"\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nimport time\n\nclass ItemTower(nn.Module):\n    def __init__(self, num_items, emb_dim, num_features):\n        super().__init__()\n        self.item_emb = nn.Embedding(num_items + 1, emb_dim, padding_idx=0)\n        self.mlp = nn.Sequential(\n            nn.Linear(emb_dim + num_features, emb_dim),\n            nn.LayerNorm(emb_dim),\n            nn.GELU(),\n            nn.Linear(emb_dim, emb_dim)\n        )\n\n    def forward(self, item_indices, item_feat):\n        e = self.item_emb(item_indices)\n        x = torch.cat([e, item_feat], dim=-1)\n        v = self.mlp(x)\n        return F.normalize(v, p=2, dim=-1)\n\nclass UserTower(nn.Module):\n    def __init__(self, item_emb, emb_dim, num_user_features):\n        super().__init__()\n        self.item_emb = item_emb\n        self.mlp = nn.Sequential(\n            nn.Linear(emb_dim * 4 + num_user_features, 128),\n            nn.LayerNorm(128),\n            nn.GELU(),\n            nn.Dropout(0.1),\n            nn.Linear(128, emb_dim)\n        )\n\n    def forward(self, listens, listen_weights, org_listens, likes, dislikes, user_stats):\n        # listens: (B, L)\n        # We can use embedding lookup + weighted sum\n        e_listens = self.item_emb(listens) # (B, L, D)\n        w = listen_weights.unsqueeze(-1)\n        h_listens = (e_listens * w).sum(dim=1) / (w.sum(dim=1).clamp(min=1e-5))\n\n        e_org = self.item_emb(org_listens)\n        mask_org = (org_listens > 0).float().unsqueeze(-1)\n        h_org = (e_org * mask_org).sum(dim=1) / mask_org.sum(dim=1).clamp(min=1e-5)\n\n        e_likes = self.item_emb(likes)\n        mask_likes = (likes > 0).float().unsqueeze(-1)\n        h_likes = (e_likes * mask_likes).sum(dim=1) / mask_likes.sum(dim=1).clamp(min=1e-5)\n\n        e_dislikes = self.item_emb(dislikes)\n        mask_dislikes = (dislikes > 0).float().unsqueeze(-1)\n        h_dislikes = (e_dislikes * mask_dislikes).sum(dim=1) / mask_dislikes.sum(dim=1).clamp(min=1e-5)\n\n        h_combined = torch.cat([h_listens, h_org, h_likes, h_dislikes, user_stats], dim=-1)\n        u = self.mlp(h_combined)\n        return F.normalize(u, p=2, dim=-1)\n\nB = 512\nD = 64\nnum_items = 30000\n\nitem_tower = ItemTower(num_items, D, num_features=4)\nuser_tower = UserTower(item_tower.item_emb, D, num_user_features=4)\n\noptimizer = torch.optim.Adam(list(item_tower.parameters()) + list(user_tower.parameters()), lr=1e-3)\ntemp = 0.07\n\n# Synthetic batch\nlistens = torch.randint(0, num_items, (B, 50))\nlisten_weights = torch.rand(B, 50)\norg_listens = torch.randint(0, num_items, (B, 30))\nlikes = torch.randint(0, num_items, (B, 30))\ndislikes = torch.randint(0, num_items, (B, 10))\nuser_stats = torch.randn(B, 4)\n\ntarget_items = torch.randint(1, num_items, (B,))\ntarget_item_feat = torch.randn(B, 4)\ngrades = torch.tensor([1.0 if i % 5 != 0 else 3.0 for i in range(B)])\nweights = torch.tensor([1.0 if i % 5 != 0 else 5.0 for i in range(B)])\n\nt0 = time.time()\nfor step in range(50):\n    optimizer.zero_grad()\n    u = user_tower(listens, listen_weights, org_listens, likes, dislikes, user_stats)\n    v = item_tower(target_items, target_item_feat)\n\n    logits = torch.matmul(u, v.T) / temp\n    loss = (F.cross_entropy(logits, torch.arange(B), reduction='none') * weights).mean()\n    loss.backward()\n    optimizer.step()\nt1 = time.time()\n\nprint(f'50 steps of batch size {B} took {t1 - t0:.2f} s ({(t1 - t0)/50*1000:.2f} ms/step)')\n\"",
  "description": "Benchmark complete two-tower training step on CPU"
}

Tool response 

<untrusted_context>
Output: /opt/task-venv/lib/python3.12/site-packages/torch/_compile.py:53: UserWarning: optimizer contains a parameter group with duplicate parameters; in future, this will cause an error; see github.com/pytorch/pytorch/issues/40967 for more information
  return disable_fn(*args, **kwargs)
50 steps of batch size 512 took 1.07 s (21.47 ms/step)
Process Group PGID: 2566
</untrusted_context>
53

Test loading validation history data

+20:27
Recorded reasoning summary 47 words

Optimizing Throughput

My current focus is on maximizing processing speed, and I've achieved an impressive 21 milliseconds per step with a batch size of 512. I've also resolved an optimizer warning that arose from duplicate parameter references between the user and item towers, ensuring efficient gradient updates.

Recorded reasoning summary 39 words

Refining Performance Metrics

I've successfully refactored the model to cleanly handle parameter duplication. My next steps involve evaluating candidate scoring and generating rankings for assessment, while also delving into the intricacies of the loss function and negative sampling strategies.

Recorded reasoning summary 38 words

Investigating Sampled Softmax

I'm examining how the frequency of items in training batches impacts sampled softmax. Popular items, sampled more often as negatives, require a $\log(q_j)$ correction to the logit to accurately reflect their true relevance, preventing underestimation.

Recorded reasoning summary 37 words

Exploring Negative Sampling

I'm considering augmenting in-batch negatives with uniformly sampled negatives from the entire catalog. This approach ensures infrequent items receive adequate negative training, preventing overly optimistic scoring. Timing for this combined strategy appears very promising.

Recorded reasoning summary 43 words

Adjusting Training Dynamics

I'm now incorporating a learnable temperature parameter, $\tau$, within the $\log$-scale, inspired by modern two-tower architectures. Simultaneously, I'm exploring how to weight relevance grades within the loss function, ensuring higher-ranked items with greater relevance contribute more significantly to gradient updates.

Recorded reasoning summary 37 words

Integrating Item Features

I am now incorporating numerical features for candidate items, including log-transformed listens and popularity ranks, all standardized. These will be fed into an item MLP alongside embeddings to generate unit vectors, which are precomputable.

Recorded reasoning summary 43 words

Incorporating Temporal Dynamics

I'm now integrating temporal decay for listened items, applying a half-life of approximately 14 days. This is in conjunction with refining how recent organic listens and active likes/dislikes are aggregated to form user representations, alongside introducing log-transformed scalar user features.

Recorded reasoning summary 51 words

Refining User Representation

I'm now consolidating various user signals – listens, organic engagement, likes, and dislikes – into a unified representation. This representation is further augmented with standardized user statistics and then processed through a dedicated MLP to generate a normalized user vector, ready for cosine similarity scoring with item vectors.

Tool callrun_shell_commandTest loading validation history data Returned

Tool call · Input 

{
  "command": "PYTHONPATH=/task python3 -c \"\nimport pyarrow.parquet as pq\nimport pandas as pd\nimport numpy as np\nimport time\n\nt0 = time.time()\nDATA = '/task/data'\ntargets_df = pd.read_parquet(f'{DATA}/target_users.parquet')\ntargets = set(targets_df.uid)\ncat_df = pd.read_parquet(f'{DATA}/candidate_catalog.parquet')\nitem_to_idx = {item: idx + 1 for idx, item in enumerate(cat_df.item_id)}\nuids_list = sorted(list(targets))\nuid_to_row = {uid: r for r, uid in enumerate(uids_list)}\n\nt_cutoff = 24786800\nt_90d = t_cutoff - 90 * 86400\n\n# 1. Listens\ntable = pq.read_table(f'{DATA}/train_listens.parquet', columns=['uid', 'timestamp', 'item_id', 'is_organic', 'played_ratio_pct'])\ndf_listens = table.to_pandas()\ndf_listens = df_listens[(df_listens.uid.isin(targets)) & (df_listens.timestamp >= t_90d) & (df_listens.item_id.isin(item_to_idx))].copy()\ndf_listens['item_idx'] = df_listens.item_id.map(item_to_idx)\ndf_listens = df_listens.sort_values(['uid', 'timestamp'])\n\n# 2. Preference state at validation cutoff\npref_df = pd.read_parquet(f'{DATA}/validation_preference_state.parquet')\npref_likes = pref_df[pref_df.state == 'liked'].copy()\npref_dislikes = pref_df[pref_df.state == 'disliked'].copy()\npref_likes['item_idx'] = pref_likes.item_id.map(item_to_idx)\npref_dislikes['item_idx'] = pref_dislikes.item_id.map(item_to_idx)\n\nprint(f'Data prepared in {time.time() - t0:.2f} s')\nprint(f'Total target listens in 90d: {len(df_listens):,}')\nprint(f'Total active likes: {len(pref_likes):,}, dislikes: {len(pref_dislikes):,}')\n\"",
  "description": "Test loading validation history data"
}

Tool response 

<untrusted_context>
Output: Data prepared in 7.82 s
Total target listens in 90d: 12,099,707
Total active likes: 418,435, dislikes: 50,689
Process Group PGID: 2579
</untrusted_context>
54

Test fast numpy array construction for listens

+22:43
Recorded reasoning summary 36 words

Converting to Dense Tensors

I'm currently focused on transforming the processed data into dense tensors. The immediate goal is to create fixed-width tensors representing user listening history and associated weights, aiming for efficiency in subsequent operations.

Tool callrun_shell_commandTest fast numpy array construction for listens Returned

Tool call · Input 

{
  "command": "PYTHONPATH=/task python3 -c \"\nimport pyarrow.parquet as pq\nimport pandas as pd\nimport numpy as np\nimport time\n\nDATA = '/task/data'\ntargets_df = pd.read_parquet(f'{DATA}/target_users.parquet')\ntargets = sorted(list(set(targets_df.uid)))\nnum_users = len(targets)\nuid_to_row = {uid: r for r, uid in enumerate(targets)}\n\ncat_df = pd.read_parquet(f'{DATA}/candidate_catalog.parquet')\nitem_to_idx = {item: idx + 1 for idx, item in enumerate(cat_df.item_id)}\n\nt_cutoff = 24786800\nt_90d = t_cutoff - 90 * 86400\n\ntable = pq.read_table(f'{DATA}/train_listens.parquet', columns=['uid', 'timestamp', 'item_id', 'is_organic', 'played_ratio_pct'])\ndf_listens = table.to_pandas()\ndf_listens = df_listens[(df_listens.uid.isin(set(targets))) & (df_listens.timestamp >= t_90d) & (df_listens.item_id.isin(item_to_idx))].copy()\ndf_listens['item_idx'] = df_listens.item_id.map(item_to_idx)\ndf_listens['row'] = df_listens.uid.map(uid_to_row)\ndf_listens = df_listens.sort_values(['uid', 'timestamp'])\n\n# Compute weights\ndelta_days = (t_cutoff - df_listens['timestamp'].to_numpy()) / 86400.0\nrecency = np.exp(-0.05 * delta_days)\ncompletion = np.clip(df_listens['played_ratio_pct'].to_numpy() / 100.0, 0.1, 1.0)\norganic = np.where(df_listens['is_organic'].to_numpy() == 1, 1.5, 1.0)\ndf_listens['weight'] = completion * recency * organic\n\nt0 = time.time()\n# Take last 100 listens per user\ntail_100 = df_listens.groupby('row').tail(100)\n# Group and build dense array\nmax_L = 100\nlistens_arr = np.zeros((num_users, max_L), dtype=np.int64)\nweights_arr = np.zeros((num_users, max_L), dtype=np.float32)\n\nfor r, group in tail_100.groupby('row'):\n    n = len(group)\n    listens_arr[r, :n] = group['item_idx'].to_numpy()\n    weights_arr[r, :n] = group['weight'].to_numpy()\n\nt1 = time.time()\nprint(f'Building listens tensors took {t1 - t0:.2f} s')\nprint(f'Non-zero rows: {(listens_arr[:, 0] > 0).sum()} / {num_users}')\n\"",
  "description": "Test fast numpy array construction for listens"
}

Tool response 

<untrusted_context>
Output: Building listens tensors took 0.96 s
Non-zero rows: 8448 / 8456
Process Group PGID: 2613
</untrusted_context>
55

Test fast numpy array construction for preference state

+23:00
Recorded reasoning summary 67 words

Analyzing User Data

My analysis of user listening data is progressing swiftly; the creation of listen tensors was remarkably fast at just 0.96 seconds. I've identified that a significant majority, 8,448 out of 8,456 users, have engaged with content in the last 90 days. I'm now investigating the sparse participation of the remaining eight users, considering their preference states or historical listen activity beyond the 90-day window.

Tool callrun_shell_commandTest fast numpy array construction for preference state Returned

Tool call · Input 

{
  "command": "PYTHONPATH=/task python3 -c \"\nimport pandas as pd\nimport numpy as np\nimport time\n\nDATA = '/task/data'\ntargets_df = pd.read_parquet(f'{DATA}/target_users.parquet')\ntargets = sorted(list(set(targets_df.uid)))\nnum_users = len(targets)\nuid_to_row = {uid: r for r, uid in enumerate(targets)}\n\ncat_df = pd.read_parquet(f'{DATA}/candidate_catalog.parquet')\nitem_to_idx = {item: idx + 1 for idx, item in enumerate(cat_df.item_id)}\n\npref_df = pd.read_parquet(f'{DATA}/validation_preference_state.parquet')\npref_likes = pref_df[pref_df.state == 'liked'].copy()\npref_dislikes = pref_df[pref_df.state == 'disliked'].copy()\npref_likes['item_idx'] = pref_likes.item_id.map(item_to_idx)\npref_dislikes['item_idx'] = pref_dislikes.item_id.map(item_to_idx)\npref_likes['row'] = pref_likes.uid.map(uid_to_row)\npref_dislikes['row'] = pref_dislikes.uid.map(uid_to_row)\n\nt0 = time.time()\nlikes_arr = np.zeros((num_users, 50), dtype=np.int64)\nfor r, group in pref_likes.groupby('row'):\n    n = min(50, len(group))\n    likes_arr[r, :n] = group['item_idx'].to_numpy()[:n]\n\ndislikes_arr = np.zeros((num_users, 20), dtype=np.int64)\nfor r, group in pref_dislikes.groupby('row'):\n    n = min(20, len(group))\n    dislikes_arr[r, :n] = group['item_idx'].to_numpy()[:n]\n\nprint(f'Building pref tensors took {time.time() - t0:.2f} s')\nprint(f'Likes non-zero: {(likes_arr[:, 0] > 0).sum()} / {num_users}')\nprint(f'Dislikes non-zero: {(dislikes_arr[:, 0] > 0).sum()} / {num_users}')\n\"",
  "description": "Test fast numpy array construction for preference state"
}

Tool response 

<untrusted_context>
Output: Building pref tensors took 0.37 s
Likes non-zero: 6769 / 8456
Dislikes non-zero: 4277 / 8456
Process Group PGID: 2641
</untrusted_context>
56

Count discovery training pairs across 4 backtest cutoffs

+23:18
Recorded reasoning summary 44 words

Exploring Training Set Construction

I'm currently focusing on how to build the training set, specifically by defining backtest cutoffs that precede the designated training end date, $T_{\text{train\_end}}$. My immediate next step is to analyze the implications of setting the first cutoff, $T_1$, at $T_{\text{train\_end}}$.

Tool callrun_shell_commandCount discovery training pairs across 4 backtest cutoffs Returned

Tool call · Input 

{
  "command": "PYTHONPATH=/task python3 -c \"\nimport pyarrow.parquet as pq\nimport pandas as pd\nimport numpy as np\nimport time\n\nDATA = '/task/data'\ntargets_df = pd.read_parquet(f'{DATA}/target_users.parquet')\ntargets = set(targets_df.uid)\ncat_df = pd.read_parquet(f'{DATA}/candidate_catalog.parquet')\ncat = set(cat_df.item_id)\nitem_to_idx = {item: idx + 1 for idx, item in enumerate(cat_df.item_id)}\n\nt0 = time.time()\ntable = pq.read_table(f'{DATA}/train_listens.parquet', columns=['uid', 'timestamp', 'item_id', 'is_organic', 'played_ratio_pct'])\ndf_all = table.to_pandas()\ndf_targets = df_all[df_all.uid.isin(targets) & df_all.item_id.isin(cat)].copy()\ndf_targets['item_idx'] = df_targets.item_id.map(item_to_idx)\ndf_targets = df_targets.sort_values(['uid', 'timestamp'])\n\nlikes_df = pd.read_parquet(f'{DATA}/train_likes.parquet')\ndf_likes = likes_df[likes_df.uid.isin(targets) & likes_df.item_id.isin(cat)].copy()\ndf_likes['item_idx'] = df_likes.item_id.map(item_to_idx)\n\nt_end = 24786800\nweek = 7 * 86400\n\nfor w in range(1, 5):\n    t_c = t_end - w * week\n    t_next = t_c + week\n    # Future window: [t_c, t_next]\n    # Positive listens: is_organic == 1, played_ratio_pct >= 80\n    fut_listens = df_targets[(df_targets.timestamp >= t_c) & (df_targets.timestamp < t_next) & (df_targets.is_organic == 1) & (df_targets.played_ratio_pct >= 80)]\n    fut_likes = df_likes[(df_likes.timestamp >= t_c) & (df_likes.timestamp < t_next) & (df_likes.is_organic == 1)]\n\n    # Past items before t_c for target users\n    past_items = df_targets[df_targets.timestamp < t_c]\n    past_pairs = set(zip(past_items.uid, past_items.item_idx))\n\n    # Discovery positive pairs\n    fut_listens_pairs = set(zip(fut_listens.uid, fut_listens.item_idx)) - past_pairs\n    fut_likes_pairs = set(zip(fut_likes.uid, fut_likes.item_idx)) - past_pairs\n\n    print(f'Cutoff w={w} (T-{w}w): {len(fut_listens_pairs):,} discovery listens (gr 1), {len(fut_likes_pairs):,} discovery likes (gr 3)')\n\"",
  "description": "Count discovery training pairs across 4 backtest cutoffs"
}

Tool response 

<untrusted_context>
Output: Cutoff w=1 (T-1w): 39,377 discovery listens (gr 1), 5,330 discovery likes (gr 3)
Cutoff w=2 (T-2w): 38,624 discovery listens (gr 1), 4,680 discovery likes (gr 3)
Cutoff w=3 (T-3w): 38,012 discovery listens (gr 1), 5,152 discovery likes (gr 3)
Cutoff w=4 (T-4w): 35,863 discovery listens (gr 1), 5,177 discovery likes (gr 3)
Process Group PGID: 2658
</untrusted_context>
57

Test metadata loading script

+24:35
Recorded reasoning summary 37 words

Analyzing Discovery Metrics

I've been crunching the numbers from our recent cutoffs. Each window yields about 38,000 discovery listens and 5,000 likes. Over four such periods, this accumulates to roughly 152,000 listens and 20,000 likes in discovery.

Recorded reasoning summary 39 words

Refining Training Data

I'm refining the training data by incorporating nuances like items appearing in both listens and likes, and exploring historical preference states. I'm also considering item co-occurrence pretraining and evaluating direct end-to-end training on recent discovery instances.

Recorded reasoning summary 48 words

Testing Two-Tower Model

I'm preparing to run an end-to-end Two-Tower model on discovery backtest instances. My next step is to evaluate the validation NDCG@10 using evaluate_public.py and compare this against our 0.01071 baseline. I've outlined the prototype structure and the components for both the ItemTower and UserTower modules.

Recorded reasoning summary 48 words

Developing User Embeddings

I'm now focusing on how to generate robust user embeddings. This involves aggregating weighted means from listens, likes, and dislikes, alongside other user statistics, before passing them through a multi-layer perceptron. The goal is a final embedding ready for scoring and retrieval via inner product.

Tool callrun_shell_commandTest metadata loading script Returned

Tool call · Input 

{
  "command": "cat << 'EOF' > /app/solution/proto_two_tower.py\nimport sys\nimport time\nfrom pathlib import Path\nimport numpy as np\nimport pandas as pd\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\nsys.path.insert(0, '/task')\nfrom ranking_evaluation import CandidateSet, ranked_items, validate_ranking, score_rankings\nfrom evaluate_public import validation_truth\n\nDATA = Path('/task/data')\n\ndef load_data():\n    t0 = time.time()\n    targets_df = pd.read_parquet(DATA / 'target_users.parquet')\n    target_uids = sorted(list(set(targets_df.uid)))\n    uid_to_target_idx = {uid: idx for idx, uid in enumerate(target_uids)}\n\n    cat_df = pd.read_parquet(DATA / 'candidate_catalog.parquet')\n    item_ids = cat_df.item_id.to_numpy()\n    num_items = len(item_ids)\n    # 1-based indexing for catalog items: 1 .. 30000. 0 is padding.\n    item_to_idx = {item: idx + 1 for idx, item in enumerate(item_ids)}\n\n    # Item numerical features\n    pos = np.log1p(cat_df.positive_listens.to_numpy().astype(np.float32))\n    org_pos = np.log1p(cat_df.organic_positive_listens.to_numpy().astype(np.float32))\n    org_ratio = (cat_df.organic_positive_listens / (cat_df.positive_listens + 1)).to_numpy().astype(np.float32)\n    pop_rank = (cat_df.popularity_rank / 30000.0).to_numpy().astype(np.float32)\n\n    item_feats = np.stack([pos, org_pos, org_ratio, pop_rank], axis=1)\n    # Normalize features\n    item_feats = (item_feats - item_feats.mean(axis=0)) / (item_feats.std(axis=0) + 1e-6)\n    # Pad index 0 with 0\n    item_feats = np.pad(item_feats, ((1, 0), (0, 0)), mode='constant')\n\n    # Item sampling probabilities for logQ correction (based on positive listens)\n    item_freqs = cat_df.positive_listens.to_numpy().astype(np.float32)\n    item_probs = item_freqs / item_freqs.sum()\n    log_q = np.pad(np.log(item_probs + 1e-10), (1, 0), mode='constant')\n\n    print(f\"Loaded metadata in {time.time() - t0:.2f} s\")\n    return target_uids, uid_to_target_idx, item_ids, item_to_idx, item_feats, log_q\n\nif __name__ == '__main__':\n    load_data()\nEOF\npython3 /app/solution/proto_two_tower.py\n",
  "description": "Test metadata loading script"
}

Tool response 

<untrusted_context>
Output: Loaded metadata in 0.04 s
Process Group PGID: 2686
</untrusted_context>
58

Test data loading and filtering speed

+24:47
Recorded reasoning summary 36 words

Developing Feature Extraction

I'm currently focused on building the feature extraction functions. Specifically, I'm defining user history features up to a given cutoff $T_c$, including the count of listens within the last 100 for active users.

Tool callrun_shell_commandTest data loading and filtering speed Returned

Tool call · Input 

{
  "description": "Test data loading and filtering speed",
  "command": "cat << 'EOF' > /app/solution/test_extract.py\nimport sys\nimport time\nfrom pathlib import Path\nimport numpy as np\nimport pandas as pd\nimport pyarrow.parquet as pq\n\nDATA = Path('/task/data')\n\ntargets_df = pd.read_parquet(DATA / 'target_users.parquet')\ntarget_uids = sorted(list(set(targets_df.uid)))\nuid_to_target_idx = {uid: idx for idx, uid in enumerate(target_uids)}\n\ncat_df = pd.read_parquet(DATA / 'candidate_catalog.parquet')\nitem_to_idx = {item: idx + 1 for idx, item in enumerate(cat_df.item_id)}\n\nt0 = time.time()\nprint(\"Reading listens...\")\ntable = pq.read_table(DATA / 'train_listens.parquet', columns=['uid', 'timestamp', 'item_id', 'is_organic', 'played_ratio_pct'])\ndf_listens = table.to_pandas()\ndf_listens = df_listens[df_listens.uid.isin(set(target_uids)) & df_listens.item_id.isin(item_to_idx)].copy()\ndf_listens['item_idx'] = df_listens.item_id.map(item_to_idx)\ndf_listens = df_listens.sort_values(['uid', 'timestamp'])\nprint(f\"Listens filtered: {len(df_listens):,} in {time.time() - t0:.2f} s\")\n\nt1 = time.time()\nlikes_df = pd.read_parquet(DATA / 'train_likes.parquet')\nunlikes_df = pd.read_parquet(DATA / 'train_unlikes.parquet')\ndislikes_df = pd.read_parquet(DATA / 'train_dislikes.parquet')\nundislikes_df = pd.read_parquet(DATA / 'train_undislikes.parquet')\n\nlikes_df['event'] = 1\nunlikes_df['event'] = 0\nall_likes = pd.concat([likes_df[['uid', 'timestamp', 'item_id', 'event']], unlikes_df[['uid', 'timestamp', 'item_id', 'event']]])\nall_likes = all_likes[all_likes.uid.isin(set(target_uids)) & all_likes.item_id.isin(item_to_idx)].copy()\nall_likes['item_idx'] = all_likes.item_id.map(item_to_idx)\n\ndislikes_df['event'] = 1\nundislikes_df['event'] = 0\nall_dislikes = pd.concat([dislikes_df[['uid', 'timestamp', 'item_id', 'event']], undislikes_df[['uid', 'timestamp', 'item_id', 'event']]])\nall_dislikes = all_dislikes[all_dislikes.uid.isin(set(target_uids)) & all_dislikes.item_id.isin(item_to_idx)].copy()\nall_dislikes['item_idx'] = all_dislikes.item_id.map(item_to_idx)\n\nprint(f\"Likes/dislikes loaded in {time.time() - t1:.2f} s\")\nEOF\npython3 /app/solution/test_extract.py\n"
}

Tool response 

<untrusted_context>
Output: Reading listens...
Listens filtered: 31,062,540 in 10.71 s
Likes/dislikes loaded in 0.28 s
Process Group PGID: 2710
</untrusted_context>
59

Test python filtering speed for discovery candidates

+27:08
Recorded reasoning summary 55 words

Analyzing User History Extraction

I'm currently focused on optimizing the speed of extracting user history tensors, specifically examining how efficiently I can filter df_listens using a cutoff timestamp $T_c$. My current approach involves a direct boolean indexing, and I'm assessing its performance implications, particularly given the sorted nature of df_listens by user ID and timestamp.

Recorded reasoning summary 52 words

Refining Windowed Data Extraction

I've been exploring time windowing for historical data. Limiting listens to 90 days prior to $T_c$ yields around 11-12 million rows, and grouping by user and taking the last 100 is remarkably fast at 0.5 seconds. Similar efficiency is observed for active likes and dislikes within similar windows.

Tool callrun_shell_commandTest python filtering speed for discovery candidates Returned

Tool call · Input 

{
  "description": "Test python filtering speed for discovery candidates",
  "command": "python3 -c \"\nimport time\nimport pandas as pd\nimport numpy as np\n\n# Let's test set-based discovery filtering speed\nnum_uids = 8456\npast_dict = {i: set(np.random.randint(1, 30000, 200)) for i in range(num_uids)}\nfut_uids = np.random.randint(0, num_uids, 50000)\nfut_items = np.random.randint(1, 30000, 50000)\n\nt0 = time.time()\nkeep = [item not in past_dict.get(uid, set()) for uid, item in zip(fut_uids, fut_items)]\nt1 = time.time()\nprint(f'Filtering 50,000 future items took {(t1 - t0)*1000:.2f} ms')\n\""
}

Tool response 

<untrusted_context>
Output: Filtering 50,000 future items took 26.86 ms
Process Group PGID: 2745
</untrusted_context>
60

Define TwoTowerModel architecture

+27:49
Recorded reasoning summary 38 words

Optimizing Filtering Performance

I've successfully reduced filtering time for 50,000 items to just 26ms. My current focus involves refining the extraction process at cutoff $T_c$, specifically retrieving user feature tensors, which include listening data, weights, and engagement metrics.

Recorded reasoning summary 45 words

Refining User Embeddings

I'm currently investigating ways to optimize user history feature extraction, aiming to speed up the retrieval of user feature tensors. My focus is on how to efficiently compute user embeddings and potentially precompute parts of them to avoid redundant calculations during backpropagation.

Recorded reasoning summary 44 words

Exploring Gradient Flow Dynamics

I'm analyzing how gradients flowing back into shared item embeddings lead to their clustering by genre and style. Simultaneously, I'm detailing the input features for the UserTower, including listens, listen_weights, and org_listens, to understand their contribution to user embedding generation.

Recorded reasoning summary 36 words

Detailing User Input Features

I'm now precisely defining the input features for the UserTower. This includes refining the handling of user_stats scalars and ensuring robustness for arrays like dislikes when a user has no recorded dislikes.

Recorded reasoning summary 38 words

Defining User Input Features

I'm now precisely defining the input features for the UserTower, handling scalar user_stats and array features like dislikes robustly. For User MLP, inputs are concatenated $h_{\text{listens}}, h_{\text{org}}, h_{\text{likes}}, h_{\text{dislikes}}$, and user_stats, followed by normalization.

Recorded reasoning summary 47 words

Precomputing Candidate Item Embeddings

I've realized I can precompute all candidate item embeddings offline and store them. This will allow for faster score computation at request time by simply performing a matrix multiplication between user embeddings and the precomputed item embeddings, followed by masking and top-k selection.

Tool callrun_shell_commandDefine TwoTowerModel architecture Returned

Tool call · Input 

{
  "command": "cat << 'EOF' > /app/solution/train_prototype.py\nimport sys\nimport time\nfrom pathlib import Path\nimport numpy as np\nimport pandas as pd\nimport pyarrow.parquet as pq\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\nsys.path.insert(0, '/task')\nfrom ranking_evaluation import CandidateSet, ranked_items, validate_ranking, score_rankings\nfrom evaluate_public import validation_truth\n\nDATA = Path('/task/data')\n\ndef build_item_metadata():\n    cat_df = pd.read_parquet(DATA / 'candidate_catalog.parquet')\n    item_ids = cat_df.item_id.to_numpy()\n    num_items = len(item_ids)\n    item_to_idx = {item: idx + 1 for idx, item in enumerate(item_ids)}\n\n    pos = np.log1p(cat_df.positive_listens.to_numpy().astype(np.float32))\n    org_pos = np.log1p(cat_df.organic_positive_listens.to_numpy().astype(np.float32))\n    org_ratio = (cat_df.organic_positive_listens / (cat_df.positive_listens + 1)).to_numpy().astype(np.float32)\n    pop_rank = (cat_df.popularity_rank / 30000.0).to_numpy().astype(np.float32)\n\n    item_feats = np.stack([pos, org_pos, org_ratio, pop_rank], axis=1)\n    item_feats = (item_feats - item_feats.mean(axis=0)) / (item_feats.std(axis=0) + 1e-6)\n    # Pad index 0 with zeros\n    item_feats = np.pad(item_feats, ((1, 0), (0, 0)), mode='constant')\n\n    item_freqs = cat_df.positive_listens.to_numpy().astype(np.float32)\n    item_probs = item_freqs / item_freqs.sum()\n    log_q = np.pad(np.log(item_probs + 1e-10), (1, 0), mode='constant')\n\n    return item_ids, item_to_idx, item_feats, log_q\n\nclass ItemTower(nn.Module):\n    def __init__(self, item_emb, emb_dim, num_features):\n        super().__init__()\n        self.item_emb = item_emb\n        self.mlp = nn.Sequential(\n            nn.Linear(emb_dim + num_features, emb_dim),\n            nn.LayerNorm(emb_dim),\n            nn.GELU(),\n            nn.Linear(emb_dim, emb_dim),\n            nn.LayerNorm(emb_dim)\n        )\n\n    def forward(self, item_indices, item_features):\n        e = self.item_emb(item_indices)\n        x = torch.cat([e, item_features], dim=-1)\n        v = self.mlp(x)\n        return F.normalize(v, p=2, dim=-1)\n\nclass UserTower(nn.Module):\n    def __init__(self, item_emb, emb_dim, num_user_features):\n        super().__init__()\n        self.item_emb = item_emb\n        self.mlp = nn.Sequential(\n            nn.Linear(emb_dim * 4 + num_user_features, emb_dim * 2),\n            nn.LayerNorm(emb_dim * 2),\n            nn.GELU(),\n            nn.Dropout(0.1),\n            nn.Linear(emb_dim * 2, emb_dim),\n            nn.LayerNorm(emb_dim)\n        )\n\n    def forward(self, listens, listen_weights, org_listens, likes, dislikes, user_stats):\n        # listens: (B, L_listens)\n        e_listens = self.item_emb(listens)\n        w = listen_weights.unsqueeze(-1)\n        sum_w = w.sum(dim=1).clamp(min=1e-5)\n        h_listens = (e_listens * w).sum(dim=1) / sum_w\n\n        e_org = self.item_emb(org_listens)\n        mask_org = (org_listens > 0).float().unsqueeze(-1)\n        sum_org = mask_org.sum(dim=1)\n        h_org = torch.where(sum_org > 0, (e_org * mask_org).sum(dim=1) / sum_org.clamp(min=1e-5), torch.zeros_like(h_listens))\n\n        e_likes = self.item_emb(likes)\n        mask_likes = (likes > 0).float().unsqueeze(-1)\n        sum_likes = mask_likes.sum(dim=1)\n        h_likes = torch.where(sum_likes > 0, (e_likes * mask_likes).sum(dim=1) / sum_likes.clamp(min=1e-5), torch.zeros_like(h_listens))\n\n        e_dislikes = self.item_emb(dislikes)\n        mask_dislikes = (dislikes > 0).float().unsqueeze(-1)\n        sum_dislikes = mask_dislikes.sum(dim=1)\n        h_dislikes = torch.where(sum_dislikes > 0, (e_dislikes * mask_dislikes).sum(dim=1) / sum_dislikes.clamp(min=1e-5), torch.zeros_like(h_listens))\n\n        h_combined = torch.cat([h_listens, h_org, h_likes, h_dislikes, user_stats], dim=-1)\n        u = self.mlp(h_combined)\n        return F.normalize(u, p=2, dim=-1)\n\nclass TwoTowerModel(nn.Module):\n    def __init__(self, num_items, emb_dim=64, num_item_features=4, num_user_features=5):\n        super().__init__()\n        self.item_emb = nn.Embedding(num_items + 1, emb_dim, padding_idx=0)\n        # Initialize embeddings with small std\n        nn.init.normal_(self.item_emb.weight, std=0.02)\n        with torch.no_grad():\n            self.item_emb.weight[0].fill_(0)\n\n        self.item_tower = ItemTower(self.item_emb, emb_dim, num_item_features)\n        self.user_tower = UserTower(self.item_emb, emb_dim, num_user_features)\n        self.logit_scale = nn.Parameter(torch.ones([]) * np.log(1 / 0.07))\n\n    def get_user_embedding(self, listens, listen_weights, org_listens, likes, dislikes, user_stats):\n        return self.user_tower(listens, listen_weights, org_listens, likes, dislikes, user_stats)\n\n    def get_item_embedding(self, item_indices, item_features):\n        return self.item_tower(item_indices, item_features)\n\nprint(\"Architecture defined successfully.\")\nEOF\npython3 /app/solution/train_prototype.py\n",
  "description": "Define TwoTowerModel architecture"
}

Tool response 

<untrusted_context>
Output: Architecture defined successfully.
Process Group PGID: 2756
</untrusted_context>
61

Define and test fast user feature extraction module

+28:09
Recorded reasoning summary 52 words

Completing the Training Script

I'm currently focused on refining /app/solution/train_prototype.py. My immediate goals are to implement efficient extraction of user history features, construct training samples using temporal backtest windows, and then train the TwoTowerModel, ensuring thorough evaluation against the validation set. I'm beginning with testing the training sample and user history extraction.

Recorded reasoning summary 54 words

Designing User Features

My focus has shifted to designing a modular and efficient helper function for user feature extraction. This function will handle filtering listening history and calculating weighted features based on recency and completion ratios, making it reusable for training and validation sets. I'm currently fleshing out the logic for this core component.

Tool callrun_shell_commandDefine and test fast user feature extraction module Returned

Tool call · Input 

{
  "command": "cat << 'EOF' > /app/solution/feature_extractor.py\nimport time\nimport numpy as np\nimport pandas as pd\nimport torch\n\ndef extract_user_features(\n    df_listens,\n    df_likes,\n    df_dislikes,\n    target_uids,\n    cutoff_timestamp,\n    history_days=90,\n    max_listens=100,\n    max_org=50,\n    max_likes=50,\n    max_dislikes=20\n):\n    t0 = time.time()\n    num_users = len(target_uids)\n    uid_to_row = {uid: r for r, uid in enumerate(target_uids)}\n\n    # 1. Listens within history window\n    t_min = cutoff_timestamp - history_days * 86400\n    sub = df_listens[(df_listens.timestamp >= t_min) & (df_listens.timestamp < cutoff_timestamp)].copy()\n\n    # Pre-allocate feature arrays\n    listens_arr = np.zeros((num_users, max_listens), dtype=np.int64)\n    weights_arr = np.zeros((num_users, max_listens), dtype=np.float32)\n    org_arr = np.zeros((num_users, max_org), dtype=np.int64)\n    likes_arr = np.zeros((num_users, max_likes), dtype=np.int64)\n    dislikes_arr = np.zeros((num_users, max_dislikes), dtype=np.int64)\n    stats_arr = np.zeros((num_users, 5), dtype=np.float32)\n\n    if len(sub) > 0:\n        delta_days = (cutoff_timestamp - sub['timestamp'].to_numpy(dtype=np.float32)) / 86400.0\n        recency = np.exp(-0.05 * delta_days)\n        completion = np.clip(sub['played_ratio_pct'].to_numpy(dtype=np.float32) / 100.0, 0.1, 1.0)\n        organic = np.where(sub['is_organic'].to_numpy() == 1, 1.5, 1.0).astype(np.float32)\n        sub['weight'] = completion * recency * organic\n        sub['row'] = sub['uid'].map(uid_to_row)\n        sub_valid = sub.dropna(subset=['row'])\n        sub_valid['row'] = sub_valid['row'].astype(int)\n\n        # Recent listens\n        tail_listens = sub_valid.groupby('row').tail(max_listens)\n        for r, group in tail_listens.groupby('row'):\n            n = len(group)\n            listens_arr[r, :n] = group['item_idx'].to_numpy()\n            weights_arr[r, :n] = group['weight'].to_numpy()\n\n        # Recent organic strong listens\n        org_sub = sub_valid[(sub_valid.is_organic == 1) & (sub_valid.played_ratio_pct >= 80)]\n        tail_org = org_sub.groupby('row').tail(max_org)\n        for r, group in tail_org.groupby('row'):\n            n = len(group)\n            org_arr[r, :n] = group['item_idx'].to_numpy()\n\n        # Compute summary stats\n        grouped = sub_valid.groupby('row')\n        tot_listens = grouped.size()\n        stats_arr[tot_listens.index, 0] = np.log1p(tot_listens.to_numpy(dtype=np.float32))\n        if len(org_sub) > 0:\n            tot_org = org_sub.groupby('row').size()\n            stats_arr[tot_org.index, 1] = np.log1p(tot_org.to_numpy(dtype=np.float32))\n        mean_comp = grouped['played_ratio_pct'].mean() / 100.0\n        stats_arr[mean_comp.index, 4] = mean_comp.to_numpy(dtype=np.float32)\n\n    # 2. Preference state (likes / dislikes)\n    if df_likes is not None and len(df_likes) > 0:\n        sub_likes = df_likes[df_likes.timestamp < cutoff_timestamp].copy()\n        if len(sub_likes) > 0:\n            sub_likes['row'] = sub_likes['uid'].map(uid_to_row)\n            sub_likes = sub_likes.dropna(subset=['row'])\n            sub_likes['row'] = sub_likes['row'].astype(int)\n            tail_likes = sub_likes.groupby('row').tail(max_likes)\n            for r, group in tail_likes.groupby('row'):\n                n = len(group)\n                likes_arr[r, :n] = group['item_idx'].to_numpy()\n            tot_likes = sub_likes.groupby('row').size()\n            stats_arr[tot_likes.index, 2] = np.log1p(tot_likes.to_numpy(dtype=np.float32))\n\n    if df_dislikes is not None and len(df_dislikes) > 0:\n        sub_dis = df_dislikes[df_dislikes.timestamp < cutoff_timestamp].copy()\n        if len(sub_dis) > 0:\n            sub_dis['row'] = sub_dis['uid'].map(uid_to_row)\n            sub_dis = sub_dis.dropna(subset=['row'])\n            sub_dis['row'] = sub_dis['row'].astype(int)\n            tail_dis = sub_dis.groupby('row').tail(max_dislikes)\n            for r, group in tail_dis.groupby('row'):\n                n = len(group)\n                dislikes_arr[r, :n] = group['item_idx'].to_numpy()\n            tot_dis = sub_dis.groupby('row').size()\n            stats_arr[tot_dis.index, 3] = np.log1p(tot_dis.to_numpy(dtype=np.float32))\n\n    # Standardize stats_arr\n    mean = stats_arr.mean(axis=0, keepdims=True)\n    std = stats_arr.std(axis=0, keepdims=True) + 1e-6\n    stats_arr = (stats_arr - mean) / std\n\n    res = {\n        'listens': torch.from_numpy(listens_arr),\n        'listen_weights': torch.from_numpy(weights_arr),\n        'org_listens': torch.from_numpy(org_arr),\n        'likes': torch.from_numpy(likes_arr),\n        'dislikes': torch.from_numpy(dislikes_arr),\n        'user_stats': torch.from_numpy(stats_arr),\n    }\n    print(f\"Extracted features for {num_users} users in {time.time() - t0:.2f} s\")\n    return res\n\nprint(\"feature_extractor.py ready.\")\nEOF\npython3 /app/solution/feature_extractor.py\n",
  "description": "Define and test fast user feature extraction module"
}

Tool response 

<untrusted_context>
Output: feature_extractor.py ready.
Process Group PGID: 2774
</untrusted_context>
62

Test feature extraction on actual dataset

+28:23
Tool callrun_shell_commandTest feature extraction on actual dataset Returned

Tool call · Input 

{
  "command": "python3 -c \"\nimport sys\nsys.path.insert(0, '/app/solution')\nfrom feature_extractor import extract_user_features\nimport pandas as pd\nimport pyarrow.parquet as pq\n\nDATA = '/task/data'\ntargets_df = pd.read_parquet(f'{DATA}/target_users.parquet')\ntarget_uids = sorted(list(set(targets_df.uid)))\n\ncat_df = pd.read_parquet(f'{DATA}/candidate_catalog.parquet')\nitem_to_idx = {item: idx + 1 for idx, item in enumerate(cat_df.item_id)}\n\ntable = pq.read_table(f'{DATA}/train_listens.parquet', columns=['uid', 'timestamp', 'item_id', 'is_organic', 'played_ratio_pct'])\ndf_listens = table.to_pandas()\ndf_listens = df_listens[df_listens.uid.isin(set(target_uids)) & df_listens.item_id.isin(item_to_idx)].copy()\ndf_listens['item_idx'] = df_listens.item_id.map(item_to_idx)\ndf_listens = df_listens.sort_values(['uid', 'timestamp'])\n\nlikes_df = pd.read_parquet(f'{DATA}/train_likes.parquet')\nlikes_df = likes_df[likes_df.uid.isin(set(target_uids)) & likes_df.item_id.isin(item_to_idx)].copy()\nlikes_df['item_idx'] = likes_df.item_id.map(item_to_idx)\n\ndislikes_df = pd.read_parquet(f'{DATA}/train_dislikes.parquet')\ndislikes_df = dislikes_df[dislikes_df.uid.isin(set(target_uids)) & dislikes_df.item_id.isin(item_to_idx)].copy()\ndislikes_df['item_idx'] = dislikes_df.item_id.map(item_to_idx)\n\nt_cutoff = 24786800\nfeats = extract_user_features(df_listens, likes_df, dislikes_df, target_uids, t_cutoff)\nfor k, v in feats.items():\n    print(k, v.shape, v.dtype)\n\"",
  "description": "Test feature extraction on actual dataset"
}

Tool response 

<untrusted_context>
Output: feature_extractor.py ready.
Extracted features for 8456 users in 2.82 s
listens torch.Size([8456, 100]) torch.int64
listen_weights torch.Size([8456, 100]) torch.float32
org_listens torch.Size([8456, 50]) torch.int64
likes torch.Size([8456, 50]) torch.int64
dislikes torch.Size([8456, 20]) torch.int64
user_stats torch.Size([8456, 5]) torch.float32
Process Group PGID: 2786
</untrusted_context>
63

Test reconstruction of active preferences at multiple cutoffs

+28:51
Recorded reasoning summary 38 words

Processing User Features

I've successfully extracted features for all 8,456 users in a mere 2.82 seconds. My current focus is on integrating the active preference state from validation_preference_state.parquet into the extract_user_features function, utilizing the likes_df and dislikes_df dataframes.

Recorded reasoning summary 52 words

Reconstructing Historical States

I'm refining my approach to historical backtesting. I've confirmed that reconstructing the active preference state using train_likes and train_dislikes up to a specific cutoff timestamp is an exact and correct method, aligning perfectly with the validation data's active_likes and active_dislikes. Now, I'm implementing the get_active_preferences function to test this.

Tool callrun_shell_commandTest reconstruction of active preferences at multiple cutoffs Returned

Tool call · Input 

{
  "description": "Test reconstruction of active preferences at multiple cutoffs",
  "command": "python3 -c \"\nimport pandas as pd\nimport numpy as np\n\nDATA = '/task/data'\nlikes_df = pd.read_parquet(f'{DATA}/train_likes.parquet')\nunlikes_df = pd.read_parquet(f'{DATA}/train_unlikes.parquet')\ndislikes_df = pd.read_parquet(f'{DATA}/train_dislikes.parquet')\nundislikes_df = pd.read_parquet(f'{DATA}/train_undislikes.parquet')\n\nlikes_df['event'] = 1\nunlikes_df['event'] = 0\nall_likes = pd.concat([likes_df[['uid', 'timestamp', 'item_id', 'event']], unlikes_df[['uid', 'timestamp', 'item_id', 'event']]])\n\ndislikes_df['event'] = 1\nundislikes_df['event'] = 0\nall_dislikes = pd.concat([dislikes_df[['uid', 'timestamp', 'item_id', 'event']], undislikes_df[['uid', 'timestamp', 'item_id', 'event']]])\n\nt_cutoff = 24786800\n# Test at t_cutoff\nsub_l = all_likes[all_likes.timestamp < t_cutoff].sort_values(['uid', 'item_id', 'timestamp'])\nlast_l = sub_l.drop_duplicates(['uid', 'item_id'], keep='last')\nact_l = last_l[last_l.event == 1]\nprint('Active likes count at train end:', len(act_l))\n\n# Test at t_cutoff - 7 days\nt_7d = t_cutoff - 7 * 86400\nsub_l_7d = all_likes[all_likes.timestamp < t_7d].sort_values(['uid', 'item_id', 'timestamp'])\nlast_l_7d = sub_l_7d.drop_duplicates(['uid', 'item_id'], keep='last')\nact_l_7d = last_l_7d[last_l_7d.event == 1]\nprint('Active likes count at train end - 7d:', len(act_l_7d))\n\""
}

Tool response 

<untrusted_context>
Output: Active likes count at train end: 718387
Active likes count at train end - 7d: 693114
Process Group PGID: 2814
</untrusted_context>
64

Run train_two_tower_v1.py prototype and measure validation score

+29:40
Recorded reasoning summary 39 words

Assembling Training Data

I'm currently focused on assembling the training dataset. Reconstructing active preferences is proving efficient, taking about half a second. Now, I'm setting up the backtest cutoffs, beginning with three, the first being T minus 7 days.

Recorded reasoning summary 48 words

Refining Feature Extraction

I'm refining the process for extracting user features across different backtest cutoffs. The plan is to extract features at each $T_c$ and then generate positive target items within a subsequent 7-day window, assigning grades and weights accordingly. I'm considering precomputing user representations to optimize this.

Recorded reasoning summary 44 words

Optimizing User Tower Inference

I've successfully streamlined user feature extraction for batches of 512 pairs, with inference taking mere milliseconds. I'm now integrating this into a prototype training script for initial validation runs, followed by evaluation of the user tower's performance on held-out data.

Tool callrun_shell_commandRun train_two_tower_v1.py prototype and measure validation score Returned

Tool call · Input 

{
  "description": "Run train_two_tower_v1.py prototype and measure validation score",
  "command": "cat << 'EOF' > /app/solution/train_two_tower_v1.py\nimport sys\nimport time\nfrom pathlib import Path\nimport numpy as np\nimport pandas as pd\nimport pyarrow.parquet as pq\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\nsys.path.insert(0, '/task')\nfrom ranking_evaluation import CandidateSet, ranked_items, validate_ranking, score_rankings\nfrom evaluate_public import validation_truth\n\nDATA = Path('/task/data')\n\ndef load_all_data():\n    t0 = time.time()\n    targets_df = pd.read_parquet(DATA / 'target_users.parquet')\n    target_uids = sorted(list(set(targets_df.uid)))\n    uid_to_target_idx = {uid: idx for idx, uid in enumerate(target_uids)}\n\n    cat_df = pd.read_parquet(DATA / 'candidate_catalog.parquet')\n    item_ids = cat_df.item_id.to_numpy()\n    num_items = len(item_ids)\n    item_to_idx = {item: idx + 1 for idx, item in enumerate(item_ids)}\n\n    # Item numerical features\n    pos = np.log1p(cat_df.positive_listens.to_numpy().astype(np.float32))\n    org_pos = np.log1p(cat_df.organic_positive_listens.to_numpy().astype(np.float32))\n    org_ratio = (cat_df.organic_positive_listens / (cat_df.positive_listens + 1)).to_numpy().astype(np.float32)\n    pop_rank = (cat_df.popularity_rank / 30000.0).to_numpy().astype(np.float32)\n\n    item_feats = np.stack([pos, org_pos, org_ratio, pop_rank], axis=1)\n    item_feats = (item_feats - item_feats.mean(axis=0)) / (item_feats.std(axis=0) + 1e-6)\n    item_feats = np.pad(item_feats, ((1, 0), (0, 0)), mode='constant')\n\n    # Item sampling probabilities for logQ correction\n    item_freqs = cat_df.positive_listens.to_numpy().astype(np.float32)\n    item_probs = item_freqs / item_freqs.sum()\n    log_q = np.pad(np.log(item_probs + 1e-10), (1, 0), mode='constant')\n\n    print(f\"Loaded metadata in {time.time() - t0:.2f} s\")\n\n    # Read listens\n    t1 = time.time()\n    table = pq.read_table(DATA / 'train_listens.parquet', columns=['uid', 'timestamp', 'item_id', 'is_organic', 'played_ratio_pct'])\n    df_listens = table.to_pandas()\n    df_listens = df_listens[df_listens.uid.isin(set(target_uids)) & df_listens.item_id.isin(item_to_idx)].copy()\n    df_listens['item_idx'] = df_listens.item_id.map(item_to_idx)\n    df_listens = df_listens.sort_values(['uid', 'timestamp'])\n    print(f\"Listens filtered: {len(df_listens):,} in {time.time() - t1:.2f} s\")\n\n    # Read explicit feedback\n    t2 = time.time()\n    likes_df = pd.read_parquet(DATA / 'train_likes.parquet')\n    unlikes_df = pd.read_parquet(DATA / 'train_unlikes.parquet')\n    dislikes_df = pd.read_parquet(DATA / 'train_dislikes.parquet')\n    undislikes_df = pd.read_parquet(DATA / 'train_undislikes.parquet')\n\n    likes_df['event'] = 1\n    unlikes_df['event'] = 0\n    all_likes = pd.concat([likes_df[['uid', 'timestamp', 'item_id', 'event']], unlikes_df[['uid', 'timestamp', 'item_id', 'event']]])\n    all_likes = all_likes[all_likes.uid.isin(set(target_uids)) & all_likes.item_id.isin(item_to_idx)].copy()\n    all_likes['item_idx'] = all_likes.item_id.map(item_to_idx)\n\n    dislikes_df['event'] = 1\n    undislikes_df['event'] = 0\n    all_dislikes = pd.concat([dislikes_df[['uid', 'timestamp', 'item_id', 'event']], undislikes_df[['uid', 'timestamp', 'item_id', 'event']]])\n    all_dislikes = all_dislikes[all_dislikes.uid.isin(set(target_uids)) & all_dislikes.item_id.isin(item_to_idx)].copy()\n    all_dislikes['item_idx'] = all_dislikes.item_id.map(item_to_idx)\n    print(f\"Feedback loaded in {time.time() - t2:.2f} s\")\n\n    return target_uids, uid_to_target_idx, item_ids, item_to_idx, item_feats, log_q, df_listens, all_likes, all_dislikes\n\ndef get_active_prefs(all_likes, all_dislikes, cutoff_timestamp):\n    sub_l = all_likes[all_likes.timestamp < cutoff_timestamp].sort_values(['uid', 'item_id', 'timestamp'])\n    last_l = sub_l.drop_duplicates(['uid', 'item_id'], keep='last')\n    active_l = last_l[last_l.event == 1].copy()\n\n    sub_d = all_dislikes[all_dislikes.timestamp < cutoff_timestamp].sort_values(['uid', 'item_id', 'timestamp'])\n    last_d = sub_d.drop_duplicates(['uid', 'item_id'], keep='last')\n    active_d = last_d[last_d.event == 1].copy()\n\n    return active_l, active_d\n\ndef extract_features(\n    df_listens,\n    active_likes,\n    active_dislikes,\n    target_uids,\n    cutoff_timestamp,\n    history_days=90,\n    max_listens=100,\n    max_org=50,\n    max_likes=50,\n    max_dislikes=20\n):\n    t0 = time.time()\n    num_users = len(target_uids)\n    uid_to_row = {uid: r for r, uid in enumerate(target_uids)}\n\n    t_min = cutoff_timestamp - history_days * 86400\n    sub = df_listens[(df_listens.timestamp >= t_min) & (df_listens.timestamp < cutoff_timestamp)].copy()\n\n    listens_arr = np.zeros((num_users, max_listens), dtype=np.int64)\n    weights_arr = np.zeros((num_users, max_listens), dtype=np.float32)\n    org_arr = np.zeros((num_users, max_org), dtype=np.int64)\n    likes_arr = np.zeros((num_users, max_likes), dtype=np.int64)\n    dislikes_arr = np.zeros((num_users, max_dislikes), dtype=np.int64)\n    stats_arr = np.zeros((num_users, 5), dtype=np.float32)\n\n    if len(sub) > 0:\n        delta_days = (cutoff_timestamp - sub['timestamp'].to_numpy(dtype=np.float32)) / 86400.0\n        recency = np.exp(-0.05 * delta_days)\n        completion = np.clip(sub['played_ratio_pct'].to_numpy(dtype=np.float32) / 100.0, 0.1, 1.0)\n        organic = np.where(sub['is_organic'].to_numpy() == 1, 1.5, 1.0).astype(np.float32)\n        sub['weight'] = completion * recency * organic\n        sub['row'] = sub['uid'].map(uid_to_row)\n        sub_valid = sub.dropna(subset=['row'])\n        sub_valid['row'] = sub_valid['row'].astype(int)\n\n        tail_listens = sub_valid.groupby('row').tail(max_listens)\n        for r, group in tail_listens.groupby('row'):\n            n = len(group)\n            listens_arr[r, :n] = group['item_idx'].to_numpy()\n            weights_arr[r, :n] = group['weight'].to_numpy()\n\n        org_sub = sub_valid[(sub_valid.is_organic == 1) & (sub_valid.played_ratio_pct >= 80)]\n        tail_org = org_sub.groupby('row').tail(max_org)\n        for r, group in tail_org.groupby('row'):\n            n = len(group)\n            org_arr[r, :n] = group['item_idx'].to_numpy()\n\n        grouped = sub_valid.groupby('row')\n        tot_listens = grouped.size()\n        stats_arr[tot_listens.index, 0] = np.log1p(tot_listens.to_numpy(dtype=np.float32))\n        if len(org_sub) > 0:\n            tot_org = org_sub.groupby('row').size()\n            stats_arr[tot_org.index, 1] = np.log1p(tot_org.to_numpy(dtype=np.float32))\n        mean_comp = grouped['played_ratio_pct'].mean() / 100.0\n        stats_arr[mean_comp.index, 4] = mean_comp.to_numpy(dtype=np.float32)\n\n    if active_likes is not None and len(active_likes) > 0:\n        sub_likes = active_likes.copy()\n        sub_likes['row'] = sub_likes['uid'].map(uid_to_row)\n        sub_likes = sub_likes.dropna(subset=['row'])\n        sub_likes['row'] = sub_likes['row'].astype(int)\n        tail_likes = sub_likes.groupby('row').tail(max_likes)\n        for r, group in tail_likes.groupby('row'):\n            n = len(group)\n            likes_arr[r, :n] = group['item_idx'].to_numpy()\n        tot_likes = sub_likes.groupby('row').size()\n        stats_arr[tot_likes.index, 2] = np.log1p(tot_likes.to_numpy(dtype=np.float32))\n\n    if active_dislikes is not None and len(active_dislikes) > 0:\n        sub_dis = active_dislikes.copy()\n        sub_dis['row'] = sub_dis['uid'].map(uid_to_row)\n        sub_dis = sub_dis.dropna(subset=['row'])\n        sub_dis['row'] = sub_dis['row'].astype(int)\n        tail_dis = sub_dis.groupby('row').tail(max_dislikes)\n        for r, group in tail_dis.groupby('row'):\n            n = len(group)\n            dislikes_arr[r, :n] = group['item_idx'].to_numpy()\n        tot_dis = sub_dis.groupby('row').size()\n        stats_arr[tot_dis.index, 3] = np.log1p(tot_dis.to_numpy(dtype=np.float32))\n\n    mean = stats_arr.mean(axis=0, keepdims=True)\n    std = stats_arr.std(axis=0, keepdims=True) + 1e-6\n    stats_arr = (stats_arr - mean) / std\n\n    res = {\n        'listens': torch.from_numpy(listens_arr),\n        'listen_weights': torch.from_numpy(weights_arr),\n        'org_listens': torch.from_numpy(org_arr),\n        'likes': torch.from_numpy(likes_arr),\n        'dislikes': torch.from_numpy(dislikes_arr),\n        'user_stats': torch.from_numpy(stats_arr),\n    }\n    print(f\"Features extracted for cutoff {cutoff_timestamp} in {time.time() - t0:.2f} s\")\n    return res\n\nclass ItemTower(nn.Module):\n    def __init__(self, item_emb, emb_dim, num_features):\n        super().__init__()\n        self.item_emb = item_emb\n        self.mlp = nn.Sequential(\n            nn.Linear(emb_dim + num_features, emb_dim),\n            nn.LayerNorm(emb_dim),\n            nn.GELU(),\n            nn.Linear(emb_dim, emb_dim),\n            nn.LayerNorm(emb_dim)\n        )\n\n    def forward(self, item_indices, item_features):\n        e = self.item_emb(item_indices)\n        x = torch.cat([e, item_features], dim=-1)\n        v = self.mlp(x)\n        return F.normalize(v, p=2, dim=-1)\n\nclass UserTower(nn.Module):\n    def __init__(self, item_emb, emb_dim, num_user_features):\n        super().__init__()\n        self.item_emb = item_emb\n        self.mlp = nn.Sequential(\n            nn.Linear(emb_dim * 4 + num_user_features, emb_dim * 2),\n            nn.LayerNorm(emb_dim * 2),\n            nn.GELU(),\n            nn.Dropout(0.1),\n            nn.Linear(emb_dim * 2, emb_dim),\n            nn.LayerNorm(emb_dim)\n        )\n\n    def forward(self, listens, listen_weights, org_listens, likes, dislikes, user_stats):\n        e_listens = self.item_emb(listens)\n        w = listen_weights.unsqueeze(-1)\n        sum_w = w.sum(dim=1).clamp(min=1e-5)\n        h_listens = (e_listens * w).sum(dim=1) / sum_w\n\n        e_org = self.item_emb(org_listens)\n        mask_org = (org_listens > 0).float().unsqueeze(-1)\n        sum_org = mask_org.sum(dim=1)\n        h_org = torch.where(sum_org > 0, (e_org * mask_org).sum(dim=1) / sum_org.clamp(min=1e-5), torch.zeros_like(h_listens))\n\n        e_likes = self.item_emb(likes)\n        mask_likes = (likes > 0).float().unsqueeze(-1)\n        sum_likes = mask_likes.sum(dim=1)\n        h_likes = torch.where(sum_likes > 0, (e_likes * mask_likes).sum(dim=1) / sum_likes.clamp(min=1e-5), torch.zeros_like(h_listens))\n\n        e_dislikes = self.item_emb(dislikes)\n        mask_dislikes = (dislikes > 0).float().unsqueeze(-1)\n        sum_dislikes = mask_dislikes.sum(dim=1)\n        h_dislikes = torch.where(sum_dislikes > 0, (e_dislikes * mask_dislikes).sum(dim=1) / sum_dislikes.clamp(min=1e-5), torch.zeros_like(h_listens))\n\n        h_combined = torch.cat([h_listens, h_org, h_likes, h_dislikes, user_stats], dim=-1)\n        u = self.mlp(h_combined)\n        return F.normalize(u, p=2, dim=-1)\n\nclass TwoTowerModel(nn.Module):\n    def __init__(self, num_items, emb_dim=64, num_item_features=4, num_user_features=5):\n        super().__init__()\n        self.item_emb = nn.Embedding(num_items + 1, emb_dim, padding_idx=0)\n        nn.init.normal_(self.item_emb.weight, std=0.02)\n        with torch.no_grad():\n            self.item_emb.weight[0].fill_(0)\n\n        self.item_tower = ItemTower(self.item_emb, emb_dim, num_item_features)\n        self.user_tower = UserTower(self.item_emb, emb_dim, num_user_features)\n        self.logit_scale = nn.Parameter(torch.ones([]) * np.log(1 / 0.07))\n\ndef main():\n    target_uids, uid_to_target_idx, item_ids, item_to_idx, item_feats, log_q, df_listens, all_likes, all_dislikes = load_all_data()\n\n    item_feats_torch = torch.from_numpy(item_feats)\n    log_q_torch = torch.from_numpy(log_q)\n\n    # Validation truth and eligibility\n    val_cand = CandidateSet(DATA / 'validation_eligible_candidates.npz')\n    val_unpacked = np.unpackbits(val_cand.packed, axis=1, count=30000, bitorder='little')\n    val_mask = torch.from_numpy(val_unpacked).bool()\n    targets_set = set(target_uids)\n    graded_val = validation_truth(val_cand, targets_set)\n\n    t_end = 24786800\n    week = 7 * 86400\n\n    # 1. Validation features (at t_end)\n    act_likes_val, act_dislikes_val = get_active_prefs(all_likes, all_dislikes, t_end)\n    val_user_feats = extract_features(df_listens, act_likes_val, act_dislikes_val, target_uids, t_end)\n\n    # 2. Backtest training cutoffs\n    # Let's use 2 cutoffs for prototype: t_end - 1w and t_end - 2w\n    cutoffs = [t_end - 1 * week, t_end - 2 * week]\n    train_samples = [] # list of (feats_dict, user_row, item_idx, grade, weight)\n\n    for c_idx, t_c in enumerate(cutoffs):\n        act_likes_c, act_dislikes_c = get_active_prefs(all_likes, all_dislikes, t_c)\n        feats_c = extract_features(df_listens, act_likes_c, act_dislikes_c, target_uids, t_c)\n\n        # Future window: [t_c, t_c + week]\n        fut_listens = df_listens[(df_listens.timestamp >= t_c) & (df_listens.timestamp < t_c + week) & (df_listens.is_organic == 1) & (df_listens.played_ratio_pct >= 80)]\n        likes_df_raw = pd.read_parquet(DATA / 'train_likes.parquet')\n        fut_likes = likes_df_raw[(likes_df_raw.timestamp >= t_c) & (likes_df_raw.timestamp < t_c + week) & (likes_df_raw.is_organic == 1) & (likes_df_raw.uid.isin(targets_set)) & (likes_df_raw.item_id.isin(item_to_idx))].copy()\n        fut_likes['item_idx'] = fut_likes.item_id.map(item_to_idx)\n\n        # Past interactions before t_c\n        past_listens = df_listens[df_listens.timestamp < t_c]\n        past_user_items = past_listens.groupby('uid')['item_idx'].apply(set).to_dict()\n\n        # Grade 1 discovery listens\n        for uid, item_idx in zip(fut_listens['uid'], fut_listens['item_idx']):\n            if item_idx not in past_user_items.get(uid, set()):\n                u_row = uid_to_target_idx[uid]\n                train_samples.append((c_idx, u_row, item_idx, 1, 1.0))\n\n        # Grade 3 discovery likes (takes precedence)\n        for uid, item_idx in zip(fut_likes['uid'], fut_likes['item_idx']):\n            if item_idx not in past_user_items.get(uid, set()):\n                u_row = uid_to_target_idx[uid]\n                train_samples.append((c_idx, u_row, item_idx, 3, 4.0))\n\n    cutoffs_feats = [extract_features(df_listens, *get_active_prefs(all_likes, all_dislikes, t_c), target_uids, t_c) for t_c in cutoffs]\n\n    print(f\"Total training samples: {len(train_samples):,}\")\n\n    # Build arrays for fast batching\n    sample_cutoff = np.array([s[0] for s in train_samples], dtype=np.int32)\n    sample_user = np.array([s[1] for s in train_samples], dtype=np.int32)\n    sample_item = np.array([s[2] for s in train_samples], dtype=np.int32)\n    sample_weight = np.array([s[4] for s in train_samples], dtype=np.float32)\n\n    # Initialize model\n    model = TwoTowerModel(num_items=30000, emb_dim=64, num_item_features=4, num_user_features=5)\n    optimizer = torch.optim.AdamW(model.parameters(), lr=1e-3, weight_decay=1e-4)\n\n    batch_size = 512\n    num_samples = len(sample_cutoff)\n    indices = np.arange(num_samples)\n\n    print(\"Beginning training...\")\n    for epoch in range(1, 6):\n        t_ep = time.time()\n        np.random.shuffle(indices)\n        model.train()\n        total_loss = 0.0\n        num_batches = 0\n\n        for start in range(0, num_samples - batch_size + 1, batch_size):\n            b_idx = indices[start:start + batch_size]\n            b_cutoff = sample_cutoff[b_idx]\n            b_user = sample_user[b_idx]\n            b_items = torch.from_numpy(sample_item[b_idx])\n            b_weights = torch.from_numpy(sample_weight[b_idx])\n\n            # Gather user features per sample\n            # Since b_cutoff can have 0 or 1, gather from corresponding cutoff_feats\n            b_listens = torch.zeros((batch_size, 100), dtype=torch.int64)\n            b_lweights = torch.zeros((batch_size, 100), dtype=torch.float32)\n            b_org = torch.zeros((batch_size, 50), dtype=torch.int64)\n            b_likes = torch.zeros((batch_size, 50), dtype=torch.int64)\n            b_dislikes = torch.zeros((batch_size, 20), dtype=torch.int64)\n            b_stats = torch.zeros((batch_size, 5), dtype=torch.float32)\n\n            for c in range(len(cutoffs)):\n                mask_c = (b_cutoff == c)\n                if np.any(mask_c):\n                    u_c = b_user[mask_c]\n                    b_listens[mask_c] = cutoffs_feats[c]['listens'][u_c]\n                    b_lweights[mask_c] = cutoffs_feats[c]['listen_weights'][u_c]\n                    b_org[mask_c] = cutoffs_feats[c]['org_listens'][u_c]\n                    b_likes[mask_c] = cutoffs_feats[c]['likes'][u_c]\n                    b_dislikes[mask_c] = cutoffs_feats[c]['dislikes'][u_c]\n                    b_stats[mask_c] = cutoffs_feats[c]['user_stats'][u_c]\n\n            b_item_feats = item_feats_torch[b_items]\n\n            optimizer.zero_grad()\n            u = model.user_tower(b_listens, b_lweights, b_org, b_likes, b_dislikes, b_stats)\n            v = model.item_tower(b_items, b_item_feats)\n\n            logit_scale = model.logit_scale.exp().clamp(max=100.0)\n            logits = torch.matmul(u, v.T) * logit_scale\n\n            # Popularity correction for in-batch negatives: subtract logQ for columns\n            b_log_q = log_q_torch[b_items].unsqueeze(0)\n            logits = logits - b_log_q\n\n            target = torch.arange(batch_size, dtype=torch.long)\n            loss = (F.cross_entropy(logits, target, reduction='none') * b_weights).mean()\n\n            loss.backward()\n            optimizer.step()\n\n            total_loss += loss.item()\n            num_batches += 1\n\n        avg_loss = total_loss / max(1, num_batches)\n\n        # Validation evaluation\n        model.eval()\n        with torch.no_grad():\n            # Precompute all 30000 candidate items\n            all_indices = torch.arange(1, 30001)\n            all_feats = item_feats_torch[1:30001]\n            V_cand = model.item_tower(all_indices, all_feats) # (30000, 64)\n\n            # Compute user vectors for all target users\n            U_val = model.user_tower(\n                val_user_feats['listens'],\n                val_user_feats['listen_weights'],\n                val_user_feats['org_listens'],\n                val_user_feats['likes'],\n                val_user_feats['dislikes'],\n                val_user_feats['user_stats']\n            ) # (8456, 64)\n\n            scores = torch.matmul(U_val, V_cand.T) # (8456, 30000)\n            scores[~val_mask] = -float('inf')\n\n            top100_idx = torch.topk(scores, 100, dim=1).indices.cpu().numpy()\n\n            # Build ranked dictionary for fast scoring\n            ranked = {\n                target_uids[r]: list(map(int, val_cand.item_ids[top100_idx[r]]))\n                for r in range(len(target_uids))\n            }\n            res = score_rankings(ranked, graded_val)\n\n        print(f\"Epoch {epoch:2d} | Loss: {avg_loss:.4f} | Val NDCG@10: {res['quality_ndcg@10']:.6f} | Time: {time.time() - t_ep:.2f} s\")\n\nif __name__ == '__main__':\n    main()\nEOF\npython3 /app/solution/train_two_tower_v1.py\n"
}

Tool response 

<untrusted_context>
Output: Loaded metadata in 0.03 s
Listens filtered: 31,062,540 in 12.18 s
Feedback loaded in 0.32 s
Features extracted for cutoff 24786800 in 2.76 s
Features extracted for cutoff 24182000 in 2.68 s
Features extracted for cutoff 23577200 in 2.55 s
Features extracted for cutoff 24182000 in 2.69 s
Features extracted for cutoff 23577200 in 2.88 s
Total training samples: 140,825
Beginning training...
Epoch  1 | Loss: 6.8819 | Val NDCG@10: 0.006406 | Time: 6.79 s
Epoch  2 | Loss: 5.4986 | Val NDCG@10: 0.006035 | Time: 6.96 s
Epoch  3 | Loss: 4.5456 | Val NDCG@10: 0.006022 | Time: 6.96 s
Epoch  4 | Loss: 3.9366 | Val NDCG@10: 0.004830 | Time: 6.92 s
Epoch  5 | Loss: 3.5545 | Val NDCG@10: 0.005091 | Time: 6.97 s
Process Group PGID: 2834
</untrusted_context>
65

Compare pure popularity vs organic popularity baselines

+31:42
Recorded reasoning summary 36 words

Observing Training Performance

I'm reviewing the recent training run. Epochs clocked in at a speedy 6.9 seconds, and the loss function showed consistent improvement. However, I'm noticing the validation NDCG@10 is currently underperforming the popularity baseline.

Recorded reasoning summary 58 words

Investigating Performance Discrepancy

I'm digging into the root cause of the validation NDCG@10 underperformance compared to the popularity baseline. My current hypothesis centers on how subtracting b_log_q during training might have inadvertently penalized popular items, and how L2 normalization of item embeddings in the ItemTower at test time completely removed any inherent popularity bias from the retrieval scores.

Recorded reasoning summary 50 words

Exploring Vector Popularity

I'm reconsidering how item vectors might implicitly capture popularity. My current thinking suggests that if item vectors aren't L2-normalized, their unnormalized magnitude could naturally reflect an item's global appeal. This approach allows for a direct dot product with user vectors, potentially reintroducing the popularity bias I observed.

Tool callrun_shell_commandCompare pure popularity vs organic popularity baselines Returned

Tool call · Input 

{
  "command": "python3 -c \"\nimport torch\nimport numpy as np\nimport pandas as pd\nfrom pathlib import Path\nimport sys\nsys.path.insert(0, '/task')\nfrom ranking_evaluation import CandidateSet, score_rankings\nfrom evaluate_public import validation_truth\n\n# Load validation candidate set and truth\nDATA = Path('/task/data')\nval_cand = CandidateSet(DATA / 'validation_eligible_candidates.npz')\nval_unpacked = np.unpackbits(val_cand.packed, axis=1, count=30000, bitorder='little')\nval_mask = torch.from_numpy(val_unpacked).bool()\ntargets_df = pd.read_parquet(DATA / 'target_users.parquet')\ntarget_uids = sorted(list(set(targets_df.uid)))\ntargets_set = set(target_uids)\ngraded_val = validation_truth(val_cand, targets_set)\n\ncat_df = pd.read_parquet(DATA / 'candidate_catalog.parquet')\n# Popularity feature: log positive listens normalized\npop = np.log1p(cat_df.positive_listens.to_numpy().astype(np.float32))\npop_tensor = torch.from_numpy(pop).unsqueeze(0) # (1, 30000)\n\n# Check pure popularity score\npop_scores = pop_tensor.repeat(8456, 1)\npop_scores[~val_mask] = -float('inf')\ntop100_idx = torch.topk(pop_scores, 100, dim=1).indices.cpu().numpy()\nranked = {\n    target_uids[r]: list(map(int, val_cand.item_ids[top100_idx[r]]))\n    for r in range(len(target_uids))\n}\nprint('Pure popularity NDCG@10:', score_rankings(ranked, graded_val))\n\n# What about organic positive listens?\norg_pop = np.log1p(cat_df.organic_positive_listens.to_numpy().astype(np.float32))\norg_scores = torch.from_numpy(org_pop).unsqueeze(0).repeat(8456, 1)\norg_scores[~val_mask] = -float('inf')\ntop100_idx = torch.topk(org_scores, 100, dim=1).indices.cpu().numpy()\nranked = {\n    target_uids[r]: list(map(int, val_cand.item_ids[top100_idx[r]]))\n    for r in range(len(target_uids))\n}\nprint('Organic popularity NDCG@10:', score_rankings(ranked, graded_val))\n\"",
  "description": "Compare pure popularity vs organic popularity baselines"
}

Tool response 

<untrusted_context>
Output: Pure popularity NDCG@10: {'quality_ndcg@10': 0.010678, 'quality_users': 4263}
Organic popularity NDCG@10: {'quality_ndcg@10': 0.010578, 'quality_users': 4263}
Process Group PGID: 2872
</untrusted_context>
66

Hypothesis check on log_q popularity penalty

+32:34
Recorded reasoning summary 43 words

Analyzing Model Performance

I'm currently investigating why our trained model underperformed compared to simple popularity metrics. My focus is on understanding the impact of the logits = logits - b_log_q operation and optimizing the two-tower model's training strategy to significantly surpass popularity scores.

Recorded reasoning summary 52 words

Correcting Logit Bias

I've identified that logits = logits - b_log_q was indeed the culprit, but in the opposite way I initially thought. Subtracting the negative log_q actually added positive values, heavily penalizing popular items and boosting unpopular ones. This explains the underperformance; the model was learning to rank unpopular items higher.

Tool callrun_shell_commandHypothesis check on log_q popularity penalty Returned

Tool call · Input 

{
  "command": "python3 -c \"\n# Let's check what ranks the prototype model was retrieving\n# Without log_q subtraction vs with log_q subtraction:\n# If we do NOT subtract log_q, what happens?\nprint('Check without log_q')\n\"",
  "description": "Hypothesis check on log_q popularity penalty"
}

Tool response 

<untrusted_context>
Output: Check without log_q
Process Group PGID: 2901
</untrusted_context>
67

Test TwoTower with item bias and no log_q penalty

+37:12
Recorded reasoning summary 47 words

Exploring Training Variants

I'm now considering training strategies that bypass direct log_q subtraction. My current focus is on exploring ranking-based losses like BPR, or standard sampled cross-entropy, to see if they offer improvements. The next step involves analyzing the specific contributions of each component within these approaches.

Recorded reasoning summary 41 words

Investigating Norm Effects

I'm now testing whether item popularity naturally emerges in two-tower models by varying normalization. My hypothesis is that allowing $\|v_j\|$ to grow for popular items, or explicitly incorporating magnitude, could naturally boost their scores without explicit popularity handling.

Recorded reasoning summary 50 words

Folding Biases Into Dot Product

I'm actively exploring how to integrate item bias directly into the dot product by augmenting item embeddings. My current focus is on expanding the embedding dimension to incorporate the bias, effectively transforming $u \cdot v_j + b_j$ into a single dot product of augmented vectors.

Tool callrun_shell_commandTest TwoTower with item bias and no log_q penalty Returned

Tool call · Input 

{
  "description": "Test TwoTower with item bias and no log_q penalty",
  "command": "python3 -c \"\nimport sys\nsys.path.insert(0, '/app/solution')\nimport train_two_tower_v1 as tt\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nimport numpy as np\nimport time\n\ntarget_uids, uid_to_target_idx, item_ids, item_to_idx, item_feats, log_q, df_listens, all_likes, all_dislikes = tt.load_all_data()\n\nt_end = 24786800\nweek = 7 * 86400\nact_likes_val, act_dislikes_val = tt.get_active_prefs(all_likes, all_dislikes, t_end)\nval_user_feats = tt.extract_features(df_listens, act_likes_val, act_dislikes_val, target_uids, t_end)\n\ncutoffs = [t_end - 1 * week, t_end - 2 * week]\ncutoffs_feats = [tt.extract_features(df_listens, *tt.get_active_prefs(all_likes, all_dislikes, t_c), target_uids, t_c) for t_c in cutoffs]\n\ntargets_set = set(target_uids)\nval_cand = tt.CandidateSet(tt.DATA / 'validation_eligible_candidates.npz')\nval_unpacked = np.unpackbits(val_cand.packed, axis=1, count=30000, bitorder='little')\nval_mask = torch.from_numpy(val_unpacked).bool()\ngraded_val = tt.validation_truth(val_cand, targets_set)\n\ntrain_samples = []\nfor c_idx, t_c in enumerate(cutoffs):\n    fut_listens = df_listens[(df_listens.timestamp >= t_c) & (df_listens.timestamp < t_c + week) & (df_listens.is_organic == 1) & (df_listens.played_ratio_pct >= 80)]\n    likes_df_raw = tt.pd.read_parquet(tt.DATA / 'train_likes.parquet')\n    fut_likes = likes_df_raw[(likes_df_raw.timestamp >= t_c) & (likes_df_raw.timestamp < t_c + week) & (likes_df_raw.is_organic == 1) & (likes_df_raw.uid.isin(targets_set)) & (likes_df_raw.item_id.isin(item_to_idx))].copy()\n    fut_likes['item_idx'] = fut_likes.item_id.map(item_to_idx)\n\n    past_listens = df_listens[df_listens.timestamp < t_c]\n    past_user_items = past_listens.groupby('uid')['item_idx'].apply(set).to_dict()\n\n    for uid, item_idx in zip(fut_listens['uid'], fut_listens['item_idx']):\n        if item_idx not in past_user_items.get(uid, set()):\n            train_samples.append((c_idx, uid_to_target_idx[uid], item_idx, 1, 1.0))\n\n    for uid, item_idx in zip(fut_likes['uid'], fut_likes['item_idx']):\n        if item_idx not in past_user_items.get(uid, set()):\n            train_samples.append((c_idx, uid_to_target_idx[uid], item_idx, 3, 4.0))\n\nsample_cutoff = np.array([s[0] for s in train_samples], dtype=np.int32)\nsample_user = np.array([s[1] for s in train_samples], dtype=np.int32)\nsample_item = np.array([s[2] for s in train_samples], dtype=np.int32)\nsample_weight = np.array([s[4] for s in train_samples], dtype=np.float32)\n\nitem_feats_torch = torch.from_numpy(item_feats)\n\n# Model without forced L2 norm on ItemTower, or with item bias\nclass ItemTowerV2(nn.Module):\n    def __init__(self, item_emb, emb_dim, num_features):\n        super().__init__()\n        self.item_emb = item_emb\n        self.mlp = nn.Sequential(\n            nn.Linear(emb_dim + num_features, emb_dim),\n            nn.LayerNorm(emb_dim),\n            nn.GELU(),\n            nn.Linear(emb_dim, emb_dim)\n        )\n        self.bias = nn.Parameter(torch.zeros(30001))\n\n    def forward(self, item_indices, item_features):\n        e = self.item_emb(item_indices)\n        x = torch.cat([e, item_features], dim=-1)\n        v = self.mlp(x)\n        v = F.normalize(v, p=2, dim=-1)\n        # bias term\n        b = self.bias[item_indices].unsqueeze(-1)\n        return torch.cat([v, b], dim=-1) # (B, emb_dim + 1)\n\nclass UserTowerV2(nn.Module):\n    def __init__(self, item_emb, emb_dim, num_user_features):\n        super().__init__()\n        self.item_emb = item_emb\n        self.mlp = nn.Sequential(\n            nn.Linear(emb_dim * 4 + num_user_features, emb_dim * 2),\n            nn.LayerNorm(emb_dim * 2),\n            nn.GELU(),\n            nn.Dropout(0.1),\n            nn.Linear(emb_dim * 2, emb_dim),\n            nn.LayerNorm(emb_dim)\n        )\n        self.bias_scale = nn.Parameter(torch.tensor([1.0]))\n\n    def forward(self, listens, listen_weights, org_listens, likes, dislikes, user_stats):\n        e_listens = self.item_emb(listens)\n        w = listen_weights.unsqueeze(-1)\n        sum_w = w.sum(dim=1).clamp(min=1e-5)\n        h_listens = (e_listens * w).sum(dim=1) / sum_w\n\n        e_org = self.item_emb(org_listens)\n        mask_org = (org_listens > 0).float().unsqueeze(-1)\n        sum_org = mask_org.sum(dim=1)\n        h_org = torch.where(sum_org > 0, (e_org * mask_org).sum(dim=1) / sum_org.clamp(min=1e-5), torch.zeros_like(h_listens))\n\n        e_likes = self.item_emb(likes)\n        mask_likes = (likes > 0).float().unsqueeze(-1)\n        sum_likes = mask_likes.sum(dim=1)\n        h_likes = torch.where(sum_likes > 0, (e_likes * mask_likes).sum(dim=1) / sum_likes.clamp(min=1e-5), torch.zeros_like(h_listens))\n\n        e_dislikes = self.item_emb(dislikes)\n        mask_dislikes = (dislikes > 0).float().unsqueeze(-1)\n        sum_dislikes = mask_dislikes.sum(dim=1)\n        h_dislikes = torch.where(sum_dislikes > 0, (e_dislikes * mask_dislikes).sum(dim=1) / sum_dislikes.clamp(min=1e-5), torch.zeros_like(h_listens))\n\n        h_combined = torch.cat([h_listens, h_org, h_likes, h_dislikes, user_stats], dim=-1)\n        u = self.mlp(h_combined)\n        u = F.normalize(u, p=2, dim=-1)\n        ones = self.bias_scale.expand(u.shape[0], 1)\n        return torch.cat([u, ones], dim=-1)\n\nclass TwoTowerV2(nn.Module):\n    def __init__(self, num_items=30000, emb_dim=64):\n        super().__init__()\n        self.item_emb = nn.Embedding(num_items + 1, emb_dim, padding_idx=0)\n        nn.init.normal_(self.item_emb.weight, std=0.02)\n        with torch.no_grad():\n            self.item_emb.weight[0].fill_(0)\n        self.item_tower = ItemTowerV2(self.item_emb, emb_dim, num_features=4)\n        # Initialize item bias with popularity\n        pop = np.log1p(tt.pd.read_parquet(tt.DATA / 'candidate_catalog.parquet').positive_listens.to_numpy().astype(np.float32))\n        pop = (pop - pop.mean()) / pop.std()\n        with torch.no_grad():\n            self.item_tower.bias[1:30001] = torch.from_numpy(pop) * 0.5\n        self.user_tower = UserTowerV2(self.item_emb, emb_dim, num_user_features=5)\n        self.logit_scale = nn.Parameter(torch.ones([]) * np.log(1 / 0.07))\n\nmodel = TwoTowerV2(num_items=30000, emb_dim=64)\noptimizer = torch.optim.AdamW(model.parameters(), lr=1e-3, weight_decay=1e-4)\n\nbatch_size = 512\nnum_samples = len(sample_cutoff)\nindices = np.arange(num_samples)\n\nprint('Testing without log_q subtraction and with popularity bias initialization...')\nfor epoch in range(1, 6):\n    t_ep = time.time()\n    np.random.shuffle(indices)\n    model.train()\n    total_loss = 0.0\n    num_batches = 0\n\n    for start in range(0, num_samples - batch_size + 1, batch_size):\n        b_idx = indices[start:start + batch_size]\n        b_cutoff = sample_cutoff[b_idx]\n        b_user = sample_user[b_idx]\n        b_items = torch.from_numpy(sample_item[b_idx])\n        b_weights = torch.from_numpy(sample_weight[b_idx])\n\n        b_listens = torch.zeros((batch_size, 100), dtype=torch.int64)\n        b_lweights = torch.zeros((batch_size, 100), dtype=torch.float32)\n        b_org = torch.zeros((batch_size, 50), dtype=torch.int64)\n        b_likes = torch.zeros((batch_size, 50), dtype=torch.int64)\n        b_dislikes = torch.zeros((batch_size, 20), dtype=torch.int64)\n        b_stats = torch.zeros((batch_size, 5), dtype=torch.float32)\n\n        for c in range(len(cutoffs)):\n            mask_c = (b_cutoff == c)\n            if np.any(mask_c):\n                u_c = b_user[mask_c]\n                b_listens[mask_c] = cutoffs_feats[c]['listens'][u_c]\n                b_lweights[mask_c] = cutoffs_feats[c]['listen_weights'][u_c]\n                b_org[mask_c] = cutoffs_feats[c]['org_listens'][u_c]\n                b_likes[mask_c] = cutoffs_feats[c]['likes'][u_c]\n                b_dislikes[mask_c] = cutoffs_feats[c]['dislikes'][u_c]\n                b_stats[mask_c] = cutoffs_feats[c]['user_stats'][u_c]\n\n        b_item_feats = item_feats_torch[b_items]\n\n        optimizer.zero_grad()\n        u = model.user_tower(b_listens, b_lweights, b_org, b_likes, b_dislikes, b_stats)\n        v = model.item_tower(b_items, b_item_feats)\n\n        logit_scale = model.logit_scale.exp().clamp(max=100.0)\n        logits = torch.matmul(u, v.T) * logit_scale\n\n        target = torch.arange(batch_size, dtype=torch.long)\n        loss = (F.cross_entropy(logits, target, reduction='none') * b_weights).mean()\n\n        loss.backward()\n        optimizer.step()\n        total_loss += loss.item()\n        num_batches += 1\n\n    model.eval()\n    with torch.no_grad():\n        all_indices = torch.arange(1, 30001)\n        all_feats = item_feats_torch[1:30001]\n        V_cand = model.item_tower(all_indices, all_feats)\n\n        U_val = model.user_tower(\n            val_user_feats['listens'],\n            val_user_feats['listen_weights'],\n            val_user_feats['org_listens'],\n            val_user_feats['likes'],\n            val_user_feats['dislikes'],\n            val_user_feats['user_stats']\n        )\n\n        scores = torch.matmul(U_val, V_cand.T)\n        scores[~val_mask] = -float('inf')\n        top100_idx = torch.topk(scores, 100, dim=1).indices.cpu().numpy()\n\n        ranked = {\n            target_uids[r]: list(map(int, val_cand.item_ids[top100_idx[r]]))\n            for r in range(len(target_uids))\n        }\n        res = tt.score_rankings(ranked, graded_val)\n\n    print(f'Epoch {epoch:2d} | Loss: {total_loss/num_batches:.4f} | Val NDCG@10: {res[\\\"quality_ndcg@10\\\"]:.6f} | Time: {time.time() - t_ep:.2f} s')\n\""
}

Tool response 

<untrusted_context>
Output: Loaded metadata in 0.04 s
Listens filtered: 31,062,540 in 12.23 s
Feedback loaded in 0.32 s
Features extracted for cutoff 24786800 in 2.70 s
Features extracted for cutoff 24182000 in 2.62 s
Features extracted for cutoff 23577200 in 2.50 s
Testing without log_q subtraction and with popularity bias initialization...
Epoch  1 | Loss: 9.6695 | Val NDCG@10: 0.005229 | Time: 7.01 s
Epoch  2 | Loss: 6.9969 | Val NDCG@10: 0.003553 | Time: 7.10 s
Epoch  3 | Loss: 6.2512 | Val NDCG@10: 0.001830 | Time: 7.34 s
Epoch  4 | Loss: 5.5583 | Val NDCG@10: 0.001885 | Time: 6.97 s
Epoch  5 | Loss: 4.9492 | Val NDCG@10: 0.001961 | Time: 7.15 s
Process Group PGID: 2928
</untrusted_context>
68

Inspect sample users history vs truth items

+38:36
Recorded reasoning summary 50 words

Investigating Performance Dip

I'm noticing a concerning trend where my NDCG metric is decreasing as the loss goes down, particularly in epoch 1. This suggests the model might be overfitting or its discovery capabilities are deteriorating. I need to pause and understand the root cause of this critical finding immediately.

Tool callrun_shell_commandInspect sample users history vs truth items Returned

Tool call · Input 

{
  "description": "Inspect sample users history vs truth items",
  "command": "python3 -c \"\nimport sys\nsys.path.insert(0, '/task')\nimport pandas as pd\nimport numpy as np\nfrom evaluate_public import validation_truth\nfrom ranking_evaluation import CandidateSet\n\nDATA = '/task/data'\nval_cand = CandidateSet(f'{DATA}/validation_eligible_candidates.npz')\ntargets = set(pd.read_parquet(f'{DATA}/target_users.parquet').uid)\ntruth = validation_truth(val_cand, targets)\n\n# For 5 random users who have labels in truth:\nimport random\nrandom.seed(42)\nsample_uids = random.sample(list(truth.keys()), 5)\n\ncat_df = pd.read_parquet(f'{DATA}/candidate_catalog.parquet')\nitem_pop = dict(zip(cat_df.item_id, cat_df.popularity_rank))\n\n# Load train listens for these sample users\ntable = pd.read_parquet(f'{DATA}/train_listens.parquet', columns=['uid', 'item_id', 'is_organic', 'played_ratio_pct'])\nsub_train = table[table.uid.isin(sample_uids)]\n\nfor uid in sample_uids:\n    print(f'=== User {uid} ===')\n    truth_items = truth[uid]\n    print(f'Truth items ({len(truth_items)}):')\n    for item, grade in list(truth_items.items())[:5]:\n        print(f'  item {item}: grade={grade}, pop_rank={item_pop.get(item, -1)}')\n\n    # User history items in train\n    u_train = sub_train[sub_train.uid == uid]\n    u_pos = u_train[(u_train.is_organic == 1) & (u_train.played_ratio_pct >= 80)]\n    print(f'Total train listens: {len(u_train)}, organic strong: {len(u_pos)}')\n    print('Recent 5 train listens:')\n    for item in u_pos.item_id.tail(5):\n        print(f'  train item {item}: pop_rank={item_pop.get(item, -1)}')\n\""
}

Tool response 

<untrusted_context>
Output: === User 234400 ===
Truth items (50):
  item 2766576: grade=1, pop_rank=50
  item 9342289: grade=1, pop_rank=336
  item 9186305: grade=1, pop_rank=591
  item 308692: grade=1, pop_rank=673
  item 5559790: grade=1, pop_rank=1514
Total train listens: 3172, organic strong: 211
Recent 5 train listens:
  train item 2166404: pop_rank=-1
  train item 1147641: pop_rank=16148
  train item 2327446: pop_rank=5997
  train item 7869263: pop_rank=-1
  train item 159001: pop_rank=68
=== User 51700 ===
Truth items (24):
  item 9340048: grade=1, pop_rank=3965
  item 8904786: grade=1, pop_rank=21587
  item 5978499: grade=3, pop_rank=23510
  item 2601846: grade=1, pop_rank=5517
  item 1530256: grade=1, pop_rank=10672
Total train listens: 8394, organic strong: 1514
Recent 5 train listens:
  train item 2408923: pop_rank=111
  train item 2906029: pop_rank=4682
  train item 2906029: pop_rank=4682
  train item 2906029: pop_rank=4682
  train item 2827453: pop_rank=7282
=== User 566200 ===
Truth items (2):
  item 6418201: grade=3, pop_rank=19955
  item 1025765: grade=3, pop_rank=4441
Total train listens: 15738, organic strong: 7873
Recent 5 train listens:
  train item 6898184: pop_rank=12922
  train item 1116794: pop_rank=11734
  train item 1391928: pop_rank=-1
  train item 6141871: pop_rank=10980
  train item 1391928: pop_rank=-1
=== User 504600 ===
Truth items (6):
  item 2424050: grade=1, pop_rank=2285
  item 2321993: grade=1, pop_rank=4150
  item 4497350: grade=3, pop_rank=12371
  item 8864588: grade=3, pop_rank=11876
  item 2026904: grade=3, pop_rank=15836
Total train listens: 858, organic strong: 262
Recent 5 train listens:
  train item 2746728: pop_rank=231
  train item 2746728: pop_rank=231
  train item 2746728: pop_rank=231
  train item 2746728: pop_rank=231
  train item 2746728: pop_rank=231
=== User 461000 ===
Truth items (2):
  item 5776316: grade=1, pop_rank=13388
  item 9385628: grade=1, pop_rank=12218
Total train listens: 938, organic strong: 291
Recent 5 train listens:
  train item 3633989: pop_rank=325
  train item 5866385: pop_rank=5174
  train item 5866385: pop_rank=5174
  train item 4544522: pop_rank=28282
  train item 4192821: pop_rank=2901
Process Group PGID: 2965
</untrusted_context>
69

Inspect hypothesis on un-updated negative candidates

+39:35
Recorded reasoning summary 42 words

Analyzing Performance Dip

I'm currently dissecting the sudden drop in prototype two-tower performance from 0.0064 to 0.0018. My focus is on scrutinizing the training loss and setup, specifically within sample_cutoff, sample_user, and sample_item, to understand the underlying cause for each training sample.

Recorded reasoning summary 42 words

Investigating Gradient Flow

I've identified a critical issue: only items present in the current batch's user listens or item targets are receiving gradient updates. This means items outside these specific contexts are not being penalized or adjusted, likely causing the performance drop.

Tool callrun_shell_commandInspect hypothesis on un-updated negative candidates Returned

Tool call · Input 

{
  "command": "python3 -c \"\nimport sys\nsys.path.insert(0, '/app/solution')\n# Let's check: if we retrieve with un-updated items vs updated items\nprint('Checking hypothesis on un-updated negatives...')\n\"",
  "description": "Inspect hypothesis on un-updated negative candidates"
}

Tool response 

<untrusted_context>
Output: Checking hypothesis on un-updated negatives...
Process Group PGID: 2994
</untrusted_context>
70

Test random catalog negatives in sampled softmax

+39:57
Recorded reasoning summary 41 words

Considering Negative Sampling Impact

I'm realizing that if a candidate item is never sampled as a negative, its embedding misses the crucial "push-away" force from users. This omission impacts how it's positioned within the embedding space, potentially affecting its future recommendations.

Recorded reasoning summary 43 words

Exploring Full Softmax

I'm now focused on the full softmax approach. With only 30,000 items in the catalog, it seems computationally feasible. This method would ensure every item receives a negative gradient, preventing any from having a "random" embedding and escaping regularization entirely.

Recorded reasoning summary 50 words

Testing Full Softmax Training

I've confirmed that full softmax training is indeed computationally viable, requiring only about 27 seconds per epoch for my dataset. This means I can achieve several epochs in just a couple of minutes, completely eliminating negative sampling bias and ensuring all item embeddings are updated consistently.

Recorded reasoning summary 42 words

Investigating Item2Vec Feasibility

I'm now exploring the potential of Item2Vec. This approach seems promising for capturing initial item embedding structure through co-occurrence from listen sequences. Critically, it avoids building a global user-item matrix, aligning with our scaling requirements for large production systems.

Tool callrun_shell_commandTest random catalog negatives in sampled softmax Returned

Tool call · Input 

{
  "command": "python3 -c \"\nimport sys\nsys.path.insert(0, '/app/solution')\nimport train_two_tower_v1 as tt\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nimport numpy as np\nimport time\n\ntarget_uids, uid_to_target_idx, item_ids, item_to_idx, item_feats, log_q, df_listens, all_likes, all_dislikes = tt.load_all_data()\n\nt_end = 24786800\nweek = 7 * 86400\nact_likes_val, act_dislikes_val = tt.get_active_prefs(all_likes, all_dislikes, t_end)\nval_user_feats = tt.extract_features(df_listens, act_likes_val, act_dislikes_val, target_uids, t_end)\n\ncutoffs = [t_end - 1 * week, t_end - 2 * week]\ncutoffs_feats = [tt.extract_features(df_listens, *tt.get_active_prefs(all_likes, all_dislikes, t_c), target_uids, t_c) for t_c in cutoffs]\n\ntargets_set = set(target_uids)\nval_cand = tt.CandidateSet(tt.DATA / 'validation_eligible_candidates.npz')\nval_unpacked = np.unpackbits(val_cand.packed, axis=1, count=30000, bitorder='little')\nval_mask = torch.from_numpy(val_unpacked).bool()\ngraded_val = tt.validation_truth(val_cand, targets_set)\n\ntrain_samples = []\nfor c_idx, t_c in enumerate(cutoffs):\n    fut_listens = df_listens[(df_listens.timestamp >= t_c) & (df_listens.timestamp < t_c + week) & (df_listens.is_organic == 1) & (df_listens.played_ratio_pct >= 80)]\n    likes_df_raw = tt.pd.read_parquet(tt.DATA / 'train_likes.parquet')\n    fut_likes = likes_df_raw[(likes_df_raw.timestamp >= t_c) & (likes_df_raw.timestamp < t_c + week) & (likes_df_raw.is_organic == 1) & (likes_df_raw.uid.isin(targets_set)) & (likes_df_raw.item_id.isin(item_to_idx))].copy()\n    fut_likes['item_idx'] = fut_likes.item_id.map(item_to_idx)\n\n    past_listens = df_listens[df_listens.timestamp < t_c]\n    past_user_items = past_listens.groupby('uid')['item_idx'].apply(set).to_dict()\n\n    for uid, item_idx in zip(fut_listens['uid'], fut_listens['item_idx']):\n        if item_idx not in past_user_items.get(uid, set()):\n            train_samples.append((c_idx, uid_to_target_idx[uid], item_idx, 1, 1.0))\n\n    for uid, item_idx in zip(fut_likes['uid'], fut_likes['item_idx']):\n        if item_idx not in past_user_items.get(uid, set()):\n            train_samples.append((c_idx, uid_to_target_idx[uid], item_idx, 3, 4.0))\n\nsample_cutoff = np.array([s[0] for s in train_samples], dtype=np.int32)\nsample_user = np.array([s[1] for s in train_samples], dtype=np.int32)\nsample_item = np.array([s[2] for s in train_samples], dtype=np.int32)\nsample_weight = np.array([s[4] for s in train_samples], dtype=np.float32)\n\nitem_feats_torch = torch.from_numpy(item_feats)\n\nmodel = tt.TwoTowerModel(num_items=30000, emb_dim=64)\noptimizer = torch.optim.AdamW(model.parameters(), lr=1e-3, weight_decay=1e-4)\n\nbatch_size = 256\nnum_negatives = 512\nnum_samples = len(sample_cutoff)\nindices = np.arange(num_samples)\n\nprint('Testing with in-batch + 512 random catalog negatives...')\nfor epoch in range(1, 4):\n    t_ep = time.time()\n    np.random.shuffle(indices)\n    model.train()\n    total_loss = 0.0\n    num_batches = 0\n\n    for start in range(0, num_samples - batch_size + 1, batch_size):\n        b_idx = indices[start:start + batch_size]\n        b_cutoff = sample_cutoff[b_idx]\n        b_user = sample_user[b_idx]\n        b_pos_items = torch.from_numpy(sample_item[b_idx])\n        b_weights = torch.from_numpy(sample_weight[b_idx])\n\n        # Random catalog negative items\n        b_neg_items = torch.randint(1, 30001, (num_negatives,))\n        b_all_items = torch.cat([b_pos_items, b_neg_items], dim=0)\n\n        b_listens = torch.zeros((batch_size, 100), dtype=torch.int64)\n        b_lweights = torch.zeros((batch_size, 100), dtype=torch.float32)\n        b_org = torch.zeros((batch_size, 50), dtype=torch.int64)\n        b_likes = torch.zeros((batch_size, 50), dtype=torch.int64)\n        b_dislikes = torch.zeros((batch_size, 20), dtype=torch.int64)\n        b_stats = torch.zeros((batch_size, 5), dtype=torch.float32)\n\n        for c in range(len(cutoffs)):\n            mask_c = (b_cutoff == c)\n            if np.any(mask_c):\n                u_c = b_user[mask_c]\n                b_listens[mask_c] = cutoffs_feats[c]['listens'][u_c]\n                b_lweights[mask_c] = cutoffs_feats[c]['listen_weights'][u_c]\n                b_org[mask_c] = cutoffs_feats[c]['org_listens'][u_c]\n                b_likes[mask_c] = cutoffs_feats[c]['likes'][u_c]\n                b_dislikes[mask_c] = cutoffs_feats[c]['dislikes'][u_c]\n                b_stats[mask_c] = cutoffs_feats[c]['user_stats'][u_c]\n\n        b_all_item_feats = item_feats_torch[b_all_items]\n\n        optimizer.zero_grad()\n        u = model.user_tower(b_listens, b_lweights, b_org, b_likes, b_dislikes, b_stats) # (B, D)\n        v = model.item_tower(b_all_items, b_all_item_feats) # (B + num_neg, D)\n\n        logit_scale = model.logit_scale.exp().clamp(max=100.0)\n        logits = torch.matmul(u, v.T) * logit_scale # (B, B + num_neg)\n\n        target = torch.arange(batch_size, dtype=torch.long)\n        loss = (F.cross_entropy(logits, target, reduction='none') * b_weights).mean()\n\n        loss.backward()\n        optimizer.step()\n        total_loss += loss.item()\n        num_batches += 1\n\n    model.eval()\n    with torch.no_grad():\n        all_indices = torch.arange(1, 30001)\n        all_feats = item_feats_torch[1:30001]\n        V_cand = model.item_tower(all_indices, all_feats)\n\n        U_val = model.user_tower(\n            val_user_feats['listens'],\n            val_user_feats['listen_weights'],\n            val_user_feats['org_listens'],\n            val_user_feats['likes'],\n            val_user_feats['dislikes'],\n            val_user_feats['user_stats']\n        )\n\n        scores = torch.matmul(U_val, V_cand.T)\n        scores[~val_mask] = -float('inf')\n        top100_idx = torch.topk(scores, 100, dim=1).indices.cpu().numpy()\n\n        ranked = {\n            target_uids[r]: list(map(int, val_cand.item_ids[top100_idx[r]]))\n            for r in range(len(target_uids))\n        }\n        res = tt.score_rankings(ranked, graded_val)\n\n    print(f'Epoch {epoch:2d} | Loss: {total_loss/num_batches:.4f} | Val NDCG@10: {res[\\\"quality_ndcg@10\\\"]:.6f} | Time: {time.time() - t_ep:.2f} s')\n\"",
  "description": "Test random catalog negatives in sampled softmax"
}

Tool response 

<untrusted_context>
Output: Loaded metadata in 0.03 s
Listens filtered: 31,062,540 in 12.21 s
Feedback loaded in 0.32 s
Features extracted for cutoff 24786800 in 2.71 s
Features extracted for cutoff 24182000 in 2.62 s
Features extracted for cutoff 23577200 in 2.54 s
Testing with in-batch + 512 random catalog negatives...
Epoch  1 | Loss: 6.7967 | Val NDCG@10: 0.006956 | Time: 9.22 s
Epoch  2 | Loss: 5.2989 | Val NDCG@10: 0.006259 | Time: 9.20 s
Epoch  3 | Loss: 4.3634 | Val NDCG@10: 0.007231 | Time: 9.71 s
Process Group PGID: 2997
</untrusted_context>
71

Inspect collaborative vs popularity trade-off

+41:26
Recorded reasoning summary 51 words

Investigating Model Performance

My latest findings show a small but positive NDCG increase to 0.007231 with the addition of 512 random catalog negatives. However, I'm currently puzzled by this result, as it still lags behind the popularity baseline's NDCG of 0.0107. I'm now examining the top recommendations to understand this discrepancy.

Tool callrun_shell_commandInspect collaborative vs popularity trade-off Returned

Tool call · Input 

{
  "command": "python3 -c \"\nimport sys\nsys.path.insert(0, '/app/solution')\n# Check popularity of truth items vs model recommended items\nimport pandas as pd\nimport numpy as np\n\ncat_df = pd.read_parquet('/task/data/candidate_catalog.parquet')\nitem_pop = dict(zip(cat_df.item_id, cat_df.popularity_rank))\n\n# Let's inspect: What does a model that combines collaborative embeddings with popularity score do?\n# If score = u . v + alpha * pop_feature\nprint('Checking combination of dot product and popularity feature...')\n\"",
  "description": "Inspect collaborative vs popularity trade-off"
}

Tool response 

<untrusted_context>
Output: Checking combination of dot product and popularity feature...
Process Group PGID: 3028
</untrusted_context>
72

Test grid search of alpha for combining dot product with popularity

+41:59
Recorded reasoning summary 36 words

Testing alpha values

I'm exploring the impact of adding alpha * pop_scores to our existing scores calculation. My current focus is on how different $\alpha$ values might affect the outcome and if this integration is feasible.

Tool callrun_shell_commandTest grid search of alpha for combining dot product with popularity Returned

Tool call · Input 

{
  "description": "Test grid search of alpha for combining dot product with popularity",
  "command": "python3 -c \"\nimport sys\nsys.path.insert(0, '/app/solution')\nimport train_two_tower_v1 as tt\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nimport numpy as np\n\n# Load data and run 1 epoch, then test various alpha combinations\ntarget_uids, uid_to_target_idx, item_ids, item_to_idx, item_feats, log_q, df_listens, all_likes, all_dislikes = tt.load_all_data()\n\nt_end = 24786800\nweek = 7 * 86400\nact_likes_val, act_dislikes_val = tt.get_active_prefs(all_likes, all_dislikes, t_end)\nval_user_feats = tt.extract_features(df_listens, act_likes_val, act_dislikes_val, target_uids, t_end)\n\ncutoffs = [t_end - 1 * week, t_end - 2 * week]\ncutoffs_feats = [tt.extract_features(df_listens, *tt.get_active_prefs(all_likes, all_dislikes, t_c), target_uids, t_c) for t_c in cutoffs]\n\ntargets_set = set(target_uids)\nval_cand = tt.CandidateSet(tt.DATA / 'validation_eligible_candidates.npz')\nval_unpacked = np.unpackbits(val_cand.packed, axis=1, count=30000, bitorder='little')\nval_mask = torch.from_numpy(val_unpacked).bool()\ngraded_val = tt.validation_truth(val_cand, targets_set)\n\ntrain_samples = []\nfor c_idx, t_c in enumerate(cutoffs):\n    fut_listens = df_listens[(df_listens.timestamp >= t_c) & (df_listens.timestamp < t_c + week) & (df_listens.is_organic == 1) & (df_listens.played_ratio_pct >= 80)]\n    likes_df_raw = tt.pd.read_parquet(tt.DATA / 'train_likes.parquet')\n    fut_likes = likes_df_raw[(likes_df_raw.timestamp >= t_c) & (likes_df_raw.timestamp < t_c + week) & (likes_df_raw.is_organic == 1) & (likes_df_raw.uid.isin(targets_set)) & (likes_df_raw.item_id.isin(item_to_idx))].copy()\n    fut_likes['item_idx'] = fut_likes.item_id.map(item_to_idx)\n\n    past_listens = df_listens[df_listens.timestamp < t_c]\n    past_user_items = past_listens.groupby('uid')['item_idx'].apply(set).to_dict()\n\n    for uid, item_idx in zip(fut_listens['uid'], fut_listens['item_idx']):\n        if item_idx not in past_user_items.get(uid, set()):\n            train_samples.append((c_idx, uid_to_target_idx[uid], item_idx, 1, 1.0))\n\n    for uid, item_idx in zip(fut_likes['uid'], fut_likes['item_idx']):\n        if item_idx not in past_user_items.get(uid, set()):\n            train_samples.append((c_idx, uid_to_target_idx[uid], item_idx, 3, 4.0))\n\nsample_cutoff = np.array([s[0] for s in train_samples], dtype=np.int32)\nsample_user = np.array([s[1] for s in train_samples], dtype=np.int32)\nsample_item = np.array([s[2] for s in train_samples], dtype=np.int32)\nsample_weight = np.array([s[4] for s in train_samples], dtype=np.float32)\n\nitem_feats_torch = torch.from_numpy(item_feats)\nmodel = tt.TwoTowerModel(num_items=30000, emb_dim=64)\noptimizer = torch.optim.AdamW(model.parameters(), lr=1e-3, weight_decay=1e-4)\n\nbatch_size = 256\nnum_negatives = 512\nnum_samples = len(sample_cutoff)\nindices = np.arange(num_samples)\n\n# Train 2 epochs\nfor epoch in range(2):\n    np.random.shuffle(indices)\n    model.train()\n    for start in range(0, num_samples - batch_size + 1, batch_size):\n        b_idx = indices[start:start + batch_size]\n        b_cutoff = sample_cutoff[b_idx]\n        b_user = sample_user[b_idx]\n        b_pos_items = torch.from_numpy(sample_item[b_idx])\n        b_weights = torch.from_numpy(sample_weight[b_idx])\n        b_neg_items = torch.randint(1, 30001, (num_negatives,))\n        b_all_items = torch.cat([b_pos_items, b_neg_items], dim=0)\n\n        b_listens = torch.zeros((batch_size, 100), dtype=torch.int64)\n        b_lweights = torch.zeros((batch_size, 100), dtype=torch.float32)\n        b_org = torch.zeros((batch_size, 50), dtype=torch.int64)\n        b_likes = torch.zeros((batch_size, 50), dtype=torch.int64)\n        b_dislikes = torch.zeros((batch_size, 20), dtype=torch.int64)\n        b_stats = torch.zeros((batch_size, 5), dtype=torch.float32)\n\n        for c in range(len(cutoffs)):\n            mask_c = (b_cutoff == c)\n            if np.any(mask_c):\n                u_c = b_user[mask_c]\n                b_listens[mask_c] = cutoffs_feats[c]['listens'][u_c]\n                b_lweights[mask_c] = cutoffs_feats[c]['listen_weights'][u_c]\n                b_org[mask_c] = cutoffs_feats[c]['org_listens'][u_c]\n                b_likes[mask_c] = cutoffs_feats[c]['likes'][u_c]\n                b_dislikes[mask_c] = cutoffs_feats[c]['dislikes'][u_c]\n                b_stats[mask_c] = cutoffs_feats[c]['user_stats'][u_c]\n\n        b_all_item_feats = item_feats_torch[b_all_items]\n        optimizer.zero_grad()\n        u = model.user_tower(b_listens, b_lweights, b_org, b_likes, b_dislikes, b_stats)\n        v = model.item_tower(b_all_items, b_all_item_feats)\n        logit_scale = model.logit_scale.exp().clamp(max=100.0)\n        logits = torch.matmul(u, v.T) * logit_scale\n        target = torch.arange(batch_size, dtype=torch.long)\n        loss = (F.cross_entropy(logits, target, reduction='none') * b_weights).mean()\n        loss.backward()\n        optimizer.step()\n\nmodel.eval()\nwith torch.no_grad():\n    all_indices = torch.arange(1, 30001)\n    all_feats = item_feats_torch[1:30001]\n    V_cand = model.item_tower(all_indices, all_feats)\n\n    U_val = model.user_tower(\n        val_user_feats['listens'],\n        val_user_feats['listen_weights'],\n        val_user_feats['org_listens'],\n        val_user_feats['likes'],\n        val_user_feats['dislikes'],\n        val_user_feats['user_stats']\n    )\n\n    dot_scores = torch.matmul(U_val, V_cand.T)\n\n    cat_df = tt.pd.read_parquet(tt.DATA / 'candidate_catalog.parquet')\n    pop = np.log1p(cat_df.positive_listens.to_numpy().astype(np.float32))\n    pop_norm = (pop - pop.mean()) / pop.std()\n    pop_tensor = torch.from_numpy(pop_norm).unsqueeze(0)\n\n    for alpha in [0.0, 0.1, 0.2, 0.3, 0.5, 0.8, 1.0, 1.5, 2.0, 5.0]:\n        scores = dot_scores + alpha * pop_tensor\n        scores[~val_mask] = -float('inf')\n        top100_idx = torch.topk(scores, 100, dim=1).indices.cpu().numpy()\n        ranked = {\n            target_uids[r]: list(map(int, val_cand.item_ids[top100_idx[r]]))\n            for r in range(len(target_uids))\n        }\n        res = tt.score_rankings(ranked, graded_val)\n        print(f'alpha={alpha:4.1f}: Val NDCG@10 = {res[\\\"quality_ndcg@10\\\"]:.6f}')\n\""
}

Tool response 

<untrusted_context>
Output: Loaded metadata in 0.03 s
Listens filtered: 31,062,540 in 12.32 s
Feedback loaded in 0.33 s
Features extracted for cutoff 24786800 in 2.67 s
Features extracted for cutoff 24182000 in 2.61 s
Features extracted for cutoff 23577200 in 2.53 s
alpha= 0.0: Val NDCG@10 = 0.005359
alpha= 0.1: Val NDCG@10 = 0.011433
alpha= 0.2: Val NDCG@10 = 0.011358
alpha= 0.3: Val NDCG@10 = 0.011134
alpha= 0.5: Val NDCG@10 = 0.011052
alpha= 0.8: Val NDCG@10 = 0.010849
alpha= 1.0: Val NDCG@10 = 0.010821
alpha= 1.5: Val NDCG@10 = 0.010758
alpha= 2.0: Val NDCG@10 = 0.010779
alpha= 5.0: Val NDCG@10 = 0.010801
Process Group PGID: 3051
</untrusted_context>
73

Benchmark PyTorch Skip-Gram on CPU

+43:22
Recorded reasoning summary 37 words

Observing Early Gains

I'm seeing promising initial results. With alpha set to 0.1, the model's Val NDCG@10 has improved to 0.011433, surpassing the popularity baseline of 0.01071. This indicates the two-tower model is successfully incorporating personalized signals.

Recorded reasoning summary 47 words

Integrating Popularity End-to-End

I've realized a way to integrate popularity naturally and end-to-end. By re-parameterizing the user and item embeddings to include the popularity term and alpha, the dot product itself now captures both personalized taste and the popularity prior, simplifying the retrieval process to pure MIPS.

Recorded reasoning summary 52 words

Refining User Representations

I'm exploring how to enhance the user tower. By incorporating more interaction data, specifically the items a user has listened to beyond just two cutoffs, I believe I can significantly strengthen the personalized component of the user embeddings. This should lead to a more robust representation and better recall.

Recorded reasoning summary 69 words

Revising Item Embedding Training

I've identified a critical issue: my item embeddings are likely not learning rich collaborative representations due to insufficient exposure. They've only been trained for two epochs, meaning many of the 30,000 items are seen very infrequently. I plan to retrain item representations using session transition data from the 31 million listens in train_listens, which will ensure every item is seen hundreds or thousands of times.

Recorded reasoning summary 45 words

Training Rich Item Embeddings

I'm focusing on improving item representations by pre-training them with Item2Vec on session sequences. This should provide dense, collaborative features, allowing user embeddings to accurately represent musical taste and improve similarity scoring. I'm checking if gensim is available for this task.

Recorded reasoning summary 45 words

Developing Item Representations

I'm exploring alternative methods for item representation learning since gensim isn't available. I'm investigating if PyTorch or SciPy can be leveraged for skip-gram or contrastive learning on streaming batches of item sequences, to generate co-occurrence embeddings without constructing a full user-item matrix.

Tool callrun_shell_commandBenchmark PyTorch Skip-Gram on CPU Returned

Tool call · Input 

{
  "command": "python3 -c \"\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nimport time\n\nnum_items = 30001\nemb_dim = 64\nbatch_size = 4096\n\ntarget_emb = nn.Embedding(num_items, emb_dim)\ncontext_emb = nn.Embedding(num_items, emb_dim)\noptimizer = torch.optim.Adam(list(target_emb.parameters()) + list(context_emb.parameters()), lr=1e-3)\n\n# 100 steps\nt0 = time.time()\nfor _ in range(100):\n    src = torch.randint(1, num_items, (batch_size,))\n    pos = torch.randint(1, num_items, (batch_size,))\n    neg = torch.randint(1, num_items, (batch_size, 5))\n\n    u = target_emb(src) # (B, D)\n    v_pos = context_emb(pos) # (B, D)\n    v_neg = context_emb(neg) # (B, 5, D)\n\n    pos_score = (u * v_pos).sum(dim=-1) # (B,)\n    neg_score = (u.unsqueeze(1) * v_neg).sum(dim=-1) # (B, 5)\n\n    loss = -F.logsigmoid(pos_score).mean() - F.logsigmoid(-neg_score).mean()\n    optimizer.zero_grad()\n    loss.backward()\n    optimizer.step()\nt1 = time.time()\n\nprint(f'100 steps of batch size {batch_size} (400k pairs): {t1 - t0:.2f} s ({(t1 - t0)/100*1000:.2f} ms/step)')\n\"",
  "description": "Benchmark PyTorch Skip-Gram on CPU"
}

Tool response 

<untrusted_context>
Output: 100 steps of batch size 4096 (400k pairs): 1.20 s (12.03 ms/step)
Process Group PGID: 3082
</untrusted_context>
74

Train Item2Vec and test discovery performance on validation set

+47:53
Recorded reasoning summary 42 words

Accelerating Through Data

I'm thrilled by the speed! Processing 400,000 transitions in just 1.2 seconds means we can tackle 3.5 million transitions in 10 seconds. This efficiency is crucial for our training, especially as I explore extracting consecutive listens from catalog tracks.

Recorded reasoning summary 48 words

Refining Discovery Metrics

My focus has shifted to a deeper understanding of relevance for the discovery task. I'm directly testing how closely co-listened tracks within a user's history align with their validation window listens, exploring the impact of Item2Vec with pooled recent track embeddings on user vector accuracy.

Tool callrun_shell_commandTrain Item2Vec and test discovery performance on validation set Returned

Tool call · Input 

{
  "description": "Train Item2Vec and test discovery performance on validation set",
  "command": "cat << 'EOF' > /app/solution/test_item2vec.py\nimport sys\nimport time\nfrom pathlib import Path\nimport numpy as np\nimport pandas as pd\nimport pyarrow.parquet as pq\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\nsys.path.insert(0, '/task')\nfrom ranking_evaluation import CandidateSet, score_rankings\nfrom evaluate_public import validation_truth\n\nDATA = Path('/task/data')\n\n# 1. Load catalog and target users\ncat_df = pd.read_parquet(DATA / 'candidate_catalog.parquet')\nitem_to_idx = {item: idx + 1 for idx, item in enumerate(cat_df.item_id)}\ntargets_df = pd.read_parquet(DATA / 'target_users.parquet')\ntarget_uids = set(targets_df.uid)\n\n# 2. Extract transitions from last 60 days of train_listens\nt_end = 24786800\nt_start = t_end - 60 * 86400\n\nt0 = time.time()\ntable = pq.read_table(DATA / 'train_listens.parquet', columns=['uid', 'timestamp', 'item_id'])\ndf = table.to_pandas()\ndf = df[(df.timestamp >= t_start) & (df.timestamp < t_end) & (df.uid.isin(target_uids)) & (df.item_id.isin(item_to_idx))].sort_values(['uid', 'timestamp'])\ndf['item_idx'] = df.item_id.map(item_to_idx)\ndf['next_idx'] = df.groupby('uid')['item_idx'].shift(-1)\ntransitions = df.dropna(subset=['next_idx']).copy()\ntransitions['next_idx'] = transitions['next_idx'].astype(int)\n# Remove self-loops\ntransitions = transitions[transitions.item_idx != transitions.next_idx]\nprint(f\"Extracted {len(transitions):,} transitions in {time.time() - t0:.2f} s\")\n\n# 3. Train Item2Vec Skip-Gram in PyTorch\nsrc_items = torch.from_numpy(transitions['item_idx'].to_numpy(dtype=np.int64))\ndst_items = torch.from_numpy(transitions['next_idx'].to_numpy(dtype=np.int64))\n\nemb_dim = 64\nnum_items = 30001\ntarget_emb = nn.Embedding(num_items, emb_dim, padding_idx=0)\ncontext_emb = nn.Embedding(num_items, emb_dim, padding_idx=0)\nnn.init.normal_(target_emb.weight, std=0.05)\nnn.init.normal_(context_emb.weight, std=0.05)\nwith torch.no_grad():\n    target_emb.weight[0].fill_(0)\n    context_emb.weight[0].fill_(0)\n\noptimizer = torch.optim.Adam(list(target_emb.parameters()) + list(context_emb.parameters()), lr=3e-3)\n\nbatch_size = 8192\nnum_transitions = len(src_items)\nindices = np.arange(num_transitions)\n\nt1 = time.time()\nprint(\"Training Skip-Gram...\")\nfor epoch in range(3):\n    np.random.shuffle(indices)\n    total_loss = 0.0\n    num_batches = 0\n    for start in range(0, num_transitions - batch_size + 1, batch_size):\n        b_idx = indices[start:start + batch_size]\n        b_src = src_items[b_idx]\n        b_pos = dst_items[b_idx]\n        b_neg = torch.randint(1, num_items, (batch_size, 5))\n\n        u = target_emb(b_src)\n        v_pos = context_emb(b_pos)\n        v_neg = context_emb(b_neg)\n\n        pos_score = (u * v_pos).sum(dim=-1)\n        neg_score = (u.unsqueeze(1) * v_neg).sum(dim=-1)\n\n        loss = -F.logsigmoid(pos_score).mean() - F.logsigmoid(-neg_score).mean()\n        optimizer.zero_grad()\n        loss.backward()\n        optimizer.step()\n\n        total_loss += loss.item()\n        num_batches += 1\n    print(f\"Epoch {epoch+1}: loss = {total_loss / num_batches:.4f}\")\n\nprint(f\"Skip-gram trained in {time.time() - t1:.2f} s\")\n\n# 4. Now evaluate a simple average user representation on validation set\n# For each target user, average recent 50 listens\nval_cand = CandidateSet(DATA / 'validation_eligible_candidates.npz')\nval_unpacked = np.unpackbits(val_cand.packed, axis=1, count=30000, bitorder='little')\nval_mask = torch.from_numpy(val_unpacked).bool()\ngraded_val = validation_truth(val_cand, target_uids)\n\nsorted_target_uids = sorted(list(target_uids))\nuid_to_r = {uid: r for r, uid in enumerate(sorted_target_uids)}\n\n# Last 50 listens per user\nrecent = df.groupby('uid').tail(50)\nuser_vectors = torch.zeros(len(sorted_target_uids), emb_dim)\nitem_embeddings = target_emb.weight.detach()\n\nfor uid, group in recent.groupby('uid'):\n    r = uid_to_r[uid]\n    items = torch.from_numpy(group['item_idx'].to_numpy())\n    vec = item_embeddings[items].mean(dim=0)\n    user_vectors[r] = F.normalize(vec, p=2, dim=0)\n\ncand_embeddings = F.normalize(item_embeddings[1:30001], p=2, dim=-1) # (30000, 64)\n\n# Pure collaborative dot product scores\ncollab_scores = torch.matmul(user_vectors, cand_embeddings.T)\n\n# Combine with popularity\npop = np.log1p(cat_df.positive_listens.to_numpy().astype(np.float32))\npop_norm = (pop - pop.mean()) / pop.std()\npop_tensor = torch.from_numpy(pop_norm).unsqueeze(0)\n\nfor alpha in [0.0, 0.05, 0.1, 0.15, 0.2, 0.3, 0.5]:\n    scores = collab_scores + alpha * pop_tensor\n    scores[~val_mask] = -float('inf')\n    top100_idx = torch.topk(scores, 100, dim=1).indices.cpu().numpy()\n    ranked = {\n        sorted_target_uids[r]: list(map(int, val_cand.item_ids[top100_idx[r]]))\n        for r in range(len(sorted_target_uids))\n    }\n    res = score_rankings(ranked, graded_val)\n    print(f\"Item2Vec + Popularity (alpha={alpha:4.2f}): NDCG@10 = {res['quality_ndcg@10']:.6f}\")\nEOF\npython3 /app/solution/test_item2vec.py\n"
}

Tool response 

<untrusted_context>
Output: Extracted 8,105,284 transitions in 7.59 s
Training Skip-Gram...
Epoch 1: loss = 1.0006
Epoch 2: loss = 0.7611
Epoch 3: loss = 0.7047
Skip-gram trained in 39.48 s
Item2Vec + Popularity (alpha=0.00): NDCG@10 = 0.005919
Item2Vec + Popularity (alpha=0.05): NDCG@10 = 0.011398
Item2Vec + Popularity (alpha=0.10): NDCG@10 = 0.012528
Item2Vec + Popularity (alpha=0.15): NDCG@10 = 0.012434
Item2Vec + Popularity (alpha=0.20): NDCG@10 = 0.011394
Item2Vec + Popularity (alpha=0.30): NDCG@10 = 0.011431
Item2Vec + Popularity (alpha=0.50): NDCG@10 = 0.011192
Process Group PGID: 3125
</untrusted_context>
75

Test zero-shot two-tower evaluation with pretrained embeddings

+49:24
Recorded reasoning summary 38 words

Exploring Item2Vec Enhancements

I've successfully integrated Item2Vec with popularity, achieving an impressive 17% relative increase in NDCG@10 compared to the baseline. This initial boost, even with a simple averaging of recent listens, suggests strong potential for further refinement.

Recorded reasoning summary 41 words

Initializing Two-Tower Embeddings

I'm now focusing on initializing the Two-Tower model's item embeddings with the Item2Vec embeddings. My next step is to incorporate recent listens, explicit preferences, and various user/item statistics into both towers before training end-to-end for graded discovery relevance.

Recorded reasoning summary 47 words

Refining Scoring Mechanism

I'm exploring how to dynamically learn user-specific scaling factors for popularity within the dot product, instead of using a fixed alpha. This approach should allow the model to fine-tune the balance between content similarity and popularity based on individual user preferences during end-to-end training.

Tool callrun_shell_commandTest zero-shot two-tower evaluation with pretrained embeddings Returned

Tool call · Input 

{
  "command": "cat << 'EOF' > /app/solution/test_two_tower_item2vec.py\nimport sys\nimport time\nfrom pathlib import Path\nimport numpy as np\nimport pandas as pd\nimport pyarrow.parquet as pq\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\nsys.path.insert(0, '/task')\nfrom ranking_evaluation import CandidateSet, score_rankings\nfrom evaluate_public import validation_truth\n\nDATA = Path('/task/data')\n\n# 1. Load catalog and target users\ncat_df = pd.read_parquet(DATA / 'candidate_catalog.parquet')\nitem_to_idx = {item: idx + 1 for idx, item in enumerate(cat_df.item_id)}\nnum_items = len(cat_df)\n\ntargets_df = pd.read_parquet(DATA / 'target_users.parquet')\ntarget_uids = sorted(list(set(targets_df.uid)))\nuid_to_r = {uid: r for r, uid in enumerate(target_uids)}\ntargets_set = set(target_uids)\n\n# Popularity feature\npop = np.log1p(cat_df.positive_listens.to_numpy().astype(np.float32))\npop_norm = (pop - pop.mean()) / pop.std()\n# 1-indexed, pad 0\npop_feat = np.pad(pop_norm, (1, 0), mode='constant')\npop_feat_torch = torch.from_numpy(pop_feat).unsqueeze(-1) # (30001, 1)\n\n# Item numerical features (pos, org_pos, org_ratio, pop_rank)\npos_f = np.log1p(cat_df.positive_listens.to_numpy().astype(np.float32))\norg_f = np.log1p(cat_df.organic_positive_listens.to_numpy().astype(np.float32))\nratio_f = (cat_df.organic_positive_listens / (cat_df.positive_listens + 1)).to_numpy().astype(np.float32)\nrank_f = (cat_df.popularity_rank / 30000.0).to_numpy().astype(np.float32)\nitem_num_feats = np.stack([pos_f, org_f, ratio_f, rank_f], axis=1)\nitem_num_feats = (item_num_feats - item_num_feats.mean(axis=0)) / (item_num_feats.std(axis=0) + 1e-6)\nitem_num_feats = np.pad(item_num_feats, ((1, 0), (0, 0)), mode='constant')\nitem_num_torch = torch.from_numpy(item_num_feats)\n\nt_end = 24786800\nt_start = t_end - 60 * 86400\nweek = 7 * 86400\n\nt0 = time.time()\ntable = pq.read_table(DATA / 'train_listens.parquet', columns=['uid', 'timestamp', 'item_id', 'is_organic', 'played_ratio_pct'])\ndf = table.to_pandas()\ndf_target = df[(df.timestamp >= t_start) & (df.timestamp < t_end) & (df.uid.isin(targets_set)) & (df.item_id.isin(item_to_idx))].sort_values(['uid', 'timestamp']).copy()\ndf_target['item_idx'] = df_target.item_id.map(item_to_idx)\nprint(f\"Loaded and filtered listens in {time.time() - t0:.2f} s\")\n\n# Extract transitions for Item2Vec pretraining\ndf_target['next_idx'] = df_target.groupby('uid')['item_idx'].shift(-1)\ntransitions = df_target.dropna(subset=['next_idx']).copy()\ntransitions['next_idx'] = transitions['next_idx'].astype(int)\ntransitions = transitions[transitions.item_idx != transitions.next_idx]\nprint(f\"Extracted {len(transitions):,} transitions.\")\n\nemb_dim = 64\nsrc_items = torch.from_numpy(transitions['item_idx'].to_numpy(dtype=np.int64))\ndst_items = torch.from_numpy(transitions['next_idx'].to_numpy(dtype=np.int64))\n\ntarget_emb = nn.Embedding(num_items + 1, emb_dim, padding_idx=0)\ncontext_emb = nn.Embedding(num_items + 1, emb_dim, padding_idx=0)\nnn.init.normal_(target_emb.weight, std=0.05)\nnn.init.normal_(context_emb.weight, std=0.05)\nwith torch.no_grad():\n    target_emb.weight[0].fill_(0)\n    context_emb.weight[0].fill_(0)\n\noptimizer_i2v = torch.optim.Adam(list(target_emb.parameters()) + list(context_emb.parameters()), lr=3e-3)\nbatch_size_i2v = 8192\nnum_transitions = len(src_items)\nindices_i2v = np.arange(num_transitions)\n\nt1 = time.time()\nprint(\"Pretraining Item2Vec (2 epochs)...\")\nfor epoch in range(2):\n    np.random.shuffle(indices_i2v)\n    for start in range(0, num_transitions - batch_size_i2v + 1, batch_size_i2v):\n        b_idx = indices_i2v[start:start + batch_size_i2v]\n        b_src = src_items[b_idx]\n        b_pos = dst_items[b_idx]\n        b_neg = torch.randint(1, num_items + 1, (batch_size_i2v, 5))\n\n        u = target_emb(b_src)\n        v_pos = context_emb(b_pos)\n        v_neg = context_emb(b_neg)\n\n        pos_score = (u * v_pos).sum(dim=-1)\n        neg_score = (u.unsqueeze(1) * v_neg).sum(dim=-1)\n\n        loss = -F.logsigmoid(pos_score).mean() - F.logsigmoid(-neg_score).mean()\n        optimizer_i2v.zero_grad()\n        loss.backward()\n        optimizer_i2v.step()\nprint(f\"Pretrained in {time.time() - t1:.2f} s\")\n\n# Now build the Two-Tower Architecture using the pretrained item embeddings\nclass ItemTower(nn.Module):\n    def __init__(self, item_emb, emb_dim):\n        super().__init__()\n        self.item_emb = item_emb\n        self.mlp = nn.Sequential(\n            nn.Linear(emb_dim + 4, emb_dim),\n            nn.LayerNorm(emb_dim),\n            nn.GELU(),\n            nn.Linear(emb_dim, emb_dim)\n        )\n\n    def forward(self, item_indices, item_num_feats, pop_feat):\n        e = self.item_emb(item_indices)\n        x = torch.cat([e, item_num_feats], dim=-1)\n        v = self.mlp(x)\n        v = F.normalize(v, p=2, dim=-1)\n        # Append popularity feature to form the full precomputable item vector\n        v_full = torch.cat([v, pop_feat], dim=-1) # (B, emb_dim + 1)\n        return v_full\n\nclass UserTower(nn.Module):\n    def __init__(self, item_emb, emb_dim):\n        super().__init__()\n        self.item_emb = item_emb\n        self.mlp = nn.Sequential(\n            nn.Linear(emb_dim * 4 + 5, emb_dim * 2),\n            nn.LayerNorm(emb_dim * 2),\n            nn.GELU(),\n            nn.Dropout(0.1),\n            nn.Linear(emb_dim * 2, emb_dim),\n            nn.LayerNorm(emb_dim)\n        )\n        self.alpha_head = nn.Sequential(\n            nn.Linear(emb_dim * 4 + 5, 32),\n            nn.ReLU(),\n            nn.Linear(32, 1),\n            nn.Softplus()\n        )\n        # Initialize alpha to ~0.1\n        with torch.no_grad():\n            self.alpha_head[-2].bias.fill_(-2.2) # softplus(-2.2) ~ 0.105\n\n    def forward(self, listens, listen_weights, org_listens, likes, dislikes, user_stats):\n        e_listens = self.item_emb(listens)\n        w = listen_weights.unsqueeze(-1)\n        sum_w = w.sum(dim=1).clamp(min=1e-5)\n        h_listens = (e_listens * w).sum(dim=1) / sum_w\n\n        e_org = self.item_emb(org_listens)\n        mask_org = (org_listens > 0).float().unsqueeze(-1)\n        sum_org = mask_org.sum(dim=1)\n        h_org = torch.where(sum_org > 0, (e_org * mask_org).sum(dim=1) / sum_org.clamp(min=1e-5), torch.zeros_like(h_listens))\n\n        e_likes = self.item_emb(likes)\n        mask_likes = (likes > 0).float().unsqueeze(-1)\n        sum_likes = mask_likes.sum(dim=1)\n        h_likes = torch.where(sum_likes > 0, (e_likes * mask_likes).sum(dim=1) / sum_likes.clamp(min=1e-5), torch.zeros_like(h_listens))\n\n        e_dislikes = self.item_emb(dislikes)\n        mask_dislikes = (dislikes > 0).float().unsqueeze(-1)\n        sum_dislikes = mask_dislikes.sum(dim=1)\n        h_dislikes = torch.where(sum_dislikes > 0, (e_dislikes * mask_dislikes).sum(dim=1) / sum_dislikes.clamp(min=1e-5), torch.zeros_like(h_listens))\n\n        h_combined = torch.cat([h_listens, h_org, h_likes, h_dislikes, user_stats], dim=-1)\n        u = self.mlp(h_combined)\n        u = F.normalize(u, p=2, dim=-1)\n        alpha = self.alpha_head(h_combined) # (B, 1)\n        u_full = torch.cat([u, alpha], dim=-1) # (B, emb_dim + 1)\n        return u_full\n\n# Build user features at validation cutoff\nimport train_two_tower_v1 as tt\nall_likes = tt.pd.read_parquet(DATA / 'train_likes.parquet')\nunlikes = tt.pd.read_parquet(DATA / 'train_unlikes.parquet')\nall_likes['event'] = 1\nunlikes['event'] = 0\nall_likes = pd.concat([all_likes[['uid', 'timestamp', 'item_id', 'event']], unlikes[['uid', 'timestamp', 'item_id', 'event']]])\nall_likes = all_likes[all_likes.uid.isin(targets_set) & all_likes.item_id.isin(item_to_idx)].copy()\nall_likes['item_idx'] = all_likes.item_id.map(item_to_idx)\n\ndislikes = tt.pd.read_parquet(DATA / 'train_dislikes.parquet')\nundislikes = tt.pd.read_parquet(DATA / 'train_undislikes.parquet')\ndislikes['event'] = 1\nundislikes['event'] = 0\nall_dislikes = pd.concat([dislikes[['uid', 'timestamp', 'item_id', 'event']], undislikes[['uid', 'timestamp', 'item_id', 'event']]])\nall_dislikes = all_dislikes[all_dislikes.uid.isin(targets_set) & all_dislikes.item_id.isin(item_to_idx)].copy()\nall_dislikes['item_idx'] = all_dislikes.item_id.map(item_to_idx)\n\nact_likes_val, act_dislikes_val = tt.get_active_prefs(all_likes, all_dislikes, t_end)\nval_user_feats = tt.extract_features(df_target, act_likes_val, act_dislikes_val, target_uids, t_end)\n\n# Validation evaluation setup\nval_cand = CandidateSet(DATA / 'validation_eligible_candidates.npz')\nval_unpacked = np.unpackbits(val_cand.packed, axis=1, count=30000, bitorder='little')\nval_mask = torch.from_numpy(val_unpacked).bool()\ngraded_val = validation_truth(val_cand, targets_set)\n\n# Evaluate initial two-tower model with pretrained embeddings\nitem_tower = ItemTower(target_emb, emb_dim)\nuser_tower = UserTower(target_emb, emb_dim)\n\n# Initialize ItemTower MLP to identity-like mapping\nwith torch.no_grad():\n    item_tower.mlp[0].weight.zero_()\n    item_tower.mlp[0].weight[:emb_dim, :emb_dim] = torch.eye(emb_dim)\n    item_tower.mlp[3].weight.zero_()\n    item_tower.mlp[3].weight[:emb_dim, :emb_dim] = torch.eye(emb_dim)\n\nitem_tower.eval()\nuser_tower.eval()\n\nwith torch.no_grad():\n    all_indices = torch.arange(1, 30001)\n    all_num = item_num_torch[1:30001]\n    all_pop = pop_feat_torch[1:30001]\n    V_cand = item_tower(all_indices, all_num, all_pop) # (30000, 65)\n\n    U_val = user_tower(\n        val_user_feats['listens'],\n        val_user_feats['listen_weights'],\n        val_user_feats['org_listens'],\n        val_user_feats['likes'],\n        val_user_feats['dislikes'],\n        val_user_feats['user_stats']\n    ) # (8456, 65)\n\n    # Dot product relevance score\n    scores = torch.matmul(U_val, V_cand.T) # (8456, 30000)\n    scores[~val_mask] = -float('inf')\n    top100_idx = torch.topk(scores, 100, dim=1).indices.cpu().numpy()\n\n    ranked = {\n        target_uids[r]: list(map(int, val_cand.item_ids[top100_idx[r]]))\n        for r in range(len(target_uids))\n    }\n    res = score_rankings(ranked, graded_val)\n    print(f\"Pretrained Two-Tower zero-shot NDCG@10: {res['quality_ndcg@10']:.6f}\")\n    print(f\"Mean learned alpha: {U_val[:, -1].mean().item():.4f}\")\nEOF\npython3 /app/solution/test_two_tower_item2vec.py\n",
  "description": "Test zero-shot two-tower evaluation with pretrained embeddings"
}

Tool response 

<untrusted_context>
Output: Loaded and filtered listens in 7.21 s
Extracted 8,105,284 transitions.
Pretraining Item2Vec (2 epochs)...
Pretrained in 26.38 s
Features extracted for cutoff 24786800 in 2.06 s
Pretrained Two-Tower zero-shot NDCG@10: 0.004407
Mean learned alpha: 0.1029
Process Group PGID: 3157
</untrusted_context>
76

Check residual connection concept

+50:18
Recorded reasoning summary 35 words

Refining Embeddings Calculations

I'm currently reviewing the normalization and matrix multiplication steps within test_item2vec.py. Specifically, I'm examining how user_vectors are normalized and then multiplied with cand_embeddings to produce collab_scores, which then become the final scores.

Recorded reasoning summary 41 words

Investigating Performance Discrepancies

I've identified a critical issue: the MLP within the UserTower was randomly initialized, completely corrupting the pretrained Item2Vec embeddings. This explains the significant performance drop. I'm now exploring options like residual connections or direct initialization to rectify this.

Tool callrun_shell_commandCheck residual connection concept Returned

Tool call · Input 

{
  "command": "python3 -c \"\nimport sys\nsys.path.insert(0, '/app/solution')\n# Test residual connection in UserTower at step 0\nprint('Testing residual connection concept...')\n\"",
  "description": "Check residual connection concept"
}

Tool response 

<untrusted_context>
Output: Testing residual connection concept...
Process Group PGID: 3189
</untrusted_context>
77

Test residual Two-Tower model at Step 0

+50:49
Recorded reasoning summary 49 words

Exploring UserTower Initialization

I'm currently investigating a specific initialization strategy for the UserTower. My focus is on setting the last layer of self.mlp to zero. I'm also considering how $h_{\text{base}}$, defined as a weighted pool of listens or a combination of listens and likes, might interact with this initialization.

Tool callrun_shell_commandTest residual Two-Tower model at Step 0 Returned

Tool call · Input 

{
  "command": "cat << 'EOF' > /app/solution/test_residual_two_tower.py\nimport sys\nimport time\nfrom pathlib import Path\nimport numpy as np\nimport pandas as pd\nimport pyarrow.parquet as pq\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\nsys.path.insert(0, '/task')\nfrom ranking_evaluation import CandidateSet, score_rankings\nfrom evaluate_public import validation_truth\n\nDATA = Path('/task/data')\n\ncat_df = pd.read_parquet(DATA / 'candidate_catalog.parquet')\nitem_to_idx = {item: idx + 1 for idx, item in enumerate(cat_df.item_id)}\nnum_items = len(cat_df)\n\ntargets_df = pd.read_parquet(DATA / 'target_users.parquet')\ntarget_uids = sorted(list(set(targets_df.uid)))\nuid_to_r = {uid: r for r, uid in enumerate(target_uids)}\ntargets_set = set(target_uids)\n\npop = np.log1p(cat_df.positive_listens.to_numpy().astype(np.float32))\npop_norm = (pop - pop.mean()) / pop.std()\npop_feat = np.pad(pop_norm, (1, 0), mode='constant')\npop_feat_torch = torch.from_numpy(pop_feat).unsqueeze(-1)\n\npos_f = np.log1p(cat_df.positive_listens.to_numpy().astype(np.float32))\norg_f = np.log1p(cat_df.organic_positive_listens.to_numpy().astype(np.float32))\nratio_f = (cat_df.organic_positive_listens / (cat_df.positive_listens + 1)).to_numpy().astype(np.float32)\nrank_f = (cat_df.popularity_rank / 30000.0).to_numpy().astype(np.float32)\nitem_num_feats = np.stack([pos_f, org_f, ratio_f, rank_f], axis=1)\nitem_num_feats = (item_num_feats - item_num_feats.mean(axis=0)) / (item_num_feats.std(axis=0) + 1e-6)\nitem_num_feats = np.pad(item_num_feats, ((1, 0), (0, 0)), mode='constant')\nitem_num_torch = torch.from_numpy(item_num_feats)\n\nt_end = 24786800\nt_start = t_end - 60 * 86400\n\ntable = pq.read_table(DATA / 'train_listens.parquet', columns=['uid', 'timestamp', 'item_id', 'is_organic', 'played_ratio_pct'])\ndf = table.to_pandas()\ndf_target = df[(df.timestamp >= t_start) & (df.timestamp < t_end) & (df.uid.isin(targets_set)) & (df.item_id.isin(item_to_idx))].sort_values(['uid', 'timestamp']).copy()\ndf_target['item_idx'] = df_target.item_id.map(item_to_idx)\n\ndf_target['next_idx'] = df_target.groupby('uid')['item_idx'].shift(-1)\ntransitions = df_target.dropna(subset=['next_idx']).copy()\ntransitions['next_idx'] = transitions['next_idx'].astype(int)\ntransitions = transitions[transitions.item_idx != transitions.next_idx]\n\nemb_dim = 64\nsrc_items = torch.from_numpy(transitions['item_idx'].to_numpy(dtype=np.int64))\ndst_items = torch.from_numpy(transitions['next_idx'].to_numpy(dtype=np.int64))\n\ntarget_emb = nn.Embedding(num_items + 1, emb_dim, padding_idx=0)\ncontext_emb = nn.Embedding(num_items + 1, emb_dim, padding_idx=0)\nnn.init.normal_(target_emb.weight, std=0.05)\nnn.init.normal_(context_emb.weight, std=0.05)\nwith torch.no_grad():\n    target_emb.weight[0].fill_(0)\n    context_emb.weight[0].fill_(0)\n\noptimizer_i2v = torch.optim.Adam(list(target_emb.parameters()) + list(context_emb.parameters()), lr=3e-3)\nbatch_size_i2v = 8192\nnum_transitions = len(src_items)\nindices_i2v = np.arange(num_transitions)\n\nprint(\"Pretraining Item2Vec (2 epochs)...\")\nfor epoch in range(2):\n    np.random.shuffle(indices_i2v)\n    for start in range(0, num_transitions - batch_size_i2v + 1, batch_size_i2v):\n        b_idx = indices_i2v[start:start + batch_size_i2v]\n        b_src = src_items[b_idx]\n        b_pos = dst_items[b_idx]\n        b_neg = torch.randint(1, num_items + 1, (batch_size_i2v, 5))\n\n        u = target_emb(b_src)\n        v_pos = context_emb(b_pos)\n        v_neg = context_emb(b_neg)\n\n        pos_score = (u * v_pos).sum(dim=-1)\n        neg_score = (u.unsqueeze(1) * v_neg).sum(dim=-1)\n\n        loss = -F.logsigmoid(pos_score).mean() - F.logsigmoid(-neg_score).mean()\n        optimizer_i2v.zero_grad()\n        loss.backward()\n        optimizer_i2v.step()\n\nclass ResidualItemTower(nn.Module):\n    def __init__(self, item_emb, emb_dim):\n        super().__init__()\n        self.item_emb = item_emb\n        self.mlp = nn.Sequential(\n            nn.Linear(emb_dim + 4, emb_dim),\n            nn.LayerNorm(emb_dim),\n            nn.GELU(),\n            nn.Linear(emb_dim, emb_dim)\n        )\n        # Zero-initialize last linear layer so residual is identity at start\n        nn.init.zeros_(self.mlp[-1].weight)\n        nn.init.zeros_(self.mlp[-1].bias)\n\n    def forward(self, item_indices, item_num_feats, pop_feat):\n        e = self.item_emb(item_indices)\n        x = torch.cat([e, item_num_feats], dim=-1)\n        delta = self.mlp(x)\n        v = F.normalize(e + delta, p=2, dim=-1)\n        return torch.cat([v, pop_feat], dim=-1)\n\nclass ResidualUserTower(nn.Module):\n    def __init__(self, item_emb, emb_dim):\n        super().__init__()\n        self.item_emb = item_emb\n        self.mlp = nn.Sequential(\n            nn.Linear(emb_dim * 4 + 5, emb_dim * 2),\n            nn.LayerNorm(emb_dim * 2),\n            nn.GELU(),\n            nn.Dropout(0.1),\n            nn.Linear(emb_dim * 2, emb_dim)\n        )\n        # Zero-initialize last linear layer\n        nn.init.zeros_(self.mlp[-1].weight)\n        nn.init.zeros_(self.mlp[-1].bias)\n\n        self.alpha = nn.Parameter(torch.tensor([0.10]))\n\n    def forward(self, listens, listen_weights, org_listens, likes, dislikes, user_stats):\n        e_listens = self.item_emb(listens)\n        w = listen_weights.unsqueeze(-1)\n        sum_w = w.sum(dim=1).clamp(min=1e-5)\n        h_listens = (e_listens * w).sum(dim=1) / sum_w\n\n        e_org = self.item_emb(org_listens)\n        mask_org = (org_listens > 0).float().unsqueeze(-1)\n        sum_org = mask_org.sum(dim=1)\n        h_org = torch.where(sum_org > 0, (e_org * mask_org).sum(dim=1) / sum_org.clamp(min=1e-5), torch.zeros_like(h_listens))\n\n        e_likes = self.item_emb(likes)\n        mask_likes = (likes > 0).float().unsqueeze(-1)\n        sum_likes = mask_likes.sum(dim=1)\n        h_likes = torch.where(sum_likes > 0, (e_likes * mask_likes).sum(dim=1) / sum_likes.clamp(min=1e-5), torch.zeros_like(h_listens))\n\n        e_dislikes = self.item_emb(dislikes)\n        mask_dislikes = (dislikes > 0).float().unsqueeze(-1)\n        sum_dislikes = mask_dislikes.sum(dim=1)\n        h_dislikes = torch.where(sum_dislikes > 0, (e_dislikes * mask_dislikes).sum(dim=1) / sum_dislikes.clamp(min=1e-5), torch.zeros_like(h_listens))\n\n        # Base user representation: weighted blend of listens and likes\n        h_base = torch.where(sum_likes > 0, 0.7 * h_listens + 0.3 * h_likes, h_listens)\n\n        h_combined = torch.cat([h_listens, h_org, h_likes, h_dislikes, user_stats], dim=-1)\n        delta = self.mlp(h_combined)\n        u = F.normalize(h_base + delta, p=2, dim=-1)\n\n        alpha_expanded = self.alpha.expand(u.shape[0], 1)\n        return torch.cat([u, alpha_expanded], dim=-1)\n\nitem_tower = ResidualItemTower(target_emb, emb_dim)\nuser_tower = ResidualUserTower(target_emb, emb_dim)\n\n# Evaluate at step 0\nimport train_two_tower_v1 as tt\nall_likes = tt.pd.read_parquet(DATA / 'train_likes.parquet')\nunlikes = tt.pd.read_parquet(DATA / 'train_unlikes.parquet')\nall_likes['event'] = 1\nunlikes['event'] = 0\nall_likes = pd.concat([all_likes[['uid', 'timestamp', 'item_id', 'event']], unlikes[['uid', 'timestamp', 'item_id', 'event']]])\nall_likes = all_likes[all_likes.uid.isin(targets_set) & all_likes.item_id.isin(item_to_idx)].copy()\nall_likes['item_idx'] = all_likes.item_id.map(item_to_idx)\n\ndislikes = tt.pd.read_parquet(DATA / 'train_dislikes.parquet')\nundislikes = tt.pd.read_parquet(DATA / 'train_undislikes.parquet')\ndislikes['event'] = 1\nundislikes['event'] = 0\nall_dislikes = pd.concat([dislikes[['uid', 'timestamp', 'item_id', 'event']], undislikes[['uid', 'timestamp', 'item_id', 'event']]])\nall_dislikes = all_dislikes[all_dislikes.uid.isin(targets_set) & all_dislikes.item_id.isin(item_to_idx)].copy()\nall_dislikes['item_idx'] = all_dislikes.item_id.map(item_to_idx)\n\nact_likes_val, act_dislikes_val = tt.get_active_prefs(all_likes, all_dislikes, t_end)\nval_user_feats = tt.extract_features(df_target, act_likes_val, act_dislikes_val, target_uids, t_end)\n\nval_cand = CandidateSet(DATA / 'validation_eligible_candidates.npz')\nval_unpacked = np.unpackbits(val_cand.packed, axis=1, count=30000, bitorder='little')\nval_mask = torch.from_numpy(val_unpacked).bool()\ngraded_val = validation_truth(val_cand, targets_set)\n\nitem_tower.eval()\nuser_tower.eval()\nwith torch.no_grad():\n    all_indices = torch.arange(1, 30001)\n    all_num = item_num_torch[1:30001]\n    all_pop = pop_feat_torch[1:30001]\n    V_cand = item_tower(all_indices, all_num, all_pop)\n\n    U_val = user_tower(\n        val_user_feats['listens'],\n        val_user_feats['listen_weights'],\n        val_user_feats['org_listens'],\n        val_user_feats['likes'],\n        val_user_feats['dislikes'],\n        val_user_feats['user_stats']\n    )\n\n    scores = torch.matmul(U_val, V_cand.T)\n    scores[~val_mask] = -float('inf')\n    top100_idx = torch.topk(scores, 100, dim=1).indices.cpu().numpy()\n\n    ranked = {\n        target_uids[r]: list(map(int, val_cand.item_ids[top100_idx[r]]))\n        for r in range(len(target_uids))\n    }\n    res = score_rankings(ranked, graded_val)\n    print(f\"Step 0 Residual Two-Tower NDCG@10: {res['quality_ndcg@10']:.6f}\")\nEOF\npython3 /app/solution/test_residual_two_tower.py\n",
  "description": "Test residual Two-Tower model at Step 0"
}

Tool response 

<untrusted_context>
Output: Pretraining Item2Vec (2 epochs)...
Features extracted for cutoff 24786800 in 2.14 s
Step 0 Residual Two-Tower NDCG@10: 0.012251
Process Group PGID: 3192
</untrusted_context>
78

Run fine-tuning of Residual Two-Tower model

+51:50
Recorded reasoning summary 42 words

Improving Residual Two-Tower Performance

I'm thrilled to report our Residual Two-Tower model has hit an NDCG@10 of 0.012251! My next focus is training on the discovery backtest samples to further boost this metric. I'm currently investigating optimal loss functions and training configurations.

Recorded reasoning summary 40 words

Refining Network Optimization

I'm exploring two distinct training objectives. One involves a graded Bayesian Personalized Ranking loss, directly optimizing relative item rankings. The second considers a sampled softmax approach with a carefully controlled learning rate schedule to prevent embedding degradation.

Tool callrun_shell_commandRun fine-tuning of Residual Two-Tower model Returned

Tool call · Input 

{
  "command": "cat << 'EOF' >> /app/solution/test_residual_two_tower.py\n\n# Now let's train the residual two-tower model with fine-tuning learning rate (1e-4)\nprint(\"Preparing discovery training samples...\")\ncutoffs = [t_end - 1 * week, t_end - 2 * week]\ncutoffs_feats = [tt.extract_features(df_target, *tt.get_active_prefs(all_likes, all_dislikes, t_c), target_uids, t_c) for t_c in cutoffs]\n\ntrain_samples = []\nfor c_idx, t_c in enumerate(cutoffs):\n    fut_listens = df_target[(df_target.timestamp >= t_c) & (df_target.timestamp < t_c + week) & (df_target.is_organic == 1) & (df_target.played_ratio_pct >= 80)]\n    likes_df_raw = tt.pd.read_parquet(DATA / 'train_likes.parquet')\n    fut_likes = likes_df_raw[(likes_df_raw.timestamp >= t_c) & (likes_df_raw.timestamp < t_c + week) & (likes_df_raw.is_organic == 1) & (likes_df_raw.uid.isin(targets_set)) & (likes_df_raw.item_id.isin(item_to_idx))].copy()\n    fut_likes['item_idx'] = fut_likes.item_id.map(item_to_idx)\n\n    past_listens = df_target[df_target.timestamp < t_c]\n    past_user_items = past_listens.groupby('uid')['item_idx'].apply(set).to_dict()\n\n    for uid, item_idx in zip(fut_listens['uid'], fut_listens['item_idx']):\n        if item_idx not in past_user_items.get(uid, set()):\n            train_samples.append((c_idx, uid_to_r[uid], item_idx, 1, 1.0))\n\n    for uid, item_idx in zip(fut_likes['uid'], fut_likes['item_idx']):\n        if item_idx not in past_user_items.get(uid, set()):\n            train_samples.append((c_idx, uid_to_r[uid], item_idx, 3, 3.0))\n\nsample_cutoff = np.array([s[0] for s in train_samples], dtype=np.int32)\nsample_user = np.array([s[1] for s in train_samples], dtype=np.int32)\nsample_item = np.array([s[2] for s in train_samples], dtype=np.int32)\nsample_weight = np.array([s[4] for s in train_samples], dtype=np.float32)\n\nclass TwoTowerWrapper(nn.Module):\n    def __init__(self, user_tower, item_tower):\n        super().__init__()\n        self.user_tower = user_tower\n        self.item_tower = item_tower\n        self.logit_scale = nn.Parameter(torch.ones([]) * np.log(1 / 0.07))\n\nmodel = TwoTowerWrapper(user_tower, item_tower)\n# Use a gentle learning rate for fine-tuning\noptimizer = torch.optim.AdamW([\n    {'params': model.user_tower.mlp.parameters(), 'lr': 3e-4},\n    {'params': model.user_tower.alpha, 'lr': 1e-3},\n    {'params': model.item_tower.mlp.parameters(), 'lr': 3e-4},\n    {'params': model.logit_scale, 'lr': 1e-3},\n    {'params': target_emb.parameters(), 'lr': 5e-5},\n], weight_decay=1e-4)\n\nbatch_size = 256\nnum_negatives = 256\nnum_samples = len(sample_cutoff)\nindices = np.arange(num_samples)\n\nprint(f\"Training on {num_samples:,} discovery samples...\")\nfor epoch in range(1, 6):\n    t_ep = time.time()\n    np.random.shuffle(indices)\n    model.train()\n    total_loss = 0.0\n    num_batches = 0\n\n    for start in range(0, num_samples - batch_size + 1, batch_size):\n        b_idx = indices[start:start + batch_size]\n        b_cutoff = sample_cutoff[b_idx]\n        b_user = sample_user[b_idx]\n        b_pos_items = torch.from_numpy(sample_item[b_idx])\n        b_weights = torch.from_numpy(sample_weight[b_idx])\n        b_neg_items = torch.randint(1, num_items + 1, (num_negatives,))\n        b_all_items = torch.cat([b_pos_items, b_neg_items], dim=0)\n\n        b_listens = torch.zeros((batch_size, 100), dtype=torch.int64)\n        b_lweights = torch.zeros((batch_size, 100), dtype=torch.float32)\n        b_org = torch.zeros((batch_size, 50), dtype=torch.int64)\n        b_likes = torch.zeros((batch_size, 50), dtype=torch.int64)\n        b_dislikes = torch.zeros((batch_size, 20), dtype=torch.int64)\n        b_stats = torch.zeros((batch_size, 5), dtype=torch.float32)\n\n        for c in range(len(cutoffs)):\n            mask_c = (b_cutoff == c)\n            if np.any(mask_c):\n                u_c = b_user[mask_c]\n                b_listens[mask_c] = cutoffs_feats[c]['listens'][u_c]\n                b_lweights[mask_c] = cutoffs_feats[c]['listen_weights'][u_c]\n                b_org[mask_c] = cutoffs_feats[c]['org_listens'][u_c]\n                b_likes[mask_c] = cutoffs_feats[c]['likes'][u_c]\n                b_dislikes[mask_c] = cutoffs_feats[c]['dislikes'][u_c]\n                b_stats[mask_c] = cutoffs_feats[c]['user_stats'][u_c]\n\n        b_all_item_feats = item_num_torch[b_all_items]\n        b_all_pop = pop_feat_torch[b_all_items]\n\n        optimizer.zero_grad()\n        u = model.user_tower(b_listens, b_lweights, b_org, b_likes, b_dislikes, b_stats) # (B, D+1)\n        v = model.item_tower(b_all_items, b_all_item_feats, b_all_pop) # (B + num_neg, D+1)\n\n        logit_scale = model.logit_scale.exp().clamp(max=50.0)\n        logits = torch.matmul(u, v.T) * logit_scale\n\n        target = torch.arange(batch_size, dtype=torch.long)\n        loss = (F.cross_entropy(logits, target, reduction='none') * b_weights).mean()\n\n        loss.backward()\n        optimizer.step()\n        total_loss += loss.item()\n        num_batches += 1\n\n    model.eval()\n    with torch.no_grad():\n        all_indices = torch.arange(1, 30001)\n        all_num = item_num_torch[1:30001]\n        all_pop = pop_feat_torch[1:30001]\n        V_cand = model.item_tower(all_indices, all_num, all_pop)\n\n        U_val = model.user_tower(\n            val_user_feats['listens'],\n            val_user_feats['listen_weights'],\n            val_user_feats['org_listens'],\n            val_user_feats['likes'],\n            val_user_feats['dislikes'],\n            val_user_feats['user_stats']\n        )\n\n        scores = torch.matmul(U_val, V_cand.T)\n        scores[~val_mask] = -float('inf')\n        top100_idx = torch.topk(scores, 100, dim=1).indices.cpu().numpy()\n\n        ranked = {\n            target_uids[r]: list(map(int, val_cand.item_ids[top100_idx[r]]))\n            for r in range(len(target_uids))\n        }\n        res = score_rankings(ranked, graded_val)\n\n    print(f\"Epoch {epoch:2d} | Loss: {total_loss/num_batches:.4f} | Val NDCG@10: {res['quality_ndcg@10']:.6f} | alpha: {model.user_tower.alpha.item():.4f} | Time: {time.time() - t_ep:.2f} s\")\nEOF\npython3 /app/solution/test_residual_two_tower.py\n",
  "description": "Run fine-tuning of Residual Two-Tower model"
}

Tool response 

<untrusted_context>
Output: Pretraining Item2Vec (2 epochs)...
Features extracted for cutoff 24786800 in 2.08 s
Step 0 Residual Two-Tower NDCG@10: 0.012576
Preparing discovery training samples...
Traceback (most recent call last):
  File "/app/solution/test_residual_two_tower.py", line 219, in <module>
    cutoffs = [t_end - 1 * week, t_end - 2 * week]
                           ^^^^
NameError: name 'week' is not defined
Exit Code: 1
Process Group PGID: 3224
</untrusted_context>
79

Fix week variable and run test_residual_two_tower.py

+52:38
Recorded reasoning summary 37 words

Improving Two-Tower NDCG

I've noticed a promising uptick in our Step 0 Residual Two-Tower NDCG@10, now at 0.012576, which already surpasses the popularity baseline. I'm addressing a definition error for week to ensure accurate calculations moving forward.

Tool callrun_shell_commandFix week variable and run test_residual_two_tower.py Returned

Tool call · Input 

{
  "description": "Fix week variable and run test_residual_two_tower.py",
  "command": "python3 -c \"\nwith open('/app/solution/test_residual_two_tower.py') as f:\n    content = f.read()\ncontent = content.replace('print(\\\"Preparing discovery training samples...\\\")', 'week = 7 * 86400\\nprint(\\\"Preparing discovery training samples...\\\")')\nwith open('/app/solution/test_residual_two_tower.py', 'w') as f:\n    f.write(content)\n\" && python3 /app/solution/test_residual_two_tower.py"
}

Tool response 

<untrusted_context>
Output: Pretraining Item2Vec (2 epochs)...
Features extracted for cutoff 24786800 in 2.13 s
Step 0 Residual Two-Tower NDCG@10: 0.012289
Preparing discovery training samples...
Features extracted for cutoff 24182000 in 2.18 s
Features extracted for cutoff 23577200 in 1.91 s
Training on 186,264 discovery samples...
Epoch  1 | Loss: 6.0922 | Val NDCG@10: 0.018379 | alpha: 0.0348 | Time: 11.41 s
Epoch  2 | Loss: 5.8486 | Val NDCG@10: 0.017824 | alpha: 0.0267 | Time: 11.76 s
Epoch  3 | Loss: 5.7303 | Val NDCG@10: 0.016309 | alpha: 0.0215 | Time: 11.75 s
Epoch  4 | Loss: 5.6321 | Val NDCG@10: 0.015916 | alpha: 0.0203 | Time: 11.72 s
Epoch  5 | Loss: 5.5469 | Val NDCG@10: 0.013939 | alpha: 0.0188 | Time: 11.79 s
Process Group PGID: 3256
</untrusted_context>
80

Inspect popularity-biased negative distribution

+54:38
Recorded reasoning summary 34 words

Observing Early Progress

I'm seeing a substantial uplift in validation NDCG@10 during Epoch 1, reaching 0.018379. This represents a significant +71.6% improvement over the baseline popularity score of 0.010710, indicating promising early learning trends.

Recorded reasoning summary 45 words

Investigating Model Behavior

I'm noticing an interesting trend: while validation NDCG@10 initially surged, later epochs showed signs of slight overfitting. I'm digging into the learning rate and negative sampling strategy as potential culprits for this shift and the resulting drop in the popularity weight (alpha).

Recorded reasoning summary 43 words

Refining Negative Sampling

I'm realizing that sampling purely random negatives allows the model to rely too heavily on popularity. This insight leads me to consider popularity-biased or in-batch negative sampling to force the model to leverage collaborative filtering and musical style more effectively.

Tool callrun_shell_commandInspect popularity-biased negative distribution Returned

Tool call · Input 

{
  "description": "Inspect popularity-biased negative distribution",
  "command": "python3 -c \"\nimport sys\nsys.path.insert(0, '/app/solution')\n# Test popularity-biased negative sampling\nimport numpy as np\nimport pandas as pd\nimport torch\n\ncat_df = pd.read_parquet('/task/data/candidate_catalog.parquet')\nprobs = (cat_df.positive_listens.to_numpy().astype(np.float32)) ** 0.75\nprobs /= probs.sum()\nprobs = torch.from_numpy(np.pad(probs, (1, 0), mode='constant'))\n\nsample = torch.multinomial(probs[1:], 1000, replacement=True) + 1\nprint('Sampled pop ranks min, mean, median, max:')\nranks = cat_df.popularity_rank.to_numpy()\nsample_ranks = ranks[sample.numpy() - 1]\nprint(f'min={sample_ranks.min()}, mean={sample_ranks.mean():.1f}, median={np.median(sample_ranks)}, max={sample_ranks.max()}')\n\""
}

Tool response 

<untrusted_context>
Output: Sampled pop ranks min, mean, median, max:
min=1, mean=9428.9, median=6980.5, max=29996
Process Group PGID: 3294
</untrusted_context>
81

Update negative sampling to pop-biased and test validation NDCG

+54:47
Recorded reasoning summary 43 words

Analyzing Sampling Techniques

My recent focus has been on evaluating sampling strategies. I've observed the median sampled popularity rank closely aligns with the ground truth, a promising indicator. Now, I'm actively testing the impact of incorporating negative sampling with $P(i)^{0.75}$ and in-batch methods.

Tool callrun_shell_commandUpdate negative sampling to pop-biased and test validation NDCG Returned

Tool call · Input 

{
  "command": "python3 -c \"\nwith open('/app/solution/test_residual_two_tower.py') as f:\n    code = f.read()\n\n# Replace uniform negative sampling with pop-biased negative sampling\nold_str = '''b_neg_items = torch.randint(1, num_items + 1, (num_negatives,))'''\nnew_str = '''# Sample negatives proportional to popularity**0.75\n        neg_probs = torch.from_numpy(cat_df.positive_listens.to_numpy().astype(np.float32) ** 0.75)\n        b_neg_items = torch.multinomial(neg_probs, num_negatives, replacement=True) + 1'''\n\nassert old_str in code\ncode = code.replace(old_str, new_str)\nwith open('/app/solution/test_residual_two_tower.py', 'w') as f:\n    f.write(code)\nprint('Updated negative sampling.')\n\" && python3 /app/solution/test_residual_two_tower.py",
  "description": "Update negative sampling to pop-biased and test validation NDCG"
}

Tool response 

<untrusted_context>
Output: Updated negative sampling.
Pretraining Item2Vec (2 epochs)...
Features extracted for cutoff 24786800 in 2.08 s
Step 0 Residual Two-Tower NDCG@10: 0.011965
Preparing discovery training samples...
Features extracted for cutoff 24182000 in 2.15 s
Features extracted for cutoff 23577200 in 1.90 s
Training on 186,264 discovery samples...
Epoch  1 | Loss: 6.1915 | Val NDCG@10: 0.013613 | alpha: 0.0321 | Time: 12.18 s
Epoch  2 | Loss: 5.9314 | Val NDCG@10: 0.013922 | alpha: 0.0252 | Time: 12.19 s
Epoch  3 | Loss: 5.8144 | Val NDCG@10: 0.012524 | alpha: 0.0226 | Time: 12.34 s
Epoch  4 | Loss: 5.7141 | Val NDCG@10: 0.012773 | alpha: 0.0191 | Time: 11.78 s
Epoch  5 | Loss: 5.6251 | Val NDCG@10: 0.011272 | alpha: 0.0184 | Time: 12.30 s
Process Group PGID: 3311
</untrusted_context>
82

Inspect user base vector blending options

+56:52
Recorded reasoning summary 42 words

Exploring Loss Variance

I'm currently investigating why uniform random negatives in the last run yielded a validation NDCG@10 of 0.018379. This seems unexpectedly high, suggesting the model might be learning something specific from this strategy, prompting a deeper dive into its behavior.

Recorded reasoning summary 43 words

Investigating Overfitting Mechanisms

My analysis suggests the model overfit after epoch one, possibly due to a fixed learning rate and insufficient validation checks. I'm now exploring strategies like learning rate decay, early stopping, and varying embedding dimensions for Item2Vec pretraining to address this.

Recorded reasoning summary 48 words

Considering Implicit Connections

I'm now thinking about how to incorporate train_likes. Unlike the sequential train_listens, likes represent direct, explicit user preferences that could offer stronger signals. I'm also re-evaluating user history pooling within ResidualUserTower, particularly how org_listens and likes are currently aggregated and weighted, to ensure optimal representation.

Tool callrun_shell_commandInspect user base vector blending options Returned

Tool call · Input 

{
  "command": "python3 -c \"\nimport sys\nsys.path.insert(0, '/app/solution')\nimport torch\nimport torch.nn.functional as F\nimport numpy as np\nimport pandas as pd\nfrom pathlib import Path\nfrom ranking_evaluation import CandidateSet, score_rankings\nfrom evaluate_public import validation_truth\n\n# Load saved Item2Vec from test_residual_two_tower\nDATA = Path('/task/data')\ntargets_df = pd.read_parquet(DATA / 'target_users.parquet')\ntarget_uids = sorted(list(set(targets_df.uid)))\ntargets_set = set(target_uids)\n\nval_cand = CandidateSet(DATA / 'validation_eligible_candidates.npz')\nval_unpacked = np.unpackbits(val_cand.packed, axis=1, count=30000, bitorder='little')\nval_mask = torch.from_numpy(val_unpacked).bool()\ngraded_val = validation_truth(val_cand, targets_set)\n\ncat_df = pd.read_parquet(DATA / 'candidate_catalog.parquet')\npop = np.log1p(cat_df.positive_listens.to_numpy().astype(np.float32))\npop_norm = (pop - pop.mean()) / pop.std()\npop_tensor = torch.from_numpy(pop_norm).unsqueeze(0)\n\n# Let's inspect weights of listens vs org_listens vs likes\nprint('Testing user base vector blending...')\n\"",
  "description": "Inspect user base vector blending options"
}

Tool response 

<untrusted_context>
Output: Traceback (most recent call last):
  File "<string>", line 9, in <module>
ModuleNotFoundError: No module named 'ranking_evaluation'
Exit Code: 1
Process Group PGID: 3349
</untrusted_context>
83

Verify validation setup

+57:02
Tool callrun_shell_commandVerify validation setup Returned

Tool call · Input 

{
  "description": "Verify validation setup",
  "command": "PYTHONPATH=/task python3 -c \"\nimport sys\nsys.path.insert(0, '/app/solution')\nimport torch\nimport torch.nn.functional as F\nimport numpy as np\nimport pandas as pd\nfrom pathlib import Path\nfrom ranking_evaluation import CandidateSet, score_rankings\nfrom evaluate_public import validation_truth\n\nDATA = Path('/task/data')\ntargets_df = pd.read_parquet(DATA / 'target_users.parquet')\ntarget_uids = sorted(list(set(targets_df.uid)))\ntargets_set = set(target_uids)\n\nval_cand = CandidateSet(DATA / 'validation_eligible_candidates.npz')\nval_unpacked = np.unpackbits(val_cand.packed, axis=1, count=30000, bitorder='little')\nval_mask = torch.from_numpy(val_unpacked).bool()\ngraded_val = validation_truth(val_cand, targets_set)\n\ncat_df = pd.read_parquet(DATA / 'candidate_catalog.parquet')\npop = np.log1p(cat_df.positive_listens.to_numpy().astype(np.float32))\npop_norm = (pop - pop.mean()) / pop.std()\npop_tensor = torch.from_numpy(pop_norm).unsqueeze(0)\nprint('Setup complete.')\n\""
}

Tool response 

<untrusted_context>
Output: Setup complete.
Process Group PGID: 3360
</untrusted_context>
84

Inspect hypothesis on alpha stability

+57:18
Recorded reasoning summary 31 words

Refining Model Performance

I'm currently analyzing the results from Epoch 1, noting the loss and validation NDCG@10. My focus remains on the directive to preserve the best submission throughout the process.

Recorded reasoning summary 58 words

Exploring Embedding Strategies

My current focus is on enhancing item embeddings. I'm investigating the impact of utilizing a larger historical dataset for Skip-Gram training, potentially expanding from 60 to 90 days of listen data, and considering the inclusion of 'like' events to generate more transition pairs. Additionally, I'm evaluating higher embedding dimensions (96 or 128) for increased expressivity.

Recorded reasoning summary 55 words

Testing Embedding Dimensions

My latest thoughts confirm that increasing embedding dimensions to 96 or 128 has negligible impact on CPU PyTorch processing time, remaining well under a second for critical matrix operations. I'm now considering how user history features, particularly dislikes, might be better represented to influence the user vector in a more nuanced way.

Recorded reasoning summary 39 words

Adjusting User Representation

I'm exploring how to incorporate dislike signals directly into the user vector, hypothesizing that subtracting a scaled dislike embedding can effectively penalize unwanted genre associations. This aims to refine the user representation further through MLP adjustments.

Recorded reasoning summary 49 words

Analyzing Popularity Impact

I'm realizing that the popularity feature isn't as helpful for distinguishing a user's true positive from in-batch negatives as initially thought. Since in-batch negatives are often other users' popular items, popularity doesn't discriminate well in that scenario, causing the model to push the popularity coefficient down.

Tool callrun_shell_commandInspect hypothesis on alpha stability Returned

Tool call · Input 

{
  "description": "Inspect hypothesis on alpha stability",
  "command": "python3 -c \"\nimport sys\nsys.path.insert(0, '/app/solution')\n# Test fixing alpha vs learning alpha\nprint('Testing hypothesis: fixing alpha prevents popularity degradation')\n\""
}

Tool response 

<untrusted_context>
Output: Testing hypothesis: fixing alpha prevents popularity degradation
Process Group PGID: 3383
</untrusted_context>
85

Test with alpha fixed at 0.10 and uniform negatives

+57:33
Recorded reasoning summary 51 words

Testing Residual Logic

I'm currently running a specific test within test_residual_two_tower.py to observe the impact of a fixed $\alpha$ value. My current focus is on evaluating the model's performance with $\alpha$ set to 0.08 during training, specifically tracking the validation NDCG across the first four epochs to see its evolutionary trajectory.

Tool callrun_shell_commandTest with alpha fixed at 0.10 and uniform negatives Returned

Tool call · Input 

{
  "description": "Test with alpha fixed at 0.10 and uniform negatives",
  "command": "python3 -c \"\nwith open('/app/solution/test_residual_two_tower.py') as f:\n    code = f.read()\n\n# Make alpha a fixed buffer or detach it, or remove alpha from optimizer\ncode = code.replace(\\\"{'params': model.user_tower.alpha, 'lr': 1e-3},\\\", \\\"\\\")\n# Revert negative sampling to uniform random negatives\nold_neg = '''# Sample negatives proportional to popularity**0.75\n        neg_probs = torch.from_numpy(cat_df.positive_listens.to_numpy().astype(np.float32) ** 0.75)\n        b_neg_items = torch.multinomial(neg_probs, num_negatives, replacement=True) + 1'''\nnew_neg = '''b_neg_items = torch.randint(1, num_items + 1, (num_negatives,))'''\nif old_neg in code:\n    code = code.replace(old_neg, new_neg)\n\nwith open('/app/solution/test_residual_two_tower.py', 'w') as f:\n    f.write(code)\nprint('Updated: alpha frozen at 0.10, uniform negatives restored.')\n\" && python3 /app/solution/test_residual_two_tower.py"
}

Tool response 

<untrusted_context>
Output: Updated: alpha frozen at 0.10, uniform negatives restored.
Pretraining Item2Vec (2 epochs)...
Features extracted for cutoff 24786800 in 2.13 s
Step 0 Residual Two-Tower NDCG@10: 0.011659
Preparing discovery training samples...
Features extracted for cutoff 24182000 in 2.15 s
Features extracted for cutoff 23577200 in 1.89 s
Training on 186,264 discovery samples...
Epoch  1 | Loss: 6.1155 | Val NDCG@10: 0.016286 | alpha: 0.1000 | Time: 11.57 s
Epoch  2 | Loss: 5.8728 | Val NDCG@10: 0.015405 | alpha: 0.1000 | Time: 11.67 s
Epoch  3 | Loss: 5.7645 | Val NDCG@10: 0.015773 | alpha: 0.1000 | Time: 11.54 s
Epoch  4 | Loss: 5.6665 | Val NDCG@10: 0.015460 | alpha: 0.1000 | Time: 11.56 s
Epoch  5 | Loss: 5.5779 | Val NDCG@10: 0.013920 | alpha: 0.1000 | Time: 11.57 s
Process Group PGID: 3392
</untrusted_context>
86

Check post-training alpha sweep

+59:32
Recorded reasoning summary 41 words

Investigating Alpha Drop

I'm digging into why Val NDCG dipped when alpha reached 0.10, especially since it was higher at around 0.035. I'm reviewing our grid search results, noting a Val NDCG of 0.011 for alpha = 0.05, which is intriguing.

Tool callrun_shell_commandCheck post-training alpha sweep Returned

Tool call · Input 

{
  "description": "Check post-training alpha sweep",
  "command": "python3 -c \"\nimport sys\nsys.path.insert(0, '/app/solution')\n# Test post-training alpha sweep on the trained model\nprint('Checking post-training alpha sweep...')\n\""
}

Tool response 

<untrusted_context>
Output: Checking post-training alpha sweep...
Process Group PGID: 3424
</untrusted_context>
Recorded session usage
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About this recording

The full recorded conversation: task prompt, agent messages, thinking text when available, tool inputs and outputs, and harness context. Empty thinking entries are labeled unavailable, not reconstructed from tool calls. Disclosures change the view; they do not shorten the content. Any truncation already present in tool output is preserved.

Steps follow the recorded model messages and reasoning events. Tool results appear with their originating call. Elapsed time starts with the first recorded event and excludes verification.

Attempt
gemini-3-8-flash-high-base-fixed-r05-3d6fca60
Recorded model
gemini-3.8-flash
Harness version
0.61.0
Session
9d86efce-f402-485a-a080-adcd3c067a19
Source SHA-256
486e3546852a05a9a93decfbf8575663e50a30093a21486dd456a63cf683faad

Session bookkeeping and encrypted reasoning or signatures are excluded from the reading export. The harness’s system prompt is not present in this session archive.

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