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Basic recommender gemini-3-8-flash-high-01

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

This recording includes 65 readable reasoning summaries, shown in full below.

Agent
gemini-3.8-flash · Gemini CLI · high effort
Recorded conversation
29:28 · 56 steps
Private reward
0.009092 · Failed
Attempt cost
$0.57
  • 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/ ├───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 music-discovery ranker

Build the strongest ranking model you can for the "Made for You" shelf of a music streaming service. Per-user candidate eligibility, cutoff-specific explicit preference state, and a temporal backtest are provided. Candidate eligibility is platform-owned; all modeling within that candidate boundary is in scope.

Data

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

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

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, fitted representation, example submission, or modeling architecture is supplied. Build and tune the best model you can, then refit the selected approach as described above.

Deliverable

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

Leave the code that builds the artifact and a concise /app/solution/README.md in /app/solution/. Keep the best valid submission saved throughout the attempt so it survives the deadline.

Constraints

  • Write deliverables and intermediate artifacts only under /app/solution/.
  • Treat /task/ and the installed Python environment as read-only.
  • Do not invoke nested agents or attempt to access verifier files, credentials, other trials, or host paths.
  • Do not install packages or use network access except for the model provider connection managed by Harbor.
  • You have up to 30 minutes. Improve the model as much as possible within that limit. Structural validity alone is not sufficient; the final submission is also evaluated for ranking quality.
03

update_topic command

+00:06
Recorded reasoning summary 42 words

Understanding Core Requirements

I'm currently focusing on grasping the prompt, environment, mandates, and the tools at my disposal. My objective is to operate in YOLO mode, adhering strictly to the provided guidelines and ensuring all deliverables are placed within the /app/solution/ directory.

Tool callupdate_topicTool call Returned

Tool call · Input 

{
  "title": "Initial Exploration & Baseline Setup",
  "strategic_intent": "Explore environment, inspect evaluation scripts and data sizes, and establish a baseline.",
  "summary": "Starting the music-discovery ranker project. First, inspect evaluation scripts, dataset sizes, hardware resources, and requirements. Then implement an initial baseline submission to ensure a valid artifact exists early."
}

Tool response 

Current topic: "Initial Exploration & Baseline Setup"
Topic summary: Starting the music-discovery ranker project. First, inspect evaluation scripts, dataset sizes, hardware resources, and requirements. Then implement an initial baseline submission to ensure a valid artifact exists early.

Strategic Intent: Explore environment, inspect evaluation scripts and data sizes, and establish a baseline.
Tool callread_file/task/evaluate_public.py Returned

Tool call · Input 

{
  "file_path": "/task/evaluate_public.py",
  "end_line": 100,
  "start_line": 1
}

Tool response 


IMPORTANT: The file content has been truncated.
Status: Showing lines 1-100 of 158 total lines.
Action: To read more of the file, you can use the 'start_line' and 'end_line' parameters in a subsequent 'read_file' call. For example, to read the next section of the file, use start_line: 101.

--- FILE CONTENT (truncated) ---
#!/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.
Tool callread_file/task/ranking_evaluation.py Returned

Tool call · Input 

{
  "end_line": 150,
  "file_path": "/task/ranking_evaluation.py",
  "start_line": 1
}

Tool response 


IMPORTANT: The file content has been truncated.
Status: Showing lines 1-150 of 232 total lines.
Action: To read more of the file, you can use the 'start_line' and 'end_line' parameters in a subsequent 'read_file' call. For example, to read the next section of the file, use start_line: 151.

--- FILE CONTENT (truncated) ---
"""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"
Tool callread_file/task/requirements.txt Returned

Tool call · Input 

{
  "start_line": 1,
  "file_path": "/task/requirements.txt",
  "end_line": 100
}

Tool response 

# Exact third-party distribution inventory for yambda-discovery-ranking-v01.
#
# This is an input to `uv pip sync`, not a list of minimum requirements. A
# conforming task environment contains exactly these third-party Python
# distributions and no others. The same inventory is used for source preparation,
# task building, reference scoring, judging, and solver attempts. Python itself
# is pinned by task.toml and both Harbor image Dockerfiles.
attrs==26.1.0
bleach==6.4.0
certifi==2026.7.22
chardet==7.6.0
charset-normalizer==3.5.1
fastjsonschema==2.22.2
idna==3.18
joblib==1.5.3
jsonschema==4.26.0
jsonschema-specifications==2025.9.1
jupyter-core==5.9.1
jupytext==1.19.5
kaggle==2.2.4
kagglesdk==0.1.37
markdown-it-py==4.2.0
mdit-py-plugins==0.6.1
mdurl==0.1.2
nbformat==5.11.1
numpy==2.2.6
packaging==26.3
pandas==2.3.3
pillow==12.3.0
pip==26.0.1
platformdirs==4.11.3
protobuf==7.35.1
pyarrow==18.1.0
python-dateutil==2.9.0.post0
python-dotenv==1.2.3
python-slugify==8.0.4
pytz==2026.3.post1
PyYAML==6.0.3
referencing==0.37.0
reportlab==4.2.5
requests==2.34.2
rpds-py==2026.6.3
scikit-learn==1.7.2
scipy==1.15.3
six==1.17.0
text-unidecode==1.3
threadpoolctl==3.6.0
tqdm==4.70.0
traitlets==5.16.1
typing-extensions==4.16.0
tzdata==2026.3
urllib3==2.7.0
webencodings==0.6.1
Tool callrun_shell_commandCheck CPU, RAM, GPU availability and data file sizes Returned

Tool call · Input 

{
  "description": "Check CPU, RAM, GPU availability and data file sizes",
  "command": "lscpu | grep \"Model name\\|CPU(s):\"; free -h; nvidia-smi || true; ls -lh /task/data/"
}

Tool response 

<untrusted_context>
Output: CPU(s):                                  8
Model name:                              AMD EPYC 7R13 Processor
NUMA node0 CPU(s):                       0-7
/usr/bin/bash: line 4: free: command not found
/usr/bin/bash: line 4: nvidia-smi: command not found
total 340M
-r--r--r-- 1 root root 342K Sep 29 03:30 candidate_catalog.parquet
-r--r--r-- 1 root root  83K Sep 29 03:30 target_users.parquet
-r--r--r-- 1 root root 7.4M Sep 29 03:31 test_eligible_candidates.npz
-r--r--r-- 1 root root 1.1M Sep 29 03:31 test_preference_state.parquet
-r--r--r-- 1 root root 838K Sep 29 03:30 train_dislikes.parquet
-r--r--r-- 1 root root 5.2M Sep 29 03:30 train_likes.parquet
-r--r--r-- 1 root root 305M Sep 29 03:30 train_listens.parquet
-r--r--r-- 1 root root 153K Sep 29 03:30 train_undislikes.parquet
-r--r--r-- 1 root root 1.8M Sep 29 03:30 train_unlikes.parquet
-r--r--r-- 1 root root  36K Sep 29 03:30 val_dislikes.parquet
-r--r--r-- 1 root root 292K Sep 29 03:30 val_likes.parquet
-r--r--r-- 1 root root 9.5M Sep 29 03:30 val_listens.parquet
-r--r--r-- 1 root root 7.7K Sep 29 03:30 val_undislikes.parquet
-r--r--r-- 1 root root  71K Sep 29 03:30 val_unlikes.parquet
-r--r--r-- 1 root root 7.3M Sep 29 03:30 validation_eligible_candidates.npz
-r--r--r-- 1 root root 1.1M Sep 29 03:31 validation_preference_state.parquet
Process Group PGID: 1612
</untrusted_context>
04

Check total RAM from /proc/meminfo

+00:10
Tool callrun_shell_commandCheck total RAM from /proc/meminfo Returned

Tool call · Input 

{
  "description": "Check total RAM from /proc/meminfo",
  "command": "cat /proc/meminfo | grep MemTotal"
}

Tool response 

<untrusted_context>
Output: MemTotal:       15988968 kB
Process Group PGID: 1619
</untrusted_context>
Tool callread_file/task/evaluate_public.py Returned

Tool call · Input 

{
  "file_path": "/task/evaluate_public.py",
  "end_line": 160,
  "start_line": 100
}

Tool response 


IMPORTANT: The file content has been truncated.
Status: Showing lines 100-158 of 158 total lines.
Action: To read more of the file, you can use the 'start_line' and 'end_line' parameters in a subsequent 'read_file' call. For example, to read the next section of the file, use start_line: 159.

--- FILE CONTENT (truncated) ---
    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",
  "start_line": 150,
  "end_line": 232
}

Tool response 


IMPORTANT: The file content has been truncated.
Status: Showing lines 150-232 of 232 total lines.
Action: To read more of the file, you can use the 'start_line' and 'end_line' parameters in a subsequent 'read_file' call. For example, to read the next section of the file, use start_line: 233.

--- FILE CONTENT (truncated) ---
            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

Check available ML libraries in python3

+00:27
Recorded reasoning summary 47 words

Analyzing Hardware Limitations

My current thinking is focused on optimizing for the available hardware. I'm noting the absence of GPUs and the substantial core count of AMD EPYC CPUs, alongside the 30-minute time constraint. The available libraries are also being considered as I plan the next steps.

Tool callrun_shell_commandCheck available ML libraries in python3 Returned

Tool call · Input 

{
  "description": "Check available ML libraries in python3",
  "command": "python3 -c \"for pkg in ['scipy', 'sklearn', 'lightgbm', 'xgboost', 'torch', 'implicit']:\n    try:\n        __import__(pkg)\n        print(pkg, 'AVAILABLE')\n    except ImportError:\n        print(pkg, 'NOT available')\n\""
}

Tool response 

<untrusted_context>
Output: scipy AVAILABLE
sklearn AVAILABLE
lightgbm NOT available
xgboost NOT available
torch NOT available
implicit NOT available
Process Group PGID: 1623
</untrusted_context>
06

Inspect target users, catalog, and eligible candidate shapes

+00:33
Tool callrun_shell_commandInspect target users, catalog, and eligible candidate shapes 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')\nprint('targets count:', len(targets), targets.columns.tolist())\ncatalog = pd.read_parquet('/task/data/candidate_catalog.parquet')\nprint('catalog count:', len(catalog), catalog.columns.tolist())\n\nval_npz = np.load('/task/data/validation_eligible_candidates.npz')\nprint('val_npz keys:', list(val_npz.keys()), 'uids len:', len(val_npz['uids']), 'items len:', len(val_npz['item_ids']), 'packed shape:', val_npz['packed_eligible'].shape)\n\ntest_npz = np.load('/task/data/test_eligible_candidates.npz')\nprint('test_npz keys:', list(test_npz.keys()), 'uids len:', len(test_npz['uids']), 'items len:', len(test_npz['item_ids']), 'packed shape:', test_npz['packed_eligible'].shape)\n\"",
  "description": "Inspect target users, catalog, and eligible candidate shapes"
}

Tool response 

<untrusted_context>
Output: targets count: 8456 ['uid', 'recent_positive_listens', 'history_positive_listens']
catalog count: 30000 ['item_id', 'positive_listens', 'organic_positive_listens', 'popularity_rank']
val_npz keys: ['uids', 'item_ids', 'packed_eligible'] uids len: 8456 items len: 30000 packed shape: (8456, 3750)
test_npz keys: ['uids', 'item_ids', 'packed_eligible'] uids len: 8456 items len: 30000 packed shape: (8456, 3750)
Process Group PGID: 1647
</untrusted_context>
07

Check alignment and eligible candidate statistics

+00:43
Recorded reasoning summary 60 words

Verifying Data Integrity

I'm currently digging into the details of our user and item data. My immediate focus is on confirming the alignment between the user IDs in our NPZ files and the target_users.parquet file. I also need to determine the average number of eligible items per user. I'll be running an exploratory script shortly to gather these crucial metrics.

Tool callrun_shell_commandCheck alignment and eligible candidate statistics 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')\nval_npz = np.load('/task/data/validation_eligible_candidates.npz')\ntest_npz = np.load('/task/data/test_eligible_candidates.npz')\n\nprint('targets equals val uids:', np.array_equal(targets.uid.values, val_npz['uids']))\nprint('targets equals test uids:', np.array_equal(targets.uid.values, test_npz['uids']))\nprint('val items equals test items:', np.array_equal(val_npz['item_ids'], test_npz['item_ids']))\n\n# Unpack eligible bits count\npacked = val_npz['packed_eligible']\n# count bits set per user\nbits_per_user = np.unpackbits(packed, axis=1)[:, :30000].sum(axis=1)\nprint(f'Eligible items per user in val: min={bits_per_user.min()}, max={bits_per_user.max()}, mean={bits_per_user.mean():.1f}, median={np.median(bits_per_user)}')\n\"",
  "description": "Check alignment and eligible candidate statistics"
}

Tool response 

<untrusted_context>
Output: targets equals val uids: True
targets equals test uids: True
val items equals test items: True
Eligible items per user in val: min=24657, max=30000, mean=29067.3, median=29242.0
Process Group PGID: 1670
</untrusted_context>
08

Check if catalog items match val_npz item_ids

+00:52
Recorded reasoning summary 38 words

Confirming Data Consistency

I've confirmed the exact alignment across targets.uid.values, val_npz['uids'], and test_npz['uids']. Furthermore, val_npz['item_ids'] and test_npz['item_ids'] are identical, encompassing 30,000 items. The next step is to verify that the catalog's item_id aligns with these found in val_npz['item_ids'].

Tool callrun_shell_commandCheck if catalog items match val_npz item_ids Returned

Tool call · Input 

{
  "command": "python3 -c \"\nimport pandas as pd\nimport numpy as np\n\ncatalog = pd.read_parquet('/task/data/candidate_catalog.parquet')\nval_npz = np.load('/task/data/validation_eligible_candidates.npz')\nprint('catalog items equal val_npz item_ids:', np.array_equal(catalog.item_id.values, val_npz['item_ids']))\n\"",
  "description": "Check if catalog items match val_npz item_ids"
}

Tool response 

<untrusted_context>
Output: catalog items equal val_npz item_ids: True
Process Group PGID: 1686
</untrusted_context>
09

Evaluate catalog popularity baseline on validation set

+00:57
Recorded reasoning summary 48 words

Confirming Data Source

I'm verifying that the item_id values from the catalog directly correspond to the item_ids found within the val_npz data. My current focus is on understanding the candidate_catalog.parquet file, which appears to be a curated list of 30,000 tracks based on their listening popularity during training.

Tool callrun_shell_commandEvaluate catalog popularity baseline on validation set Returned

Tool call · Input 

{
  "command": "python3 -c \"\nimport numpy as np\nimport pandas as pd\nfrom ranking_evaluation import CandidateSet, score_rankings\nfrom evaluate_public import validation_truth\n\ntargets_frame = pd.read_parquet('/task/data/target_users.parquet')\ntargets = set(targets_frame.uid.astype(int))\neligibility = CandidateSet('/task/data/validation_eligible_candidates.npz')\ngraded = validation_truth(eligibility, targets)\n\n# Test top 100 eligible by catalog popularity\ncatalog = pd.read_parquet('/task/data/candidate_catalog.parquet')\nitem_ids = catalog.item_id.values\n\npacked = eligibility.packed\nuids = eligibility.uids\n\n# For each user, find first 100 set bits\nranked = {}\nfor i, uid in enumerate(uids):\n    bits = np.unpackbits(packed[i], count=30000, bitorder='little')\n    eligible_indices = np.where(bits == 1)[0][:100]\n    ranked[int(uid)] = list(item_ids[eligible_indices])\n\nres = score_rankings(ranked, graded)\nprint('Catalog popularity baseline:', res)\n\"",
  "description": "Evaluate catalog popularity baseline on validation set"
}

Tool response 

<untrusted_context>
Output: Traceback (most recent call last):
  File "<string>", line 4, in <module>
ModuleNotFoundError: No module named 'ranking_evaluation'
Exit Code: 1
Process Group PGID: 1709
</untrusted_context>
10

Evaluate catalog popularity baseline on validation set

+01:01
Tool callrun_shell_commandEvaluate catalog popularity baseline on validation set Returned

Tool call · Input 

{
  "description": "Evaluate catalog popularity baseline on validation set",
  "command": "python3 -c \"\nimport sys\nsys.path.insert(0, '/task')\nimport numpy as np\nimport pandas as pd\nfrom ranking_evaluation import CandidateSet, score_rankings\nfrom evaluate_public import validation_truth\n\ntargets_frame = pd.read_parquet('/task/data/target_users.parquet')\ntargets = set(targets_frame.uid.astype(int))\neligibility = CandidateSet('/task/data/validation_eligible_candidates.npz')\ngraded = validation_truth(eligibility, targets)\n\ncatalog = pd.read_parquet('/task/data/candidate_catalog.parquet')\nitem_ids = catalog.item_id.values\n\npacked = eligibility.packed\nuids = eligibility.uids\n\nranked = {}\nfor i, uid in enumerate(uids):\n    bits = np.unpackbits(packed[i], count=30000, bitorder='little')\n    eligible_indices = np.where(bits == 1)[0][:100]\n    ranked[int(uid)] = list(item_ids[eligible_indices])\n\nres = score_rankings(ranked, graded)\nprint('Catalog popularity baseline:', res)\n\""
}

Tool response 

<untrusted_context>
Output: Catalog popularity baseline: {'quality_ndcg@10': 0.01071, 'quality_users': 4263}
Process Group PGID: 1720
</untrusted_context>
11

Generate initial baseline submission and verify test eligibility

+01:08
Recorded reasoning summary 39 words

Creating Baseline Submission

I've just achieved an NDCG@10 of 0.01071 with catalog popularity, involving 4263 users. My immediate next step is to generate a valid submission file, /app/solution/submission.parquet, based on this baseline, ensuring it adheres to the specified contract.

Tool callrun_shell_commandGenerate initial baseline submission and verify test eligibility Returned

Tool call · Input 

{
  "command": "python3 -c \"\nimport sys\nsys.path.insert(0, '/task')\nfrom pathlib import Path\nimport numpy as np\nimport pandas as pd\nfrom ranking_evaluation import CandidateSet, validate_ranking\n\ntest_npz = CandidateSet(Path('/task/data/test_eligible_candidates.npz'))\ntargets = pd.read_parquet('/task/data/target_users.parquet')\ntarget_set = set(targets.uid.astype(int))\n\nrows = []\nfor i, uid in enumerate(test_npz.uids):\n    bits = np.unpackbits(test_npz.packed[i], count=30000, bitorder='little')\n    chosen_items = test_npz.item_ids[np.where(bits == 1)[0][:100]]\n    for rank, item in enumerate(chosen_items, 1):\n        rows.append((uid, item, rank))\n\ndf = pd.DataFrame(rows, columns=['uid', 'item_id', 'rank'])\nvalidated = validate_ranking(df, target_set)\nprint('Validation passed! Row count:', len(validated))\nfor uid, item in df[['uid', 'item_id']].itertuples(index=False, name=None):\n    if not test_npz.is_eligible(uid, item):\n        raise ValueError(f'Ineligible item: {uid}, {item}')\nprint('All items verified eligible!')\nPath('/app/solution').mkdir(parents=True, exist_ok=True)\ndf.to_parquet('/app/solution/submission.parquet', index=False)\nprint('Baseline submission.parquet saved successfully!')\n\"",
  "description": "Generate initial baseline submission and verify test eligibility"
}

Tool response 

<untrusted_context>
Output: Validation passed! Row count: 845600
All items verified eligible!
Baseline submission.parquet saved successfully!
Process Group PGID: 1740
</untrusted_context>
12

Run evaluate_public.py on validation baseline

+01:15
Recorded reasoning summary 35 words

Establishing Initial Baseline

I've successfully generated a valid baseline submission file. My next step is to confirm the integrity of the evaluate_public.py script by running it against a validation version of this newly created baseline.

Tool callrun_shell_commandRun evaluate_public.py on validation baseline Returned

Tool call · Input 

{
  "description": "Run evaluate_public.py on validation baseline",
  "command": "python3 -c \"\nimport sys\nsys.path.insert(0, '/task')\nfrom pathlib import Path\nimport numpy as np\nimport pandas as pd\nfrom ranking_evaluation import CandidateSet\n\nval_npz = CandidateSet(Path('/task/data/validation_eligible_candidates.npz'))\nrows = []\nfor i, uid in enumerate(val_npz.uids):\n    bits = np.unpackbits(val_npz.packed[i], count=30000, bitorder='little')\n    chosen_items = val_npz.item_ids[np.where(bits == 1)[0][:100]]\n    for rank, item in enumerate(chosen_items, 1):\n        rows.append((uid, item, rank))\n\ndf = pd.DataFrame(rows, columns=['uid', 'item_id', 'rank'])\ndf.to_parquet('/app/solution/val_baseline.parquet', index=False)\n\" && python3 /task/evaluate_public.py /app/solution/val_baseline.parquet && rm /app/solution/val_baseline.parquet"
}

Tool response 

<untrusted_context>
Output: {
  "model": "/app/solution/val_baseline.parquet",
  "scores": {
    "quality_ndcg@10": 0.01071,
    "quality_users": 4263
  }
}
Process Group PGID: 1756
</untrusted_context>
13

update_topic command

+01:23
Tool callupdate_topicTool call Returned

Tool call · Input 

{
  "title": "Data Analysis & Recommender Architecture Design",
  "strategic_intent": "Analyze interaction datasets, preference state, user history, and design collaborative filtering / recommender models.",
  "summary": "We established an initial valid baseline submission scoring 0.01071 on public validation. Now, we will analyze the interaction datasets (train_listens, likes, dislikes, preference states), examine timestamps, candidate sets, and design candidate ranking architectures (e.g. item-item collaborative filtering, matrix factorization, implicit feedback, linear models) suitable for music discovery."
}

Tool response 

Current topic: "Data Analysis & Recommender Architecture Design"
Topic summary: We established an initial valid baseline submission scoring 0.01071 on public validation. Now, we will analyze the interaction datasets (train_listens, likes, dislikes, preference states), examine timestamps, candidate sets, and design candidate ranking architectures (e.g. item-item collaborative filtering, matrix factorization, implicit feedback, linear models) suitable for music discovery.

Strategic Intent: Analyze interaction datasets, preference state, user history, and design collaborative filtering / recommender models.
14

Inspect timestamps, event counts, and preference state tables

+01:31
Recorded reasoning summary 35 words

Exploring Data Properties

I'm currently inspecting the train_listens.parquet file, focusing on its row and column counts, timestamp range, and distributions for played_ratio_pct and is_organic. This will help me understand the fundamental characteristics of this dataset.

Tool callrun_shell_commandInspect timestamps, event counts, and preference state tables Returned

Tool call · Input 

{
  "description": "Inspect timestamps, event counts, and preference state tables",
  "command": "python3 -c \"\nimport pandas as pd\nimport numpy as np\n\nfor name in ['train_listens', 'train_likes', 'train_dislikes', 'val_listens', 'val_likes']:\n    df = pd.read_parquet(f'/task/data/{name}.parquet')\n    print(f'{name}: shape={df.shape}, min_ts={df.timestamp.min()}, max_ts={df.timestamp.max()}, unique_uids={df.uid.nunique()}, unique_items={df.item_id.nunique()}')\n\npref = pd.read_parquet('/task/data/validation_preference_state.parquet')\nprint('validation_preference_state:', pref.shape, pref.head(5))\ntest_pref = pd.read_parquet('/task/data/test_preference_state.parquet')\nprint('test_preference_state:', test_pref.shape, test_pref.head(5))\n\""
}

Tool response 

<untrusted_context>
Output: train_listens: shape=(43484159, 6), min_ts=0, max_ts=24786795, unique_uids=9238, unique_items=845395
train_likes: shape=(814831, 4), min_ts=80, max_ts=24786790, unique_uids=8144, unique_items=172303
train_dislikes: shape=(101075, 4), min_ts=75, max_ts=24786580, unique_uids=5732, unique_items=50876
val_listens: shape=(1501697, 6), min_ts=24788600, max_ts=25393395, unique_uids=8196, unique_items=191747
val_likes: shape=(37795, 4), min_ts=24788815, max_ts=25393390, unique_uids=4114, unique_items=22902
validation_preference_state: (469124, 3)    uid  item_id  state
0  100   410868  liked
1  100   732449  liked
2  100   982903  liked
3  100  1730273  liked
4  100  2621343  liked
test_preference_state: (484776, 3)    uid  item_id  state
0  100   410868  liked
1  100   732449  liked
2  100   982903  liked
3  100  1730273  liked
4  100  2621343  liked
Process Group PGID: 1786
</untrusted_context>
15

Check relationship between preference state and candidate eligibility

+01:44
Recorded reasoning summary 50 words

Analyzing User Data

I'm currently examining the train_listens dataset. It's quite large, with over 43 million entries, yet it represents a surprisingly small cohort of approximately 9,000 unique users, which closely matches the target user count. This suggests a deep dive into user behavior within this specific group is warranted.

Recorded reasoning summary 47 words

Investigating Candidate Data

My analysis confirms the 30,000 candidate items are consistent across datasets. I'm now focused on understanding the criteria for "candidate eligibility" – why some items are not accessible to specific users, and I'm digging into the ranking_evaluation.py script for clues on this selective availability.

Tool callrun_shell_commandCheck relationship between preference state and candidate eligibility Returned

Tool call · Input 

{
  "description": "Check relationship between preference state and candidate eligibility",
  "command": "python3 -c \"\nimport pandas as pd\nimport numpy as np\nfrom ranking_evaluation import CandidateSet\n\nval_npz = CandidateSet('/task/data/validation_eligible_candidates.npz')\npref = pd.read_parquet('/task/data/validation_preference_state.parquet')\nprint('Preference state values:', pref.state.value_counts())\n\n# Check user 100\nuid = 100\nuser_pref = pref[pref.uid == uid]\nprint(f'User 100 pref count: {len(user_pref)}')\nineligible_count = 0\nfor item in user_pref.item_id:\n    if not val_npz.is_eligible(uid, item):\n        ineligible_count += 1\nprint(f'User 100 pref items ineligible: {ineligible_count} / {len(user_pref)}')\n\""
}

Tool response 

<untrusted_context>
Output: Traceback (most recent call last):
  File "<string>", line 4, in <module>
ModuleNotFoundError: No module named 'ranking_evaluation'
Exit Code: 1
Process Group PGID: 1806
</untrusted_context>
16

Check preference state counts and eligibility

+01:50
Tool callrun_shell_commandCheck preference state counts and eligibility Returned

Tool call · Input 

{
  "command": "python3 -c \"\nimport sys\nsys.path.insert(0, '/task')\nimport pandas as pd\nimport numpy as np\nfrom ranking_evaluation import CandidateSet\n\nval_npz = CandidateSet('/task/data/validation_eligible_candidates.npz')\npref = pd.read_parquet('/task/data/validation_preference_state.parquet')\nprint('Preference state values:\\n', pref.state.value_counts())\n\n# Check user 100\nuid = 100\nuser_pref = pref[pref.uid == uid]\nprint(f'User 100 pref count: {len(user_pref)}')\nineligible_count = sum(not val_npz.is_eligible(uid, item) for item in user_pref.item_id)\nprint(f'User 100 pref items ineligible: {ineligible_count} / {len(user_pref)}')\n\"",
  "description": "Check preference state counts and eligibility"
}

Tool response 

<untrusted_context>
Output: Preference state values:
 state
liked       418435
disliked     50689
Name: count, dtype: int64
User 100 pref count: 15
User 100 pref items ineligible: 15 / 15
Process Group PGID: 1823
</untrusted_context>
17

Inspect validation truth distributions

+01:58
Recorded reasoning summary 33 words

Refining Eligibility Filters

I'm focusing on how the existing candidate eligibility mask already handles exclusions. This means I don't need to reinvent eligibility logic, as the platform provides validation_eligible_candidates.npz which should cover it.

Recorded reasoning summary 35 words

Prioritizing Eligibility Checks

I'm confirming that the existing candidate eligibility mask, specifically validation_eligible_candidates.npz, adequately covers exclusions. This means I can leverage this for ranking eligible candidates per user, rather than developing custom logic for it.

Tool callrun_shell_commandInspect validation truth distributions Returned

Tool call · Input 

{
  "description": "Inspect validation truth distributions",
  "command": "python3 -c \"\nimport sys\nsys.path.insert(0, '/task')\nimport pandas as pd\nimport numpy as np\nfrom ranking_evaluation import CandidateSet\nfrom evaluate_public import validation_truth\n\ntargets_frame = pd.read_parquet('/task/data/target_users.parquet')\ntargets = set(targets_frame.uid.astype(int))\neligibility = CandidateSet('/task/data/validation_eligible_candidates.npz')\ngraded = validation_truth(eligibility, targets)\n\ngrades_counts = []\ngrade3_counts = []\ngrade1_counts = []\nfor uid, items in graded.items():\n    grades_counts.append(len(items))\n    grade3_counts.append(sum(1 for g in items.values() if g == 3))\n    grade1_counts.append(sum(1 for g in items.values() if g == 1))\n\nprint(f'Users with any graded item: {len(graded)} / {len(targets)} ({len(graded)/len(targets)*100:.1f}%)')\nprint(f'Items per user: mean={np.mean(grades_counts):.2f}, median={np.median(grades_counts)}, max={np.max(grades_counts)}')\nprint(f'Grade 3 per user: mean={np.mean(grade3_counts):.2f}, Grade 1 per user: mean={np.mean(grade1_counts):.2f}')\ntotal_g3 = sum(grade3_counts)\ntotal_g1 = sum(grade1_counts)\nprint(f'Total grade 3 items: {total_g3}, Total grade 1 items: {total_g1}')\n\""
}

Tool response 

<untrusted_context>
Output: Users with any graded item: 4263 / 8456 (50.4%)
Items per user: mean=8.86, median=4.0, max=442
Grade 3 per user: mean=1.04, Grade 1 per user: mean=7.82
Total grade 3 items: 4417, Total grade 1 items: 33352
Process Group PGID: 1840
</untrusted_context>
18

Inspect candidate catalog data and distributions

+02:10
Recorded reasoning summary 53 words

Analyzing User Engagement Metrics

My current focus is on understanding user engagement within the 7-day validation window. I've identified 4263 users with relevant items, averaging 8.86 items each, specifically tracking grade 3 (likes) and grade 1 (strong listens >= 80%). I'm evaluating performance using NDCG@10, aiming to quantify the gain for each grade.

Recorded reasoning summary 50 words

Calculating Engagement Gains

My analysis now incorporates the calculated gains: grade 3 items offer a 7x gain over grade 1 items. This is critical as we target top positions, particularly within the NDCG@10 metric. I'm also starting to inventory the available training data, noting the substantial volume of listens data.

Recorded reasoning summary 59 words

Evaluating Data Memory Usage

I've confirmed that the train_listens dataset, though 305MB compressed, will fit comfortably into RAM with pandas, around 1.2GB. This leaves ample room for modeling. My next step is to conduct EDA on this dataset to identify features correlating with future organic discovery. I'm curious how many of the 30,000 candidate items actually appear in train_listens.

Tool callrun_shell_commandInspect candidate catalog data and distributions Returned

Tool call · Input 

{
  "description": "Inspect candidate catalog data and distributions",
  "command": "python3 -c \"\nimport pandas as pd\ncatalog = pd.read_parquet('/task/data/candidate_catalog.parquet')\nprint(catalog.head(10))\nprint(catalog.tail(10))\nprint('Popularity rank min/max:', catalog.popularity_rank.min(), catalog.popularity_rank.max())\nprint('Positive listens min/max:', catalog.positive_listens.min(), catalog.positive_listens.max())\nprint('Organic positive listens min/max:', catalog.organic_positive_listens.min(), catalog.organic_positive_listens.max())\n\""
}

Tool response 

<untrusted_context>
Output: 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
Popularity rank min/max: 1 30000
Positive listens min/max: 163 26615
Organic positive listens min/max: 0 20620
Process Group PGID: 1860
</untrusted_context>
19

Check whether graded items appeared in user's prior train listens

+02:21
Recorded reasoning summary 38 words

Confirming Catalog Inclusion

I'm verifying that all items designated in the validation ground truth are indeed present within the 30,000-item catalog. My current checks confirm this is the case, as the validation_truth logic inherently filters for catalog membership.

Tool callrun_shell_commandCheck whether graded items appeared in user's prior train listens Returned

Tool call · Input 

{
  "description": "Check whether graded items appeared in user's prior train listens",
  "command": "python3 -c \"\nimport sys\nsys.path.insert(0, '/task')\nimport pandas as pd\nimport numpy as np\nfrom ranking_evaluation import CandidateSet\nfrom evaluate_public import validation_truth\n\ntargets_frame = pd.read_parquet('/task/data/target_users.parquet')\ntargets = set(targets_frame.uid.astype(int))\neligibility = CandidateSet('/task/data/validation_eligible_candidates.npz')\ngraded = validation_truth(eligibility, targets)\n\n# Sample 100 users from graded\nsample_uids = list(graded.keys())[:100]\n\n# Check train_listens for these users\n# Read train_listens with columns uid, item_id\ntrain = pd.read_parquet('/task/data/train_listens.parquet', columns=['uid', 'item_id'])\ntrain_sample = train[train.uid.isin(sample_uids)]\n\n# For the sample users, what fraction of graded items were in train_sample?\nin_train_count = 0\ntotal_graded_sample = 0\nfor uid in sample_uids:\n    user_train_items = set(train_sample[train_sample.uid == uid].item_id)\n    user_graded_items = set(graded[uid].keys())\n    in_train_count += len(user_graded_items.intersection(user_train_items))\n    total_graded_sample += len(user_graded_items)\n\nprint(f'Sample: {in_train_count} / {total_graded_sample} ({in_train_count/total_graded_sample*100:.2f}%) of graded items appeared in user train listens')\n\""
}

Tool response 

<untrusted_context>
Output: Sample: 0 / 858 (0.00%) of graded items appeared in user train listens
Process Group PGID: 1877
</untrusted_context>
20

Check if any user train items are marked eligible in validation candidate set

+02:29
Recorded reasoning summary 49 words

Verifying User Engagement

I'm currently cross-referencing eligible items with user listening history. My focus is on clarifying the precise definition of "eligible" – specifically, whether it encompasses items never listened to, or if it also excludes those with past like/dislike interactions. This will ensure accurate analysis of graded items.

Tool callrun_shell_commandCheck if any user train items are marked eligible in validation candidate set Returned

Tool call · Input 

{
  "description": "Check if any user train items are marked eligible in validation candidate set",
  "command": "python3 -c \"\nimport sys\nsys.path.insert(0, '/task')\nimport pandas as pd\nimport numpy as np\nfrom ranking_evaluation import CandidateSet\n\neligibility = CandidateSet('/task/data/validation_eligible_candidates.npz')\nuid = 100\ntrain = pd.read_parquet('/task/data/train_listens.parquet', columns=['uid', 'item_id'])\nuser_train = set(train[train.uid == uid].item_id)\nprint('User 100 total train items:', len(user_train))\n# Check how many user_train items are in eligibility.item_ids\noverlap = user_train.intersection(set(eligibility.item_ids))\nprint('User 100 train items in catalog:', len(overlap))\n# Check how many are eligible\neligible_overlap = sum(eligibility.is_eligible(uid, it) for it in overlap)\nprint('User 100 train items that are eligible:', eligible_overlap)\n\""
}

Tool response 

<untrusted_context>
Output: User 100 total train items: 830
User 100 train items in catalog: 682
User 100 train items that are eligible: 0
Process Group PGID: 1900
</untrusted_context>
21

Check interaction counts of target users with catalog items

+02:40
Recorded reasoning summary 50 words

Prioritizing Novelty

I'm focusing on building a robust ranking model for music discovery. My core insight is that every user has a history of listened-to items marked as ineligible. This means my primary objective is to ensure pure novelty and introduce users to entirely new content they haven't encountered before.

Recorded reasoning summary 47 words

Refining Novelty Prediction

I'm now focused on predicting which entirely new tracks users will actively engage with. This involves a classic collaborative filtering challenge: ranking unseen items based on implicit and explicit feedback. I'm evaluating methods like Item-Item CF and implicit matrix factorization to identify top candidates.

Recorded reasoning summary 36 words

Exploring Matrix Factorization

I'm investigating matrix factorization techniques like Truncated SVD and item-item similarity for predicting user engagement with novel music. The interaction matrix, even with 30,000 items, is surprisingly manageable, making these approaches computationally feasible.

Tool callrun_shell_commandCheck interaction counts of target users with catalog items Returned

Tool call · Input 

{
  "description": "Check interaction counts of target users with catalog items",
  "command": "python3 -c \"\nimport pandas as pd\nimport numpy as np\n\ntargets = pd.read_parquet('/task/data/target_users.parquet')\ncatalog = pd.read_parquet('/task/data/candidate_catalog.parquet')\n\ntarget_uids = set(targets.uid)\ncatalog_items = set(catalog.item_id)\n\n# Check likes\nlikes = pd.read_parquet('/task/data/train_likes.parquet')\ntarget_likes = likes[likes.uid.isin(target_uids) & likes.item_id.isin(catalog_items)]\nprint(f'Target user likes in catalog: {len(target_likes)} rows, {target_likes.uid.nunique()} users, {target_likes.item_id.nunique()} items')\n\n# Check listens\nlistens = pd.read_parquet('/task/data/train_listens.parquet', columns=['uid', 'item_id', 'played_ratio_pct', 'is_organic', 'timestamp'])\ntarget_listens = listens[listens.uid.isin(target_uids) & listens.item_id.isin(catalog_items)]\nprint(f'Target user listens in catalog: {len(target_listens)} rows, {target_listens.uid.nunique()} users, {target_listens.item_id.nunique()} items')\n\""
}

Tool response 

<untrusted_context>
Output: Target user likes in catalog: 472889 rows, 6874 users, 28606 items
Target user listens in catalog: 31062540 rows, 8452 users, 30000 items
Process Group PGID: 1922
</untrusted_context>
22

Calculate unique user-item interaction pairs and matrix density

+02:53
Recorded reasoning summary 41 words

Analyzing Catalog Data

I'm examining the catalog listening data. It appears all 30,000 catalog items have been heard by our target users, and a significant majority, over 8400, of our target users have engaged with the catalog. This is quite promising.

Tool callrun_shell_commandCalculate unique user-item interaction pairs and matrix density Returned

Tool call · Input 

{
  "description": "Calculate unique user-item interaction pairs and matrix density",
  "command": "python3 -c \"\nimport pandas as pd\n\ntargets = pd.read_parquet('/task/data/target_users.parquet')\ncatalog = pd.read_parquet('/task/data/candidate_catalog.parquet')\ntarget_uids = set(targets.uid)\ncatalog_items = set(catalog.item_id)\n\nlistens = pd.read_parquet('/task/data/train_listens.parquet', columns=['uid', 'item_id'])\ntarget_listens = listens[listens.uid.isin(target_uids) & listens.item_id.isin(catalog_items)]\nunique_pairs = len(target_listens.drop_duplicates())\nprint(f'Distinct (uid, item) pairs: {unique_pairs}')\nprint(f'Sparsity: {unique_pairs / (len(target_uids) * len(catalog_items)) * 100:.2f}% of user-catalog matrix is filled!')\n\""
}

Tool response 

<untrusted_context>
Output: Distinct (uid, item) pairs: 7868664
Sparsity: 3.10% of user-catalog matrix is filled!
Process Group PGID: 1949
</untrusted_context>
23

Benchmark matrix inverse time for 8456x8456 matrix

+03:17
Recorded reasoning summary 45 words

Analyzing Data Density

I've identified that the current matrix density for collaborative filtering is a remarkable 3.10%, significantly exceeding the typical 0.01%-0.1% range. This translates to approximately 7.87 million distinct (uid, item) pairs. I'm also noting the presence of train_likes.parquet and other training data files.

Recorded reasoning summary 41 words

Exploring New Data Sources

I'm now considering additional data files such as dislikes.parquet, train_unlikes.parquet, and train_undislikes.parquet. I'm also examining validation_preference_state.parquet for explicit states and noting features like played_ratio_pct and is_organic within train_listens.parquet. The timing of data splits is also being refined.

Recorded reasoning summary 53 words

Calculating Item Similarities

I'm calculating item similarities, considering both dense and sparse approaches for the similarity matrix $S$. A dense $30000 \times 30000$ matrix would fit in RAM, but I'm also exploring sparse representations using top-$k$ neighbors to significantly reduce memory usage. This could bring the memory footprint down to around 50 MB.

Recorded reasoning summary 37 words

Testing Matrix Inversion

I'm evaluating the feasibility of inverting a large $30000 \times 30000$ matrix within 16 GB of RAM, a crucial step for the EASE algorithm. This inversion is a primary bottleneck I need to overcome.

Tool callrun_shell_commandBenchmark matrix inverse time for 8456x8456 matrix Returned

Tool call · Input 

{
  "command": "python3 -c \"\nimport numpy as np\nimport time\n\nA = np.random.randn(8456, 8456).astype(np.float32)\nA = A.T @ A + 100 * np.eye(8456, dtype=np.float32)\nt0 = time.time()\ninv = np.linalg.inv(A)\nt1 = time.time()\nprint(f'8456x8456 matrix inverse time: {t1 - t0:.2f} seconds')\n\"",
  "description": "Benchmark matrix inverse time for 8456x8456 matrix"
}

Tool response 

<untrusted_context>
Output: 8456x8456 matrix inverse time: 16.49 seconds
Process Group PGID: 1971
</untrusted_context>
24

Benchmark TruncatedSVD on realistic matrix dimensions

+03:42
Recorded reasoning summary 55 words

Exploring SVD Options

I'm currently evaluating different Singular Value Decomposition methods. Initial tests with full inversion on an $8456 \times 8456$ matrix were promising, achieving completion in under 17 seconds. My next step is to explore Truncated SVD and Randomized SVD via scikit-learn, particularly focusing on their performance with a sparse $8456 \times 30000$ matrix.

Tool callrun_shell_commandBenchmark TruncatedSVD on realistic matrix dimensions Returned

Tool call · Input 

{
  "command": "python3 -c \"\nimport scipy.sparse as sp\nfrom sklearn.decomposition import TruncatedSVD\nimport time\nimport numpy as np\n\nR = sp.random(8456, 30000, density=0.03, format='csr', dtype=np.float32)\nfor k in [64, 128, 256]:\n    t0 = time.time()\n    svd = TruncatedSVD(n_components=k, algorithm='randomized', random_state=42)\n    U = svd.fit_transform(R)\n    V = svd.components_\n    t1 = time.time()\n    print(f'TruncatedSVD k={k}: {t1 - t0:.2f} s, shape U={U.shape}, shape V={V.shape}')\n\"",
  "description": "Benchmark TruncatedSVD on realistic matrix dimensions"
}

Tool response 

<untrusted_context>
Output: TruncatedSVD k=64: 2.83 s, shape U=(8456, 64), shape V=(64, 30000)
TruncatedSVD k=128: 4.04 s, shape U=(8456, 128), shape V=(128, 30000)
TruncatedSVD k=256: 7.62 s, shape U=(8456, 256), shape V=(256, 30000)
Process Group PGID: 1981
</untrusted_context>
25

Benchmark user-user similarity and prediction time

+04:16
Recorded reasoning summary 54 words

Exploring Similarity Options

My current focus is on evaluating various item-item similarity approaches, noting that TruncatedSVD with k=128 is remarkably fast at 4 seconds. I'm now planning benchmarks for cosine similarity, BM25, and co-occurrence, aiming to compare their performance against the SVD results, especially considering the $8456 \times 30000$ dimensions of the rating matrix.

Tool callrun_shell_commandBenchmark user-user similarity and prediction time Returned

Tool call · Input 

{
  "command": "python3 -c \"\nimport scipy.sparse as sp\nimport time\n\nR = sp.random(8456, 30000, density=0.03, format='csr', dtype=np.float32)\nt0 = time.time()\nU_sim = R @ R.T\nt1 = time.time()\nprint(f'R @ R.T took: {t1 - t0:.2f} s, shape={U_sim.shape}, nonzeros={U_sim.nnz}')\nt0 = time.time()\npreds = U_sim @ R\nt1 = time.time()\nprint(f'U_sim @ R took: {t1 - t0:.2f} s')\n\"",
  "description": "Benchmark user-user similarity and prediction time"
}

Tool response 

<untrusted_context>
Output: Traceback (most recent call last):
  File "<string>", line 5, in <module>
NameError: name 'np' is not defined. Did you mean: 'sp'?
Exit Code: 1
Process Group PGID: 2005
</untrusted_context>
26

Benchmark user-user similarity and prediction time with numpy import

+04:22
Tool callrun_shell_commandBenchmark user-user similarity and prediction time with numpy import Returned

Tool call · Input 

{
  "description": "Benchmark user-user similarity and prediction time with numpy import",
  "command": "python3 -c \"\nimport scipy.sparse as sp\nimport numpy as np\nimport time\n\nR = sp.random(8456, 30000, density=0.03, format='csr', dtype=np.float32)\nt0 = time.time()\nU_sim = R @ R.T\nt1 = time.time()\nprint(f'R @ R.T took: {t1 - t0:.2f} s, shape={U_sim.shape}, nonzeros={U_sim.nnz}')\nt0 = time.time()\npreds = U_sim @ R\nt1 = time.time()\nprint(f'U_sim @ R took: {t1 - t0:.2f} s')\n\""
}

Tool response 

<untrusted_context>
Output: R @ R.T took: 5.92 s, shape=(8456, 8456), nonzeros=71503936
U_sim @ R took: 152.54 s
Process Group PGID: 2022
</untrusted_context>
27

Benchmark dense matrix multiplication time

+07:16
Recorded reasoning summary 45 words

Analyzing Performance Bottlenecks

I'm investigating why the U_sim @ R operation with a large, dense $8456 \times 8456$ matrix took 152 seconds. It seems dense matrix multiplication is the culprit here, significantly impacting performance. I'm now turning my attention to understanding the performance within TruncatedSVD.

Tool callrun_shell_commandBenchmark dense matrix multiplication time Returned

Tool call · Input 

{
  "description": "Benchmark dense matrix multiplication time",
  "command": "python3 -c \"\nimport numpy as np\nimport time\n\nU = np.random.randn(8456, 128).astype(np.float32)\nV = np.random.randn(128, 30000).astype(np.float32)\nt0 = time.time()\nscores = U @ V\nt1 = time.time()\nprint(f'Dense U @ V took: {t1 - t0:.3f} s, shape={scores.shape}')\n\""
}

Tool response 

<untrusted_context>
Output: Dense U @ V took: 0.408 s, shape=(8456, 30000)
Process Group PGID: 2057
</untrusted_context>
28

Benchmark sparse R @ S matrix multiplication

+07:21
Recorded reasoning summary 51 words

Exploring Item-Item Efficiency

I'm currently investigating the computational feasibility of item-item similarity, specifically focusing on sparse matrix representations and their performance implications. My current analysis centers on the speed of scoring all users against items when only retaining the top-K most similar items per item, a critical step for optimizing recommendations.

Tool callrun_shell_commandBenchmark sparse R @ S matrix multiplication Returned

Tool call · Input 

{
  "description": "Benchmark sparse R @ S matrix multiplication",
  "command": "python3 -c \"\nimport scipy.sparse as sp\nimport numpy as np\nimport time\n\nR = sp.random(8456, 30000, density=0.03, format='csr', dtype=np.float32)\n# sparse S with 100 nonzeros per column\ncols = np.repeat(np.arange(30000), 100)\nrows = np.random.randint(0, 30000, size=30000 * 100)\ndata = np.random.rand(30000 * 100).astype(np.float32)\nS = sp.csr_matrix((data, (rows, cols)), shape=(30000, 30000))\nt0 = time.time()\nscores = R @ S\nt1 = time.time()\nprint(f'Sparse R @ S (top-100 items) took: {t1 - t0:.3f} s, nonzeros={scores.nnz}')\n\""
}

Tool response 

<untrusted_context>
Output: Sparse R @ S (top-100 items) took: 6.638 s, nonzeros=241554916
Process Group PGID: 2067
</untrusted_context>
29

Measure load time of train_listens.parquet

+07:46
Recorded reasoning summary 43 words

Analyzing Validation Data

I'm currently examining the validation data to define track relevance. My focus is on understanding what constitutes a "liked" organic track versus one that was simply listened to. This will inform how I construct the interaction matrix and associated weights.

Recorded reasoning summary 47 words

Refining Interaction Signals

I'm now incorporating signals from the training data. Specifically, I'm analyzing train_likes and train_listens to better define taste profiles. This involves considering explicit likes, weighted listens based on played_ratio_pct and organic status, and recency through timestamps. Negative signals from train_dislikes are also being evaluated.

Tool callrun_shell_commandMeasure load time of train_listens.parquet Returned

Tool call · Input 

{
  "description": "Measure load time of train_listens.parquet",
  "command": "python3 -c \"\nimport pandas as pd\nimport time\n\nt0 = time.time()\ndf = pd.read_parquet('/task/data/train_listens.parquet', columns=['uid', 'item_id', 'timestamp', 'played_ratio_pct', 'is_organic'])\nt1 = time.time()\nprint(f'Loading train_listens took {t1 - t0:.2f} s. Rows: {len(df)}')\n\""
}

Tool response 

<untrusted_context>
Output: Loading train_listens took 0.53 s. Rows: 43484159
Process Group PGID: 2090
</untrusted_context>
30

Run TruncatedSVD baseline on public validation set

+08:01
Recorded reasoning summary 49 words

Loading Data Rapidly

I've successfully loaded a substantial dataset of 43.4 million rows in a mere 0.53 seconds using pyarrow – impressive speed! My current focus is on analyzing the connection between user listening histories and catalog items, particularly within the train_listens dataset, which currently involves 8,456 target users.

Recorded reasoning summary 50 words

Evaluating Model Viability

I'm currently exploring whether non-catalog items within train_listens offer significant value for our models, given the extensive interaction data target users already have with catalog items. I’m setting up an evaluation framework to test this experimentally, aiming to quickly assess model performance on the public validation set.

Recorded reasoning summary 47 words

Designing Interaction Matrix

I'm now focusing on the core of collaborative filtering: defining the user-item interaction matrix. I'm weighing several options for entry values, from simple binary presence to weighted counts incorporating event type and even time decay, aiming for the most informative representation for my models.

Tool callrun_shell_commandRun TruncatedSVD baseline on public validation set Returned

Tool call · Input 

{
  "description": "Run TruncatedSVD baseline on public validation set",
  "command": "python3 -c \"\nimport sys\nsys.path.insert(0, '/task')\nimport time\nimport numpy as np\nimport pandas as pd\nimport scipy.sparse as sp\nfrom sklearn.decomposition import TruncatedSVD\nfrom ranking_evaluation import CandidateSet, score_rankings\nfrom evaluate_public import validation_truth\n\n# 1. Load targets and eligibility\ntargets_frame = pd.read_parquet('/task/data/target_users.parquet')\ntargets = set(targets_frame.uid.astype(int))\neligibility = CandidateSet('/task/data/validation_eligible_candidates.npz')\ngraded = validation_truth(eligibility, targets)\n\ntarget_uids = eligibility.uids\nitem_ids = eligibility.item_ids\nuid_to_idx = {uid: i for i, uid in enumerate(target_uids)}\nitem_to_idx = {item: i for i, item in enumerate(item_ids)}\n\nprint(f'Target users: {len(target_uids)}, Catalog items: {len(item_ids)}')\n\n# Load train listens for target users & catalog items\nt0 = time.time()\nlistens = pd.read_parquet('/task/data/train_listens.parquet', columns=['uid', 'item_id', 'played_ratio_pct', 'is_organic', 'timestamp'])\nlistens = listens[listens.uid.isin(targets) & listens.item_id.isin(item_to_idx)]\nprint(f'Filtered listens in {time.time()-t0:.2f}s: {len(listens)} rows')\n\n# Build user-item positive counts\n# Only positive listens (played_ratio_pct >= 50)\npos_listens = listens[listens.played_ratio_pct >= 50]\nu_idx = pos_listens.uid.map(uid_to_idx).to_numpy()\ni_idx = pos_listens.item_id.map(item_to_idx).to_numpy()\n\n# Sparse matrix with counts\nR_counts = sp.csr_matrix((np.ones(len(u_idx), dtype=np.float32), (u_idx, i_idx)), shape=(len(target_uids), len(item_ids)))\nR_log = R_counts.copy()\nR_log.data = np.log1p(R_log.data)\n\nprint(f'R_log constructed, nnz={R_log.nnz}')\n\n# Evaluate SVD for different k\nfor k in [64, 128, 256]:\n    t0 = time.time()\n    svd = TruncatedSVD(n_components=k, algorithm='randomized', random_state=42)\n    U = svd.fit_transform(R_log)\n    V = svd.components_\n    \n    # Predict scores for all users\n    # Batch predict top 100 eligible per user\n    scores = U @ V # shape (8456, 30000)\n    \n    # Mask ineligible items by setting their score to -infinity\n    # Use packed eligibility\n    ranked = {}\n    for i, uid in enumerate(target_uids):\n        user_scores = scores[i].copy()\n        bits = np.unpackbits(eligibility.packed[i], count=30000, bitorder='little')\n        user_scores[bits == 0] = -np.inf\n        # Top 100\n        top100_idx = np.argpartition(user_scores, -100)[-100:]\n        top100_sorted = top100_idx[np.argsort(-user_scores[top100_idx])]\n        ranked[int(uid)] = list(item_ids[top100_sorted])\n    \n    res = score_rankings(ranked, graded)\n    print(f'TruncatedSVD k={k}: {res} (took {time.time()-t0:.2f}s)')\n\""
}

Tool response 

<untrusted_context>
Output: Target users: 8456, Catalog items: 30000
Filtered listens in 8.21s: 31062540 rows
R_log constructed, nnz=5514505
TruncatedSVD k=64: {'quality_ndcg@10': 0.017838, 'quality_users': 4263} (took 3.74s)
TruncatedSVD k=128: {'quality_ndcg@10': 0.019115, 'quality_users': 4263} (took 4.66s)
TruncatedSVD k=256: {'quality_ndcg@10': 0.020296, 'quality_users': 4263} (took 7.17s)
Process Group PGID: 2111
</untrusted_context>
31

update_topic command

+08:46
Recorded reasoning summary 40 words

Exploring SVD Improvements

I'm seeing significant gains with TruncatedSVD, with catalog popularity nearly doubling at k=256. This progress, achieved in just 7 seconds, is very encouraging. I'm now eager to investigate further model possibilities to build on these promising results.

Tool callupdate_topicTool call Returned

Tool call · Input 

{
  "title": "Model Architecture Exploration & Feature Engineering",
  "summary": "We established a validation pipeline and verified that TruncatedSVD reaches 0.0203 NDCG@10 (vs 0.0107 popularity baseline). Now we will test and compare multiple collaborative filtering and ranking architectures: Item-Item Cosine / BM25 / TF-IDF, EASE (Embarrassingly Shallow Autoencoders), weighted implicit feedback (organic listens, likes, dislikes, time decay, played ratio), and model ensembling/blending.",
  "strategic_intent": "Develop, experiment with, and optimize diverse recommendation models and feature weighting."
}

Tool response 

Current topic: "Model Architecture Exploration & Feature Engineering"
Topic summary: We established a validation pipeline and verified that TruncatedSVD reaches 0.0203 NDCG@10 (vs 0.0107 popularity baseline). Now we will test and compare multiple collaborative filtering and ranking architectures: Item-Item Cosine / BM25 / TF-IDF, EASE (Embarrassingly Shallow Autoencoders), weighted implicit feedback (organic listens, likes, dislikes, time decay, played ratio), and model ensembling/blending.

Strategic Intent: Develop, experiment with, and optimize diverse recommendation models and feature weighting.
32

Test SVD with larger latent dimensions (256, 384, 512)

+09:02
Recorded reasoning summary 43 words

Exploring Recommendation Efficacy

I'm currently dissecting the effectiveness of recommendation systems for discovery. My focus is on analyzing the available signals, particularly user likes. I've noted that 'grade 3' in validation preference state signifies organic liking, a crucial insight for understanding user engagement.

Recorded reasoning summary 44 words

Analyzing User Engagement Metrics

I'm now prioritizing the impact of organic listens and completion ratios on predicting taste. I'm also looking into how to properly interpret "skips" based on the played_ratio_pct distribution and the temporal relevance of data, considering potential taste drift over time.

Tool callrun_shell_commandTest SVD with larger latent dimensions (256, 384, 512) Returned

Tool call · Input 

{
  "command": "python3 -c \"\nimport sys\nsys.path.insert(0, '/task')\nimport time\nimport numpy as np\nimport pandas as pd\nimport scipy.sparse as sp\nfrom sklearn.decomposition import TruncatedSVD\nfrom sklearn.preprocessing import normalize\nfrom ranking_evaluation import CandidateSet, score_rankings\nfrom evaluate_public import validation_truth\n\n# 1. Load targets and eligibility\ntargets_frame = pd.read_parquet('/task/data/target_users.parquet')\ntargets = set(targets_frame.uid.astype(int))\neligibility = CandidateSet('/task/data/validation_eligible_candidates.npz')\ngraded = validation_truth(eligibility, targets)\n\ntarget_uids = eligibility.uids\nitem_ids = eligibility.item_ids\nuid_to_idx = {uid: i for i, uid in enumerate(target_uids)}\nitem_to_idx = {item: i for i, item in enumerate(item_ids)}\n\n# Load listens\nlistens = pd.read_parquet('/task/data/train_listens.parquet', columns=['uid', 'item_id', 'played_ratio_pct', 'is_organic', 'timestamp'])\nlistens = listens[listens.uid.isin(targets) & listens.item_id.isin(item_to_idx)]\n\n# Filter positive listens\npos = listens[listens.played_ratio_pct >= 50].copy()\nu_idx = pos.uid.map(uid_to_idx).to_numpy()\ni_idx = pos.item_id.map(item_to_idx).to_numpy()\ncounts = sp.csr_matrix((np.ones(len(u_idx), dtype=np.float32), (u_idx, i_idx)), shape=(len(target_uids), len(item_ids)))\nR_log = counts.copy()\nR_log.data = np.log1p(R_log.data)\n\n# Test SVD with k=384 and k=512\nfor k in [256, 384, 512]:\n    t0 = time.time()\n    svd = TruncatedSVD(n_components=k, algorithm='randomized', random_state=42)\n    U = svd.fit_transform(R_log)\n    V = svd.components_\n    scores = U @ V\n    \n    ranked = {}\n    for i, uid in enumerate(target_uids):\n        user_scores = scores[i].copy()\n        bits = np.unpackbits(eligibility.packed[i], count=30000, bitorder='little')\n        user_scores[bits == 0] = -np.inf\n        top100_idx = np.argpartition(user_scores, -100)[-100:]\n        top100_sorted = top100_idx[np.argsort(-user_scores[top100_idx])]\n        ranked[int(uid)] = list(item_ids[top100_sorted])\n    \n    res = score_rankings(ranked, graded)\n    print(f'SVD k={k}: {res} ({time.time()-t0:.2f}s)')\n\"",
  "description": "Test SVD with larger latent dimensions (256, 384, 512)"
}

Tool response 

<untrusted_context>
Output: SVD k=256: {'quality_ndcg@10': 0.020296, 'quality_users': 4263} (7.30s)
SVD k=384: {'quality_ndcg@10': 0.019694, 'quality_users': 4263} (12.11s)
SVD k=512: {'quality_ndcg@10': 0.020185, 'quality_users': 4263} (16.01s)
Process Group PGID: 2145
</untrusted_context>
33

Check matrix multiplication associativity for Item-Item vs User-User

+10:14
Recorded reasoning summary 40 words

Exploring Collaborative Filtering

I'm now shifting focus from SVD to Item-Item Collaborative Filtering. My current goal is to evaluate several weighting schemes (Cosine, BM25, TFIDF, Co-occurrence) within this framework, starting with understanding its core mechanics using the provided user-item matrix.

Tool callrun_shell_commandCheck matrix multiplication associativity for Item-Item vs User-User Returned

Tool call · Input 

{
  "command": "python3 -c \"\nimport scipy.sparse as sp\nimport numpy as np\nimport time\n\nR = sp.random(8456, 30000, density=0.02, format='csr', dtype=np.float32)\n# Normalize columns (items) for cosine similarity\nfrom sklearn.preprocessing import normalize\nR_norm = normalize(R, norm='l2', axis=0) # unit norm items\n\nt0 = time.time()\n# compute block by block or sparse dot\n# Let's test R.dot(R.T.dot(R)) or similar:\n# Note: user predictions = R @ S = R @ (R.T @ R) = (R @ R.T) @ R !!\n# Notice the associativity of matrix multiplication!\n# (R @ R.T) is (8456 x 8456)!\n# And ((R @ R.T) @ R) has shape (8456 x 30000)!\n\"",
  "description": "Check matrix multiplication associativity for Item-Item vs User-User"
}

Tool response 

<untrusted_context>
Output: (empty)
Process Group PGID: 2179
</untrusted_context>
34

Benchmark fast block-wise top-K item similarity computation

+10:34
Recorded reasoning summary 31 words

Exploring Mathematical Equivalence

I'm currently examining the mathematical equivalence in item-item collaborative filtering. Specifically, I'm interested in how the formula transforms with and without top-k truncation, highlighting the user-user co-occurrence aspect.

Tool callrun_shell_commandBenchmark fast block-wise top-K item similarity computation Returned

Tool call · Input 

{
  "description": "Benchmark fast block-wise top-K item similarity computation",
  "command": "python3 -c \"\nimport numpy as np\nimport scipy.sparse as sp\nimport time\nfrom sklearn.preprocessing import normalize\n\n# Create normalized item matrix R: shape (N_users, N_items)\nN_users, N_items = 8456, 30000\nR = sp.random(N_users, N_items, density=0.02, format='csr', dtype=np.float32)\nR_items = normalize(R, norm='l2', axis=0) # columns are unit norm\n# Convert to csc for fast column slicing or csr of R.T\nRt = R_items.T.tocsr() # shape (30000, 8456)\n\nt0 = time.time()\nK = 100\nrows, cols, vals = [], [], []\nbatch_size = 1000\nfor start in range(0, N_items, batch_size):\n    end = min(start + batch_size, N_items)\n    block = Rt[start:end] # (batch, 8456)\n    sims = (block @ R_items).toarray() # (batch, 30000) dense\n    # zero out self similarity\n    for b in range(end - start):\n        sims[b, start + b] = 0.0\n        # top K\n        top_k = np.argpartition(sims[b], -K)[-K:]\n        top_k = top_k[sims[b, top_k] > 0]\n        rows.extend([start + b] * len(top_k))\n        cols.extend(top_k)\n        vals.extend(sims[b, top_k])\n\nS = sp.csr_matrix((vals, (rows, cols)), shape=(N_items, N_items), dtype=np.float32)\nt1 = time.time()\nprint(f'Constructed top-{K} item-item similarity in {t1 - t0:.2f} s, nnz={S.nnz}')\n\""
}

Tool response 

<untrusted_context>
Output: Constructed top-100 item-item similarity in 29.30 s, nnz=3000000
Process Group PGID: 2197
</untrusted_context>
35

Benchmark dense BLAS multiplication of User similarity by R

+11:37
Recorded reasoning summary 57 words

Exploring Faster Approaches

I'm currently investigating ways to accelerate our experimentation process, as the 29-second per run is proving to be a bottleneck. My focus is on evaluating the feasibility of techniques like user-user collaborative filtering, dual EASE, SVD, and TF-IDF/BM25 to achieve faster results, starting with an analysis of user-user CF given our $N_{\text{users}} = 8456$.

Recorded reasoning summary 46 words

Calculating User-User Similarity

My initial exploration into user-user collaborative filtering is showing promise. The matrix multiplication to compute user-user similarity, given our user base size, completes in approximately 3 seconds. I am now focused on testing the speed of generating predictions using this dense similarity matrix.

Tool callrun_shell_commandBenchmark dense BLAS multiplication of User similarity by R Returned

Tool call · Input 

{
  "command": "python3 -c \"\nimport numpy as np\nimport time\n\nU = np.random.randn(8456, 8456).astype(np.float32)\nR = np.random.randn(8456, 30000).astype(np.float32)\nt0 = time.time()\nout = U @ R\nt1 = time.time()\nprint(f'Dense 8456x8456 @ 8456x30000 took {t1 - t0:.2f} s')\n\"",
  "description": "Benchmark dense BLAS multiplication of User similarity by R"
}

Tool response 

<untrusted_context>
Output: Dense 8456x8456 @ 8456x30000 took 19.73 s
Process Group PGID: 2221
</untrusted_context>
36

Run event weighting experiment and evaluate NDCG@10

+12:21
Recorded reasoning summary 78 words

Exploring SVD Efficiency

I've been exploring Singular Value Decomposition and found that truncated SVD, specifically with matrices U and V of dimensions (8456 x 256) and (256 x 30000) respectively, is remarkably fast. The fitting process took approximately 5 seconds, and the subsequent matrix multiplication of U and V was completed in just 0.4 seconds, for a total of around 6 seconds. My next step is to investigate randomized SVD, PCA, and NMF for potentially even greater speed.

Recorded reasoning summary 53 words

Improving Input Matrix

My current focus is on enhancing the input matrix $R$ by incorporating various weighting schemes. I'm particularly interested in how TF-IDF, BM25, time decay, and explicit signals like likes and dislikes will impact validation NDCG. I'll begin by experimenting with these feature weightings, comparing them to the previous R_log baseline.

Recorded reasoning summary 37 words

Investigating Decay Parameters

I'm currently evaluating different half-lives for exponential decay, specifically considering 30, 60, and 90 days to see their impact on training. The maximum timestamp observed is 24,786,800, which will be a key reference point.

Tool callrun_shell_commandRun event weighting experiment and evaluate NDCG@10 Returned

Tool call · Input 

{
  "command": "python3 -c \"\nimport sys\nsys.path.insert(0, '/task')\nimport time\nimport numpy as np\nimport pandas as pd\nimport scipy.sparse as sp\nfrom sklearn.decomposition import TruncatedSVD\nfrom sklearn.preprocessing import normalize\nfrom ranking_evaluation import CandidateSet, score_rankings\nfrom evaluate_public import validation_truth\n\n# 1. Load targets, catalog, and eligibility\ntargets_frame = pd.read_parquet('/task/data/target_users.parquet')\ntargets = set(targets_frame.uid.astype(int))\neligibility = CandidateSet('/task/data/validation_eligible_candidates.npz')\ngraded = validation_truth(eligibility, targets)\n\ntarget_uids = eligibility.uids\nitem_ids = eligibility.item_ids\nuid_to_idx = {uid: i for i, uid in enumerate(target_uids)}\nitem_to_idx = {item: i for i, item in enumerate(item_ids)}\n\n# Load listens\nprint('Loading train data...')\nt0 = time.time()\nlistens = pd.read_parquet('/task/data/train_listens.parquet', columns=['uid', 'item_id', 'timestamp', 'played_ratio_pct', 'is_organic'])\nlistens = listens[listens.uid.isin(targets) & listens.item_id.isin(item_to_idx)].copy()\n\nlikes = pd.read_parquet('/task/data/train_likes.parquet')\nlikes = likes[likes.uid.isin(targets) & likes.item_id.isin(item_to_idx)].copy()\n\ndislikes = pd.read_parquet('/task/data/train_dislikes.parquet')\ndislikes = dislikes[dislikes.uid.isin(targets) & dislikes.item_id.isin(item_to_idx)].copy()\nprint(f'Data loaded in {time.time()-t0:.2f}s')\n\nT_max = 24786800\n\ndef evaluate_predictions(scores, name=''):\n    t0 = time.time()\n    ranked = {}\n    for i, uid in enumerate(target_uids):\n        user_scores = scores[i].copy()\n        bits = np.unpackbits(eligibility.packed[i], count=30000, bitorder='little')\n        user_scores[bits == 0] = -np.inf\n        top100_idx = np.argpartition(user_scores, -100)[-100:]\n        top100_sorted = top100_idx[np.argsort(-user_scores[top100_idx])]\n        ranked[int(uid)] = list(item_ids[top100_sorted])\n    res = score_rankings(ranked, graded)\n    print(f'{name} -> NDCG@10: {res[\\\"quality_ndcg@10\\\"]} (eval took {time.time()-t0:.2f}s)')\n    return res[\\\"quality_ndcg@10\\\"]\n\n# Experiment 1: Baseline log counts\npos = listens[listens.played_ratio_pct >= 50].copy()\nu = pos.uid.map(uid_to_idx).to_numpy()\nit = pos.item_id.map(item_to_idx).to_numpy()\nR_base = sp.csr_matrix((np.ones(len(u), dtype=np.float32), (u, it)), shape=(len(target_uids), len(item_ids)))\nR_base.data = np.log1p(R_base.data)\n\nsvd = TruncatedSVD(n_components=256, algorithm='randomized', random_state=42)\nU = svd.fit_transform(R_base)\nV = svd.components_\nevaluate_predictions(U @ V, 'Baseline pos >= 50, k=256')\n\n# Experiment 2: Event weighting (likes, organic, completion)\n# Let's assign weights to events:\n# like: 5.0\n# organic listen >= 80%: 3.0\n# organic listen >= 50%: 2.0\n# non-organic listen >= 80%: 1.5\n# non-organic listen >= 50%: 1.0\n\n# Calculate listen weights\nweights = np.ones(len(listens), dtype=np.float32)\nstrong = listens.played_ratio_pct >= 80\norganic = listens.is_organic == 1\n\nweights[~strong & ~organic] = 1.0\nweights[strong & ~organic] = 1.5\nweights[~strong & organic] = 2.0\nweights[strong & organic] = 3.0\n\nu_l = listens.uid.map(uid_to_idx).to_numpy()\nit_l = listens.item_id.map(item_to_idx).to_numpy()\n\nu_lk = likes.uid.map(uid_to_idx).to_numpy()\nit_lk = likes.item_id.map(item_to_idx).to_numpy()\nweights_lk = np.full(len(likes), 5.0, dtype=np.float32)\n\nall_u = np.concatenate([u_l, u_lk])\nall_it = np.concatenate([it_l, it_lk])\nall_w = np.concatenate([weights, weights_lk])\n\nR_weighted = sp.csr_matrix((all_w, (all_u, all_it)), shape=(len(target_uids), len(item_ids)))\nR_w_log = R_weighted.copy()\nR_w_log.data = np.log1p(R_w_log.data)\n\nsvd = TruncatedSVD(n_components=256, algorithm='randomized', random_state=42)\nU = svd.fit_transform(R_w_log)\nV = svd.components_\nevaluate_predictions(U @ V, 'Weighted events (likes + organic + strong)')\n\"",
  "description": "Run event weighting experiment and evaluate NDCG@10"
}

Tool response 

<untrusted_context>
Output: Loading train data...
Data loaded in 8.38s
Baseline pos >= 50, k=256 -> NDCG@10: 0.020296 (eval took 1.07s)
Weighted events (likes + organic + strong) -> NDCG@10: 0.022618 (eval took 1.07s)
Process Group PGID: 2231
</untrusted_context>
37

Test time decay half-lives from 15 to 180 days

+13:00
Recorded reasoning summary 62 words

Improving NDCG Performance

I'm excited to report a significant NDCG improvement, a jump from 0.020296 to 0.022618, which is an 11.4% gain attributed to event weighting. My next step involves investigating the impact of incorporating time decay into these events. I'll be examining how this adjustment might influence user engagement, particularly considering the dynamic nature of preferences in contexts like music streaming.

Tool callrun_shell_commandTest time decay half-lives from 15 to 180 days Returned

Tool call · Input 

{
  "command": "python3 -c \"\nimport sys\nsys.path.insert(0, '/task')\nimport time\nimport numpy as np\nimport pandas as pd\nimport scipy.sparse as sp\nfrom sklearn.decomposition import TruncatedSVD\nfrom ranking_evaluation import CandidateSet, score_rankings\nfrom evaluate_public import validation_truth\n\n# 1. Load targets, catalog, and eligibility\ntargets_frame = pd.read_parquet('/task/data/target_users.parquet')\ntargets = set(targets_frame.uid.astype(int))\neligibility = CandidateSet('/task/data/validation_eligible_candidates.npz')\ngraded = validation_truth(eligibility, targets)\n\ntarget_uids = eligibility.uids\nitem_ids = eligibility.item_ids\nuid_to_idx = {uid: i for i, uid in enumerate(target_uids)}\nitem_to_idx = {item: i for i, item in enumerate(item_ids)}\n\nlistens = pd.read_parquet('/task/data/train_listens.parquet', columns=['uid', 'item_id', 'timestamp', 'played_ratio_pct', 'is_organic'])\nlistens = listens[listens.uid.isin(targets) & listens.item_id.isin(item_to_idx)].copy()\n\nlikes = pd.read_parquet('/task/data/train_likes.parquet')\nlikes = likes[likes.uid.isin(targets) & likes.item_id.isin(item_to_idx)].copy()\n\nT_max = 24786800\n\ndef evaluate_predictions(scores, name=''):\n    ranked = {}\n    for i, uid in enumerate(target_uids):\n        user_scores = scores[i].copy()\n        bits = np.unpackbits(eligibility.packed[i], count=30000, bitorder='little')\n        user_scores[bits == 0] = -np.inf\n        top100_idx = np.argpartition(user_scores, -100)[-100:]\n        top100_sorted = top100_idx[np.argsort(-user_scores[top100_idx])]\n        ranked[int(uid)] = list(item_ids[top100_sorted])\n    res = score_rankings(ranked, graded)\n    print(f'{name} -> NDCG@10: {res[\\\"quality_ndcg@10\\\"]}')\n    return res[\\\"quality_ndcg@10\\\"]\n\nbase_weights_l = np.ones(len(listens), dtype=np.float32)\nstrong = listens.played_ratio_pct >= 80\norganic = listens.is_organic == 1\nbase_weights_l[~strong & ~organic] = 1.0\nbase_weights_l[strong & ~organic] = 1.5\nbase_weights_l[~strong & organic] = 2.0\nbase_weights_l[strong & organic] = 3.0\n\nu_l = listens.uid.map(uid_to_idx).to_numpy()\nit_l = listens.item_id.map(item_to_idx).to_numpy()\nts_l = listens.timestamp.to_numpy()\n\nu_lk = likes.uid.map(uid_to_idx).to_numpy()\nit_lk = likes.item_id.map(item_to_idx).to_numpy()\nts_lk = likes.timestamp.to_numpy()\nbase_weights_lk = np.full(len(likes), 5.0, dtype=np.float32)\n\nall_u = np.concatenate([u_l, u_lk])\nall_it = np.concatenate([it_l, it_lk])\nall_ts = np.concatenate([ts_l, ts_lk])\nall_w_base = np.concatenate([base_weights_l, base_weights_lk])\nage_days = (T_max - all_ts) / 86400.0\n\nfor H in [15, 30, 45, 60, 90, 120, 180]:\n    decay = np.power(0.5, age_days / H).astype(np.float32)\n    w = all_w_base * decay\n    R = sp.csr_matrix((w, (all_u, all_it)), shape=(len(target_uids), len(item_ids)))\n    R.data = np.log1p(R.data)\n    svd = TruncatedSVD(n_components=256, algorithm='randomized', random_state=42)\n    U = svd.fit_transform(R)\n    V = svd.components_\n    evaluate_predictions(U @ V, f'Half-life H={H} days')\n\"",
  "description": "Test time decay half-lives from 15 to 180 days"
}

Tool response 

<untrusted_context>
Output: Half-life H=15 days -> NDCG@10: 0.035707
Half-life H=30 days -> NDCG@10: 0.033335
Half-life H=45 days -> NDCG@10: 0.031045
Half-life H=60 days -> NDCG@10: 0.029109
Half-life H=90 days -> NDCG@10: 0.026911
Half-life H=120 days -> NDCG@10: 0.026084
Half-life H=180 days -> NDCG@10: 0.024919
Process Group PGID: 2265
</untrusted_context>
38

Test fine-grained half-lives from 3 to 18 days

+14:36
Tool callrun_shell_commandTest fine-grained half-lives from 3 to 18 days Returned

Tool call · Input 

{
  "description": "Test fine-grained half-lives from 3 to 18 days",
  "command": "python3 -c \"\nimport sys\nsys.path.insert(0, '/task')\nimport time\nimport numpy as np\nimport pandas as pd\nimport scipy.sparse as sp\nfrom sklearn.decomposition import TruncatedSVD\nfrom ranking_evaluation import CandidateSet, score_rankings\nfrom evaluate_public import validation_truth\n\n# 1. Load targets, catalog, and eligibility\ntargets_frame = pd.read_parquet('/task/data/target_users.parquet')\ntargets = set(targets_frame.uid.astype(int))\neligibility = CandidateSet('/task/data/validation_eligible_candidates.npz')\ngraded = validation_truth(eligibility, targets)\n\ntarget_uids = eligibility.uids\nitem_ids = eligibility.item_ids\nuid_to_idx = {uid: i for i, uid in enumerate(target_uids)}\nitem_to_idx = {item: i for i, item in enumerate(item_ids)}\n\nlistens = pd.read_parquet('/task/data/train_listens.parquet', columns=['uid', 'item_id', 'timestamp', 'played_ratio_pct', 'is_organic'])\nlistens = listens[listens.uid.isin(targets) & listens.item_id.isin(item_to_idx)].copy()\n\nlikes = pd.read_parquet('/task/data/train_likes.parquet')\nlikes = likes[likes.uid.isin(targets) & likes.item_id.isin(item_to_idx)].copy()\n\nT_max = 24786800\n\ndef evaluate_predictions(scores, name=''):\n    ranked = {}\n    for i, uid in enumerate(target_uids):\n        user_scores = scores[i].copy()\n        bits = np.unpackbits(eligibility.packed[i], count=30000, bitorder='little')\n        user_scores[bits == 0] = -np.inf\n        top100_idx = np.argpartition(user_scores, -100)[-100:]\n        top100_sorted = top100_idx[np.argsort(-user_scores[top100_idx])]\n        ranked[int(uid)] = list(item_ids[top100_sorted])\n    res = score_rankings(ranked, graded)\n    print(f'{name} -> NDCG@10: {res[\\\"quality_ndcg@10\\\"]}')\n    return res[\\\"quality_ndcg@10\\\"]\n\nbase_weights_l = np.ones(len(listens), dtype=np.float32)\nstrong = listens.played_ratio_pct >= 80\norganic = listens.is_organic == 1\nbase_weights_l[~strong & ~organic] = 1.0\nbase_weights_l[strong & ~organic] = 1.5\nbase_weights_l[~strong & organic] = 2.0\nbase_weights_l[strong & organic] = 3.0\n\nu_l = listens.uid.map(uid_to_idx).to_numpy()\nit_l = listens.item_id.map(item_to_idx).to_numpy()\nts_l = listens.timestamp.to_numpy()\n\nu_lk = likes.uid.map(uid_to_idx).to_numpy()\nit_lk = likes.item_id.map(item_to_idx).to_numpy()\nts_lk = likes.timestamp.to_numpy()\nbase_weights_lk = np.full(len(likes), 5.0, dtype=np.float32)\n\nall_u = np.concatenate([u_l, u_lk])\nall_it = np.concatenate([it_l, it_lk])\nall_ts = np.concatenate([ts_l, ts_lk])\nall_w_base = np.concatenate([base_weights_l, base_weights_lk])\nage_days = (T_max - all_ts) / 86400.0\n\nfor H in [3, 5, 7, 10, 12, 14, 18]:\n    decay = np.power(0.5, age_days / H).astype(np.float32)\n    w = all_w_base * decay\n    R = sp.csr_matrix((w, (all_u, all_it)), shape=(len(target_uids), len(item_ids)))\n    R.data = np.log1p(R.data)\n    svd = TruncatedSVD(n_components=256, algorithm='randomized', random_state=42)\n    U = svd.fit_transform(R)\n    V = svd.components_\n    evaluate_predictions(U @ V, f'Half-life H={H} days')\n\""
}

Tool response 

<untrusted_context>
Output: Half-life H=3 days -> NDCG@10: 0.033782
Half-life H=5 days -> NDCG@10: 0.036387
Half-life H=7 days -> NDCG@10: 0.037852
Half-life H=10 days -> NDCG@10: 0.037834
Half-life H=12 days -> NDCG@10: 0.036565
Half-life H=14 days -> NDCG@10: 0.036082
Half-life H=18 days -> NDCG@10: 0.035692
Process Group PGID: 2305
</untrusted_context>
39

Test blending short-term and medium-term models

+16:15
Recorded reasoning summary 41 words

Optimizing Exposure Duration

I've found that a 7 to 10-day window significantly boosts performance, exceeding the baseline by nearly 3.8 times. My current focus is exploring how to integrate this short-term insight with longer-term taste preferences to further refine our approach.

Tool callrun_shell_commandTest blending short-term and medium-term models Returned

Tool call · Input 

{
  "description": "Test blending short-term and medium-term models",
  "command": "python3 -c \"\nimport sys\nsys.path.insert(0, '/task')\nimport numpy as np\nimport pandas as pd\nimport scipy.sparse as sp\nfrom sklearn.decomposition import TruncatedSVD\nfrom ranking_evaluation import CandidateSet, score_rankings\nfrom evaluate_public import validation_truth\n\n# 1. Load targets, catalog, and eligibility\ntargets_frame = pd.read_parquet('/task/data/target_users.parquet')\ntargets = set(targets_frame.uid.astype(int))\neligibility = CandidateSet('/task/data/validation_eligible_candidates.npz')\ngraded = validation_truth(eligibility, targets)\n\ntarget_uids = eligibility.uids\nitem_ids = eligibility.item_ids\nuid_to_idx = {uid: i for i, uid in enumerate(target_uids)}\nitem_to_idx = {item: i for i, item in enumerate(item_ids)}\n\nlistens = pd.read_parquet('/task/data/train_listens.parquet', columns=['uid', 'item_id', 'timestamp', 'played_ratio_pct', 'is_organic'])\nlistens = listens[listens.uid.isin(targets) & listens.item_id.isin(item_to_idx)].copy()\n\nlikes = pd.read_parquet('/task/data/train_likes.parquet')\nlikes = likes[likes.uid.isin(targets) & likes.item_id.isin(item_to_idx)].copy()\n\nT_max = 24786800\n\ndef evaluate_predictions(scores, name=''):\n    ranked = {}\n    for i, uid in enumerate(target_uids):\n        user_scores = scores[i].copy()\n        bits = np.unpackbits(eligibility.packed[i], count=30000, bitorder='little')\n        user_scores[bits == 0] = -np.inf\n        top100_idx = np.argpartition(user_scores, -100)[-100:]\n        top100_sorted = top100_idx[np.argsort(-user_scores[top100_idx])]\n        ranked[int(uid)] = list(item_ids[top100_sorted])\n    res = score_rankings(ranked, graded)\n    print(f'{name} -> NDCG@10: {res[\\\"quality_ndcg@10\\\"]}')\n    return res[\\\"quality_ndcg@10\\\"]\n\nbase_weights_l = np.ones(len(listens), dtype=np.float32)\nstrong = listens.played_ratio_pct >= 80\norganic = listens.is_organic == 1\nbase_weights_l[~strong & ~organic] = 1.0\nbase_weights_l[strong & ~organic] = 1.5\nbase_weights_l[~strong & organic] = 2.0\nbase_weights_l[strong & organic] = 3.0\n\nu_l = listens.uid.map(uid_to_idx).to_numpy()\nit_l = listens.item_id.map(item_to_idx).to_numpy()\nts_l = listens.timestamp.to_numpy()\n\nu_lk = likes.uid.map(uid_to_idx).to_numpy()\nit_lk = likes.item_id.map(item_to_idx).to_numpy()\nts_lk = likes.timestamp.to_numpy()\nbase_weights_lk = np.full(len(likes), 5.0, dtype=np.float32)\n\nall_u = np.concatenate([u_l, u_lk])\nall_it = np.concatenate([it_l, it_lk])\nall_ts = np.concatenate([ts_l, ts_lk])\nall_w_base = np.concatenate([base_weights_l, base_weights_lk])\nage_days = (T_max - all_ts) / 86400.0\n\n# Model 1: H=7\ndecay7 = np.power(0.5, age_days / 7.0).astype(np.float32)\nR7 = sp.csr_matrix((all_w_base * decay7, (all_u, all_it)), shape=(len(target_uids), len(item_ids)))\nR7.data = np.log1p(R7.data)\nsvd7 = TruncatedSVD(n_components=256, algorithm='randomized', random_state=42)\nU7 = svd7.fit_transform(R7)\nV7 = svd7.components_\nscore7 = U7 @ V7\n\n# Model 2: H=45\ndecay45 = np.power(0.5, age_days / 45.0).astype(np.float32)\nR45 = sp.csr_matrix((all_w_base * decay45, (all_u, all_it)), shape=(len(target_uids), len(item_ids)))\nR45.data = np.log1p(R45.data)\nsvd45 = TruncatedSVD(n_components=256, algorithm='randomized', random_state=42)\nU45 = svd45.fit_transform(R45)\nV45 = svd45.components_\nscore45 = U45 @ V45\n\nevaluate_predictions(score7, 'Model 1 (H=7)')\nevaluate_predictions(score45, 'Model 2 (H=45)')\n\n# Test blend weights\nfor alpha in [0.1, 0.2, 0.3, 0.4, 0.5]:\n    # Normalize standard deviations or scale\n    std7 = np.std(score7)\n    std45 = np.std(score45)\n    blend = (score7 / std7) + alpha * (score45 / std45)\n    evaluate_predictions(blend, f'Blend H=7 + {alpha} * H=45')\n\""
}

Tool response 

<untrusted_context>
Output: /usr/bin/bash: line 98:  2341 Killed                  python3 -c "
import sys
sys.path.insert(0, '/task')
import numpy as np
import pandas as pd
import scipy.sparse as sp
from sklearn.decomposition import TruncatedSVD
from ranking_evaluation import CandidateSet, score_rankings
from evaluate_public import validation_truth

# 1. Load targets, catalog, and eligibility
targets_frame = pd.read_parquet('/task/data/target_users.parquet')
targets = set(targets_frame.uid.astype(int))
eligibility = CandidateSet('/task/data/validation_eligible_candidates.npz')
graded = validation_truth(eligibility, targets)

target_uids = eligibility.uids
item_ids = eligibility.item_ids
uid_to_idx = {uid: i for i, uid in enumerate(target_uids)}
item_to_idx = {item: i for i, item in enumerate(item_ids)}

listens = pd.read_parquet('/task/data/train_listens.parquet', columns=['uid', 'item_id', 'timestamp', 'played_ratio_pct', 'is_organic'])
listens = listens[listens.uid.isin(targets) & listens.item_id.isin(item_to_idx)].copy()

likes = pd.read_parquet('/task/data/train_likes.parquet')
likes = likes[likes.uid.isin(targets) & likes.item_id.isin(item_to_idx)].copy()

T_max = 24786800

def evaluate_predictions(scores, name=''):
    ranked = {}
    for i, uid in enumerate(target_uids):
        user_scores = scores[i].copy()
        bits = np.unpackbits(eligibility.packed[i], count=30000, bitorder='little')
        user_scores[bits == 0] = -np.inf
        top100_idx = np.argpartition(user_scores, -100)[-100:]
        top100_sorted = top100_idx[np.argsort(-user_scores[top100_idx])]
        ranked[int(uid)] = list(item_ids[top100_sorted])
    res = score_rankings(ranked, graded)
    print(f'{name} -> NDCG@10: {res[\"quality_ndcg@10\"]}')
    return res[\"quality_ndcg@10\"]

base_weights_l = np.ones(len(listens), dtype=np.float32)
strong = listens.played_ratio_pct >= 80
organic = listens.is_organic == 1
base_weights_l[~strong & ~organic] = 1.0
base_weights_l[strong & ~organic] = 1.5
base_weights_l[~strong & organic] = 2.0
base_weights_l[strong & organic] = 3.0

u_l = listens.uid.map(uid_to_idx).to_numpy()
it_l = listens.item_id.map(item_to_idx).to_numpy()
ts_l = listens.timestamp.to_numpy()

u_lk = likes.uid.map(uid_to_idx).to_numpy()
it_lk = likes.item_id.map(item_to_idx).to_numpy()
ts_lk = likes.timestamp.to_numpy()
base_weights_lk = np.full(len(likes), 5.0, dtype=np.float32)

all_u = np.concatenate([u_l, u_lk])
all_it = np.concatenate([it_l, it_lk])
all_ts = np.concatenate([ts_l, ts_lk])
all_w_base = np.concatenate([base_weights_l, base_weights_lk])
age_days = (T_max - all_ts) / 86400.0

# Model 1: H=7
decay7 = np.power(0.5, age_days / 7.0).astype(np.float32)
R7 = sp.csr_matrix((all_w_base * decay7, (all_u, all_it)), shape=(len(target_uids), len(item_ids)))
R7.data = np.log1p(R7.data)
svd7 = TruncatedSVD(n_components=256, algorithm='randomized', random_state=42)
U7 = svd7.fit_transform(R7)
V7 = svd7.components_
score7 = U7 @ V7

# Model 2: H=45
decay45 = np.power(0.5, age_days / 45.0).astype(np.float32)
R45 = sp.csr_matrix((all_w_base * decay45, (all_u, all_it)), shape=(len(target_uids), len(item_ids)))
R45.data = np.log1p(R45.data)
svd45 = TruncatedSVD(n_components=256, algorithm='randomized', random_state=42)
U45 = svd45.fit_transform(R45)
V45 = svd45.components_
score45 = U45 @ V45

evaluate_predictions(score7, 'Model 1 (H=7)')
evaluate_predictions(score45, 'Model 2 (H=45)')

# Test blend weights
for alpha in [0.1, 0.2, 0.3, 0.4, 0.5]:
    # Normalize standard deviations or scale
    std7 = np.std(score7)
    std45 = np.std(score45)
    blend = (score7 / std7) + alpha * (score45 / std45)
    evaluate_predictions(blend, f'Blend H=7 + {alpha} * H=45')
"
Exit Code: 137
Process Group PGID: 2339
</untrusted_context>
40

Check row count of train_listens filtered to last 90 days

+16:59
Recorded reasoning summary 40 words

Investigating Memory Bottleneck

I'm currently dissecting the root cause of the "Process was killed" exit code, specifically noting the substantial memory footprint from large sparse and dense matrices, coupled with the svd.fit_transform operation, which I suspect triggered the OOM killer.

Tool callrun_shell_commandCheck row count of train_listens filtered to last 90 days Returned

Tool call · Input 

{
  "command": "python3 -c \"\nimport pandas as pd\nimport gc\n\nt_cut = 24786800\nt_90d = t_cut - 90 * 86400\nt_120d = t_cut - 120 * 86400\n\n# Read with pyarrow filter\ndf_90 = pd.read_parquet(\n    '/task/data/train_listens.parquet',\n    columns=['uid', 'item_id', 'timestamp', 'played_ratio_pct', 'is_organic'],\n    filters=[('timestamp', '>=', t_90d)]\n)\nprint(f'Listens in last 90 days: {len(df_90)} rows (vs 43.4M total)!')\n\"",
  "description": "Check row count of train_listens filtered to last 90 days"
}

Tool response 

<untrusted_context>
Output: Listens in last 90 days: 16769274 rows (vs 43.4M total)!
Process Group PGID: 2373
</untrusted_context>
41

Check validation preference state overlap with catalog

+17:06
Recorded reasoning summary 54 words

Refining Target Users

I'm focusing on defining our target user group. The initial filtering based on a 90-day window and positive listens has loaded a substantial dataset, efficiently discarding stale records. My current thought is to solidify this 90-day criteria for defining users with at least 20 positive listens prior to the training cutoff.

Tool callrun_shell_commandCheck validation preference state overlap with catalog Returned

Tool call · Input 

{
  "description": "Check validation preference state overlap with catalog",
  "command": "python3 -c \"\nimport pandas as pd\npref = pd.read_parquet('/task/data/validation_preference_state.parquet')\ncatalog = pd.read_parquet('/task/data/candidate_catalog.parquet')\ntargets = pd.read_parquet('/task/data/target_users.parquet')\n\ncat_set = set(catalog.item_id)\ntarget_set = set(targets.uid)\n\npref_cat = pref[pref.uid.isin(target_set) & pref.item_id.isin(cat_set)]\nprint('Preference state in catalog:', len(pref_cat))\nprint(pref_cat.state.value_counts())\n\""
}

Tool response 

<untrusted_context>
Output: Preference state in catalog: 469124
state
liked       418435
disliked     50689
Name: count, dtype: int64
Process Group PGID: 2394
</untrusted_context>
42

Evaluate effect of Item IDF weighting on SVD

+17:24
Recorded reasoning summary 29 words

Cataloging preferences complete

I’ve successfully cataloged all 469,124 preference state entries. They accurately reflect the precise net active likes and dislikes at the cutoff, presenting a clean, explicit signal.

Recorded reasoning summary 53 words

Refining the approach

My current focus is on validating the impact of incorporating validation_preference_state and refining our weighting strategies. I'm exploring how to best integrate organic status, completion, and time decay into listen counts, while simultaneously assessing the efficacy of different matrix factorization techniques like SVD against ALS, and considering pre-processing with TF-IDF/BM25.

Tool callrun_shell_commandEvaluate effect of Item IDF weighting on SVD Returned

Tool call · Input 

{
  "description": "Evaluate effect of Item IDF weighting on SVD",
  "command": "python3 -c \"\nimport sys\nsys.path.insert(0, '/task')\nimport gc\nimport time\nimport numpy as np\nimport pandas as pd\nimport scipy.sparse as sp\nfrom sklearn.decomposition import TruncatedSVD\nfrom ranking_evaluation import CandidateSet, score_rankings\nfrom evaluate_public import validation_truth\n\n# 1. Load targets, catalog, and eligibility\ntargets_frame = pd.read_parquet('/task/data/target_users.parquet')\ntargets = set(targets_frame.uid.astype(int))\neligibility = CandidateSet('/task/data/validation_eligible_candidates.npz')\ngraded = validation_truth(eligibility, targets)\n\ntarget_uids = eligibility.uids\nitem_ids = eligibility.item_ids\nuid_to_idx = {uid: i for i, uid in enumerate(target_uids)}\nitem_to_idx = {item: i for i, item in enumerate(item_ids)}\n\nT_cut = 24786800\nt_90d = T_cut - 90 * 86400\n\n# Load filtered listens\nlistens = pd.read_parquet(\n    '/task/data/train_listens.parquet',\n    columns=['uid', 'item_id', 'timestamp', 'played_ratio_pct', 'is_organic'],\n    filters=[('timestamp', '>=', t_90d)]\n)\nlistens = listens[listens.uid.isin(targets) & listens.item_id.isin(item_to_idx)]\n\nlikes = pd.read_parquet(\n    '/task/data/train_likes.parquet',\n    filters=[('timestamp', '>=', t_90d)]\n)\nlikes = likes[likes.uid.isin(targets) & likes.item_id.isin(item_to_idx)]\n\npref = pd.read_parquet('/task/data/validation_preference_state.parquet')\npref = pref[pref.uid.isin(targets) & pref.item_id.isin(item_to_idx)]\n\nprint(f'Filtered loaded: listens={len(listens)}, likes={len(likes)}, pref={len(pref)}')\n\n# Compute weights\nage_days = (T_cut - listens.timestamp.to_numpy()) / 86400.0\ndecay = np.power(0.5, age_days / 8.0).astype(np.float32)\n\nweights = np.ones(len(listens), dtype=np.float32)\nstrong = (listens.played_ratio_pct >= 80).to_numpy()\norganic = (listens.is_organic == 1).to_numpy()\nweights[~strong & ~organic] = 1.0\nweights[strong & ~organic] = 1.5\nweights[~strong & organic] = 2.0\nweights[strong & organic] = 3.0\nweights *= decay\n\nu_l = listens.uid.map(uid_to_idx).to_numpy()\nit_l = listens.item_id.map(item_to_idx).to_numpy()\ndel listens; gc.collect()\n\n# Likes\nage_days_lk = (T_cut - likes.timestamp.to_numpy()) / 86400.0\ndecay_lk = np.power(0.5, age_days_lk / 8.0).astype(np.float32)\nweights_lk = 5.0 * decay_lk\nu_lk = likes.uid.map(uid_to_idx).to_numpy()\nit_lk = likes.item_id.map(item_to_idx).to_numpy()\ndel likes; gc.collect()\n\n# Active preference state (give base weight 3.0 for liked, -2.0 for disliked)\npref_liked = pref[pref.state == 'liked']\nu_pref = pref_liked.uid.map(uid_to_idx).to_numpy()\nit_pref = pref_liked.item_id.map(item_to_idx).to_numpy()\nweights_pref = np.full(len(pref_liked), 3.0, dtype=np.float32)\n\nall_u = np.concatenate([u_l, u_lk, u_pref])\nall_it = np.concatenate([it_l, it_lk, it_pref])\nall_w = np.concatenate([weights, weights_lk, weights_pref])\ndel u_l, it_l, weights, u_lk, it_lk, weights_lk, u_pref, it_pref, weights_pref; gc.collect()\n\nR_raw = sp.csr_matrix((all_w, (all_u, all_it)), shape=(len(target_uids), len(item_ids)))\nR_log = R_raw.copy()\nR_log.data = np.log1p(R_log.data)\n\ndef evaluate_predictions(scores, name=''):\n    ranked = {}\n    for i, uid in enumerate(target_uids):\n        user_scores = scores[i].copy()\n        bits = np.unpackbits(eligibility.packed[i], count=30000, bitorder='little')\n        user_scores[bits == 0] = -np.inf\n        top100_idx = np.argpartition(user_scores, -100)[-100:]\n        top100_sorted = top100_idx[np.argsort(-user_scores[top100_idx])]\n        ranked[int(uid)] = list(item_ids[top100_sorted])\n    res = score_rankings(ranked, graded)\n    print(f'{name} -> NDCG@10: {res[\\\"quality_ndcg@10\\\"]}')\n    return res[\\\"quality_ndcg@10\\\"]\n\n# 1. Without IDF\nsvd = TruncatedSVD(n_components=256, algorithm='randomized', random_state=42)\nU = svd.fit_transform(R_log)\nV = svd.components_\nevaluate_predictions(U @ V, 'Standard log1p, k=256')\n\n# 2. With IDF\nN_users = len(target_uids)\ndoc_freq = np.diff(R_raw.tocsc().indptr) # count of nonzeros per item\nidf = np.log((N_users + 1.0) / (doc_freq + 1.0)) + 1.0\nR_idf = R_log.multiply(sp.diags(idf))\n\nsvd_idf = TruncatedSVD(n_components=256, algorithm='randomized', random_state=42)\nU_idf = svd_idf.fit_transform(R_idf)\nV_idf = svd_idf.components_\nevaluate_predictions(U_idf @ V_idf, 'Log1p + Item IDF, k=256')\n\n# 3. With IDF power 0.5 (smoother)\nidf_half = np.power(idf, 0.5)\nR_idf_half = R_log.multiply(sp.diags(idf_half))\nsvd_idf2 = TruncatedSVD(n_components=256, algorithm='randomized', random_state=42)\nU_idf2 = svd_idf2.fit_transform(R_idf_half)\nV_idf2 = svd_idf2.components_\nevaluate_predictions(U_idf2 @ V_idf2, 'Log1p + Item IDF^0.5, k=256')\n\""
}

Tool response 

<untrusted_context>
Output: Traceback (most recent call last):
  File "<string>", line 109, in <module>
  File "/opt/task-venv/lib/python3.12/site-packages/scipy/sparse/_compressed.py", line 440, in multiply
    raise ValueError("inconsistent shapes")
ValueError: inconsistent shapes
Filtered loaded: listens=12099707, likes=193175, pref=469124
Standard log1p, k=256 -> NDCG@10: 0.037856
Exit Code: 1
Process Group PGID: 2411
</untrusted_context>
43

Run corrected IDF test

+17:50
Recorded reasoning summary 45 words

Clarifying Matrix Multiplication

I've realized my previous approach for multiplying columns by idf was incorrect. The sp.diags(idf) function generates a large sparse matrix, which is incompatible with elementwise multiplication in R_log.multiply. I now understand the correct method is to use matrix multiplication: R_log @ sp.diags(idf).

Tool callrun_shell_commandRun corrected IDF test Returned

Tool call · Input 

{
  "command": "python3 -c \"\nimport sys\nsys.path.insert(0, '/task')\nimport gc\nimport numpy as np\nimport pandas as pd\nimport scipy.sparse as sp\nfrom sklearn.decomposition import TruncatedSVD\nfrom ranking_evaluation import CandidateSet, score_rankings\nfrom evaluate_public import validation_truth\n\n# 1. Load targets, catalog, and eligibility\ntargets_frame = pd.read_parquet('/task/data/target_users.parquet')\ntargets = set(targets_frame.uid.astype(int))\neligibility = CandidateSet('/task/data/validation_eligible_candidates.npz')\ngraded = validation_truth(eligibility, targets)\n\ntarget_uids = eligibility.uids\nitem_ids = eligibility.item_ids\nuid_to_idx = {uid: i for i, uid in enumerate(target_uids)}\nitem_to_idx = {item: i for i, item in enumerate(item_ids)}\n\nT_cut = 24786800\nt_90d = T_cut - 90 * 86400\n\nlistens = pd.read_parquet(\n    '/task/data/train_listens.parquet',\n    columns=['uid', 'item_id', 'timestamp', 'played_ratio_pct', 'is_organic'],\n    filters=[('timestamp', '>=', t_90d)]\n)\nlistens = listens[listens.uid.isin(targets) & listens.item_id.isin(item_to_idx)]\n\nlikes = pd.read_parquet('/task/data/train_likes.parquet', filters=[('timestamp', '>=', t_90d)])\nlikes = likes[likes.uid.isin(targets) & likes.item_id.isin(item_to_idx)]\n\npref = pd.read_parquet('/task/data/validation_preference_state.parquet')\npref = pref[pref.uid.isin(targets) & pref.item_id.isin(item_to_idx)]\n\nage_days = (T_cut - listens.timestamp.to_numpy()) / 86400.0\ndecay = np.power(0.5, age_days / 8.0).astype(np.float32)\n\nweights = np.ones(len(listens), dtype=np.float32)\nstrong = (listens.played_ratio_pct >= 80).to_numpy()\norganic = (listens.is_organic == 1).to_numpy()\nweights[~strong & ~organic] = 1.0\nweights[strong & ~organic] = 1.5\nweights[~strong & organic] = 2.0\nweights[strong & organic] = 3.0\nweights *= decay\n\nu_l = listens.uid.map(uid_to_idx).to_numpy()\nit_l = listens.item_id.map(item_to_idx).to_numpy()\ndel listens; gc.collect()\n\nage_days_lk = (T_cut - likes.timestamp.to_numpy()) / 86400.0\ndecay_lk = np.power(0.5, age_days_lk / 8.0).astype(np.float32)\nweights_lk = 5.0 * decay_lk\nu_lk = likes.uid.map(uid_to_idx).to_numpy()\nit_lk = likes.item_id.map(item_to_idx).to_numpy()\ndel likes; gc.collect()\n\npref_liked = pref[pref.state == 'liked']\nu_pref = pref_liked.uid.map(uid_to_idx).to_numpy()\nit_pref = pref_liked.item_id.map(item_to_idx).to_numpy()\nweights_pref = np.full(len(pref_liked), 3.0, dtype=np.float32)\n\nall_u = np.concatenate([u_l, u_lk, u_pref])\nall_it = np.concatenate([it_l, it_lk, it_pref])\nall_w = np.concatenate([weights, weights_lk, weights_pref])\ndel u_l, it_l, weights, u_lk, it_lk, weights_lk, u_pref, it_pref, weights_pref; gc.collect()\n\nR_raw = sp.csr_matrix((all_w, (all_u, all_it)), shape=(len(target_uids), len(item_ids)))\nR_log = R_raw.copy()\nR_log.data = np.log1p(R_log.data)\n\ndef evaluate_predictions(scores, name=''):\n    ranked = {}\n    for i, uid in enumerate(target_uids):\n        user_scores = scores[i].copy()\n        bits = np.unpackbits(eligibility.packed[i], count=30000, bitorder='little')\n        user_scores[bits == 0] = -np.inf\n        top100_idx = np.argpartition(user_scores, -100)[-100:]\n        top100_sorted = top100_idx[np.argsort(-user_scores[top100_idx])]\n        ranked[int(uid)] = list(item_ids[top100_sorted])\n    res = score_rankings(ranked, graded)\n    print(f'{name} -> NDCG@10: {res[\\\"quality_ndcg@10\\\"]}')\n    return res[\\\"quality_ndcg@10\\\"]\n\n# With IDF\nN_users = len(target_uids)\ndoc_freq = np.diff(R_raw.tocsc().indptr)\nidf = (np.log((N_users + 1.0) / (doc_freq + 1.0)) + 1.0).astype(np.float32)\nR_idf = R_log @ sp.diags(idf)\n\nsvd_idf = TruncatedSVD(n_components=256, algorithm='randomized', random_state=42)\nU_idf = svd_idf.fit_transform(R_idf)\nV_idf = svd_idf.components_\nevaluate_predictions(U_idf @ V_idf, 'Log1p + Item IDF, k=256')\n\n# With IDF^0.5\nidf_half = np.power(idf, 0.5).astype(np.float32)\nR_idf_half = R_log @ sp.diags(idf_half)\nsvd_idf2 = TruncatedSVD(n_components=256, algorithm='randomized', random_state=42)\nU_idf2 = svd_idf2.fit_transform(R_idf_half)\nV_idf2 = svd_idf2.components_\nevaluate_predictions(U_idf2 @ V_idf2, 'Log1p + Item IDF^0.5, k=256')\n\"",
  "description": "Run corrected IDF test"
}

Tool response 

<untrusted_context>
Output: Log1p + Item IDF, k=256 -> NDCG@10: 0.038861
Log1p + Item IDF^0.5, k=256 -> NDCG@10: 0.040174
Process Group PGID: 2445
</untrusted_context>
44

Test singular value exponent alpha on validation NDCG

+18:25
Recorded reasoning summary 37 words

Improving NDCG@10

I'm pleased to report our NDCG@10 has reached 0.040174. This reflects a consistent upward trend from our initial baseline, with our latest IDF-scaled time-decayed SVD model showing significant improvement. I'm now focused on further enhancements.

Recorded reasoning summary 63 words

Refining Latent Factor Scaling

I'm exploring strategies to better balance user and item influences within the latent space. Specifically, I'm investigating the impact of normalizing user vectors before SVD and experimenting with different singular value scaling exponents ($\alpha$) beyond the standard SVD, aiming to reduce the dominance of top eigenvalues. The goal is to improve the model's ability to capture nuanced user preferences.

Tool callrun_shell_commandTest singular value exponent alpha on validation NDCG Returned

Tool call · Input 

{
  "command": "python3 -c \"\nimport sys\nsys.path.insert(0, '/task')\nimport gc\nimport numpy as np\nimport pandas as pd\nimport scipy.sparse as sp\nfrom sklearn.decomposition import TruncatedSVD\nfrom sklearn.preprocessing import normalize\nfrom ranking_evaluation import CandidateSet, score_rankings\nfrom evaluate_public import validation_truth\n\n# 1. Load targets, catalog, and eligibility\ntargets_frame = pd.read_parquet('/task/data/target_users.parquet')\ntargets = set(targets_frame.uid.astype(int))\neligibility = CandidateSet('/task/data/validation_eligible_candidates.npz')\ngraded = validation_truth(eligibility, targets)\n\ntarget_uids = eligibility.uids\nitem_ids = eligibility.item_ids\nuid_to_idx = {uid: i for i, uid in enumerate(target_uids)}\nitem_to_idx = {item: i for i, item in enumerate(item_ids)}\n\nT_cut = 24786800\nt_90d = T_cut - 90 * 86400\n\nlistens = pd.read_parquet(\n    '/task/data/train_listens.parquet',\n    columns=['uid', 'item_id', 'timestamp', 'played_ratio_pct', 'is_organic'],\n    filters=[('timestamp', '>=', t_90d)]\n)\nlistens = listens[listens.uid.isin(targets) & listens.item_id.isin(item_to_idx)]\n\nlikes = pd.read_parquet('/task/data/train_likes.parquet', filters=[('timestamp', '>=', t_90d)])\nlikes = likes[likes.uid.isin(targets) & likes.item_id.isin(item_to_idx)]\n\npref = pd.read_parquet('/task/data/validation_preference_state.parquet')\npref = pref[pref.uid.isin(targets) & pref.item_id.isin(item_to_idx)]\n\nage_days = (T_cut - listens.timestamp.to_numpy()) / 86400.0\ndecay = np.power(0.5, age_days / 8.0).astype(np.float32)\n\nweights = np.ones(len(listens), dtype=np.float32)\nstrong = (listens.played_ratio_pct >= 80).to_numpy()\norganic = (listens.is_organic == 1).to_numpy()\nweights[~strong & ~organic] = 1.0\nweights[strong & ~organic] = 1.5\nweights[~strong & organic] = 2.0\nweights[strong & organic] = 3.0\nweights *= decay\n\nu_l = listens.uid.map(uid_to_idx).to_numpy()\nit_l = listens.item_id.map(item_to_idx).to_numpy()\ndel listens; gc.collect()\n\nage_days_lk = (T_cut - likes.timestamp.to_numpy()) / 86400.0\ndecay_lk = np.power(0.5, age_days_lk / 8.0).astype(np.float32)\nweights_lk = 5.0 * decay_lk\nu_lk = likes.uid.map(uid_to_idx).to_numpy()\nit_lk = likes.item_id.map(item_to_idx).to_numpy()\ndel likes; gc.collect()\n\npref_liked = pref[pref.state == 'liked']\nu_pref = pref_liked.uid.map(uid_to_idx).to_numpy()\nit_pref = pref_liked.item_id.map(item_to_idx).to_numpy()\nweights_pref = np.full(len(pref_liked), 3.0, dtype=np.float32)\n\nall_u = np.concatenate([u_l, u_lk, u_pref])\nall_it = np.concatenate([it_l, it_lk, it_pref])\nall_w = np.concatenate([weights, weights_lk, weights_pref])\ndel u_l, it_l, weights, u_lk, it_lk, weights_lk, u_pref, it_pref, weights_pref; gc.collect()\n\nR_raw = sp.csr_matrix((all_w, (all_u, all_it)), shape=(len(target_uids), len(item_ids)))\nR_log = R_raw.copy()\nR_log.data = np.log1p(R_log.data)\n\nN_users = len(target_uids)\ndoc_freq = np.diff(R_raw.tocsc().indptr)\nidf = (np.log((N_users + 1.0) / (doc_freq + 1.0)) + 1.0).astype(np.float32)\nidf_half = np.power(idf, 0.5).astype(np.float32)\nR_idf = R_log @ sp.diags(idf_half)\n\ndef evaluate_predictions(scores, name=''):\n    ranked = {}\n    for i, uid in enumerate(target_uids):\n        user_scores = scores[i].copy()\n        bits = np.unpackbits(eligibility.packed[i], count=30000, bitorder='little')\n        user_scores[bits == 0] = -np.inf\n        top100_idx = np.argpartition(user_scores, -100)[-100:]\n        top100_sorted = top100_idx[np.argsort(-user_scores[top100_idx])]\n        ranked[int(uid)] = list(item_ids[top100_sorted])\n    res = score_rankings(ranked, graded)\n    print(f'{name} -> NDCG@10: {res[\\\"quality_ndcg@10\\\"]}')\n    return res[\\\"quality_ndcg@10\\\"]\n\nsvd = TruncatedSVD(n_components=300, algorithm='randomized', random_state=42)\nU_sigma = svd.fit_transform(R_idf) # U * Sigma\nsigma = svd.singular_values_ # Sigma\nV = svd.components_ # V^T\nU_ortho = U_sigma / sigma # orthonormal U\n\nfor alpha in [1.0, 0.8, 0.6, 0.5, 0.4, 0.2, 0.0]:\n    scores = (U_ortho * (sigma ** alpha)) @ V\n    evaluate_predictions(scores, f'Sigma power alpha={alpha}')\n\"",
  "description": "Test singular value exponent alpha on validation NDCG"
}

Tool response 

<untrusted_context>
Output: Sigma power alpha=1.0 -> NDCG@10: 0.039454
Sigma power alpha=0.8 -> NDCG@10: 0.037045
Sigma power alpha=0.6 -> NDCG@10: 0.035139
Sigma power alpha=0.5 -> NDCG@10: 0.034337
Sigma power alpha=0.4 -> NDCG@10: 0.033474
Sigma power alpha=0.2 -> NDCG@10: 0.031411
Sigma power alpha=0.0 -> NDCG@10: 0.029817
Process Group PGID: 2479
</untrusted_context>
45

Test dimensions k and row normalization

+19:03
Recorded reasoning summary 46 words

Exploring Dimension Effects

My current focus is on the impact of the dimension parameter, $k$. I've observed an improvement in performance by increasing it from 256 to 300, with the score dropping from 0.040174 to 0.039454. I'm now considering further increases to ascertain the optimal value.

Tool callrun_shell_commandTest dimensions k and row normalization Returned

Tool call · Input 

{
  "command": "python3 -c \"\nimport sys\nsys.path.insert(0, '/task')\nimport gc\nimport numpy as np\nimport pandas as pd\nimport scipy.sparse as sp\nfrom sklearn.decomposition import TruncatedSVD\nfrom sklearn.preprocessing import normalize\nfrom ranking_evaluation import CandidateSet, score_rankings\nfrom evaluate_public import validation_truth\n\n# 1. Load targets, catalog, and eligibility\ntargets_frame = pd.read_parquet('/task/data/target_users.parquet')\ntargets = set(targets_frame.uid.astype(int))\neligibility = CandidateSet('/task/data/validation_eligible_candidates.npz')\ngraded = validation_truth(eligibility, targets)\n\ntarget_uids = eligibility.uids\nitem_ids = eligibility.item_ids\nuid_to_idx = {uid: i for i, uid in enumerate(target_uids)}\nitem_to_idx = {item: i for i, item in enumerate(item_ids)}\n\nT_cut = 24786800\nt_90d = T_cut - 90 * 86400\n\nlistens = pd.read_parquet(\n    '/task/data/train_listens.parquet',\n    columns=['uid', 'item_id', 'timestamp', 'played_ratio_pct', 'is_organic'],\n    filters=[('timestamp', '>=', t_90d)]\n)\nlistens = listens[listens.uid.isin(targets) & listens.item_id.isin(item_to_idx)]\n\nlikes = pd.read_parquet('/task/data/train_likes.parquet', filters=[('timestamp', '>=', t_90d)])\nlikes = likes[likes.uid.isin(targets) & likes.item_id.isin(item_to_idx)]\n\npref = pd.read_parquet('/task/data/validation_preference_state.parquet')\npref = pref[pref.uid.isin(targets) & pref.item_id.isin(item_to_idx)]\n\nage_days = (T_cut - listens.timestamp.to_numpy()) / 86400.0\ndecay = np.power(0.5, age_days / 8.0).astype(np.float32)\n\nweights = np.ones(len(listens), dtype=np.float32)\nstrong = (listens.played_ratio_pct >= 80).to_numpy()\norganic = (listens.is_organic == 1).to_numpy()\nweights[~strong & ~organic] = 1.0\nweights[strong & ~organic] = 1.5\nweights[~strong & organic] = 2.0\nweights[strong & organic] = 3.0\nweights *= decay\n\nu_l = listens.uid.map(uid_to_idx).to_numpy()\nit_l = listens.item_id.map(item_to_idx).to_numpy()\ndel listens; gc.collect()\n\nage_days_lk = (T_cut - likes.timestamp.to_numpy()) / 86400.0\ndecay_lk = np.power(0.5, age_days_lk / 8.0).astype(np.float32)\nweights_lk = 5.0 * decay_lk\nu_lk = likes.uid.map(uid_to_idx).to_numpy()\nit_lk = likes.item_id.map(item_to_idx).to_numpy()\ndel likes; gc.collect()\n\npref_liked = pref[pref.state == 'liked']\nu_pref = pref_liked.uid.map(uid_to_idx).to_numpy()\nit_pref = pref_liked.item_id.map(item_to_idx).to_numpy()\nweights_pref = np.full(len(pref_liked), 3.0, dtype=np.float32)\n\nall_u = np.concatenate([u_l, u_lk, u_pref])\nall_it = np.concatenate([it_l, it_lk, it_pref])\nall_w = np.concatenate([weights, weights_lk, weights_pref])\ndel u_l, it_l, weights, u_lk, it_lk, weights_lk, u_pref, it_pref, weights_pref; gc.collect()\n\nR_raw = sp.csr_matrix((all_w, (all_u, all_it)), shape=(len(target_uids), len(item_ids)))\nR_log = R_raw.copy()\nR_log.data = np.log1p(R_log.data)\n\nN_users = len(target_uids)\ndoc_freq = np.diff(R_raw.tocsc().indptr)\nidf = (np.log((N_users + 1.0) / (doc_freq + 1.0)) + 1.0).astype(np.float32)\nidf_half = np.power(idf, 0.5).astype(np.float32)\nR_idf = R_log @ sp.diags(idf_half)\n\ndef evaluate_predictions(scores, name=''):\n    ranked = {}\n    for i, uid in enumerate(target_uids):\n        user_scores = scores[i].copy()\n        bits = np.unpackbits(eligibility.packed[i], count=30000, bitorder='little')\n        user_scores[bits == 0] = -np.inf\n        top100_idx = np.argpartition(user_scores, -100)[-100:]\n        top100_sorted = top100_idx[np.argsort(-user_scores[top100_idx])]\n        ranked[int(uid)] = list(item_ids[top100_sorted])\n    res = score_rankings(ranked, graded)\n    print(f'{name} -> NDCG@10: {res[\\\"quality_ndcg@10\\\"]}')\n    return res[\\\"quality_ndcg@10\\\"]\n\nfor k in [128, 160, 192, 224, 256]:\n    svd = TruncatedSVD(n_components=k, algorithm='randomized', random_state=42)\n    U = svd.fit_transform(R_idf)\n    V = svd.components_\n    evaluate_predictions(U @ V, f'R_idf, k={k}')\n\n# Test L2 row normalization\nR_norm = normalize(R_idf, norm='l2', axis=1)\nfor k in [160, 224, 256]:\n    svd = TruncatedSVD(n_components=k, algorithm='randomized', random_state=42)\n    U = svd.fit_transform(R_norm)\n    V = svd.components_\n    evaluate_predictions(U @ V, f'R_norm (L2), k={k}')\n\"",
  "description": "Test dimensions k and row normalization"
}

Tool response 

<untrusted_context>
Output: R_idf, k=128 -> NDCG@10: 0.040999
R_idf, k=160 -> NDCG@10: 0.041634
R_idf, k=192 -> NDCG@10: 0.039876
R_idf, k=224 -> NDCG@10: 0.040027
R_idf, k=256 -> NDCG@10: 0.040174
R_norm (L2), k=160 -> NDCG@10: 0.037777
R_norm (L2), k=224 -> NDCG@10: 0.036706
R_norm (L2), k=256 -> NDCG@10: 0.036614
Process Group PGID: 2513
</untrusted_context>
46

Grid search on half-life H and IDF power p

+20:05
Recorded reasoning summary 44 words

Evaluating R_idf Parameters

I'm excited by the R_idf results, hitting NDCG@10 at 0.041634 with k=160. It's interesting that L2 normalization actually decreased performance, suggesting that unnormalized user magnitude offers a valuable insight into engagement levels. I'm now considering the next steps to refine this.

Tool callrun_shell_commandGrid search on half-life H and IDF power p Returned

Tool call · Input 

{
  "description": "Grid search on half-life H and IDF power p",
  "command": "python3 -c \"\nimport sys\nsys.path.insert(0, '/task')\nimport gc\nimport numpy as np\nimport pandas as pd\nimport scipy.sparse as sp\nfrom sklearn.decomposition import TruncatedSVD\nfrom ranking_evaluation import CandidateSet, score_rankings\nfrom evaluate_public import validation_truth\n\n# 1. Load targets, catalog, and eligibility\ntargets_frame = pd.read_parquet('/task/data/target_users.parquet')\ntargets = set(targets_frame.uid.astype(int))\neligibility = CandidateSet('/task/data/validation_eligible_candidates.npz')\ngraded = validation_truth(eligibility, targets)\n\ntarget_uids = eligibility.uids\nitem_ids = eligibility.item_ids\nuid_to_idx = {uid: i for i, uid in enumerate(target_uids)}\nitem_to_idx = {item: i for i, item in enumerate(item_ids)}\n\nT_cut = 24786800\nt_90d = T_cut - 90 * 86400\n\nlistens = pd.read_parquet(\n    '/task/data/train_listens.parquet',\n    columns=['uid', 'item_id', 'timestamp', 'played_ratio_pct', 'is_organic'],\n    filters=[('timestamp', '>=', t_90d)]\n)\nlistens = listens[listens.uid.isin(targets) & listens.item_id.isin(item_to_idx)]\n\nlikes = pd.read_parquet('/task/data/train_likes.parquet', filters=[('timestamp', '>=', t_90d)])\nlikes = likes[likes.uid.isin(targets) & likes.item_id.isin(item_to_idx)]\n\npref = pd.read_parquet('/task/data/validation_preference_state.parquet')\npref = pref[pref.uid.isin(targets) & pref.item_id.isin(item_to_idx)]\n\npref_liked = pref[pref.state == 'liked']\nu_pref = pref_liked.uid.map(uid_to_idx).to_numpy()\nit_pref = pref_liked.item_id.map(item_to_idx).to_numpy()\nweights_pref = np.full(len(pref_liked), 3.0, dtype=np.float32)\n\ndef evaluate_predictions(scores, name=''):\n    ranked = {}\n    for i, uid in enumerate(target_uids):\n        user_scores = scores[i].copy()\n        bits = np.unpackbits(eligibility.packed[i], count=30000, bitorder='little')\n        user_scores[bits == 0] = -np.inf\n        top100_idx = np.argpartition(user_scores, -100)[-100:]\n        top100_sorted = top100_idx[np.argsort(-user_scores[top100_idx])]\n        ranked[int(uid)] = list(item_ids[top100_sorted])\n    res = score_rankings(ranked, graded)\n    print(f'{name} -> NDCG@10: {res[\\\"quality_ndcg@10\\\"]}')\n    return res[\\\"quality_ndcg@10\\\"]\n\nage_days_l = (T_cut - listens.timestamp.to_numpy()) / 86400.0\nage_days_lk = (T_cut - likes.timestamp.to_numpy()) / 86400.0\n\nweights_l_base = np.ones(len(listens), dtype=np.float32)\nstrong = (listens.played_ratio_pct >= 80).to_numpy()\norganic = (listens.is_organic == 1).to_numpy()\nweights_l_base[~strong & ~organic] = 1.0\nweights_l_base[strong & ~organic] = 1.5\nweights_l_base[~strong & organic] = 2.0\nweights_l_base[strong & organic] = 3.0\n\nu_l = listens.uid.map(uid_to_idx).to_numpy()\nit_l = listens.item_id.map(item_to_idx).to_numpy()\nu_lk = likes.uid.map(uid_to_idx).to_numpy()\nit_lk = likes.item_id.map(item_to_idx).to_numpy()\n\nfor H in [5.0, 7.0, 8.0, 9.0, 10.0]:\n    decay_l = np.power(0.5, age_days_l / H).astype(np.float32)\n    decay_lk = np.power(0.5, age_days_lk / H).astype(np.float32)\n    \n    all_u = np.concatenate([u_l, u_lk, u_pref])\n    all_it = np.concatenate([it_l, it_lk, it_pref])\n    all_w = np.concatenate([weights_l_base * decay_l, 5.0 * decay_lk, weights_pref])\n    \n    R = sp.csr_matrix((all_w, (all_u, all_it)), shape=(len(target_uids), len(item_ids)))\n    R.data = np.log1p(R.data)\n    \n    N_users = len(target_uids)\n    doc_freq = np.diff(R.tocsc().indptr)\n    idf = (np.log((N_users + 1.0) / (doc_freq + 1.0)) + 1.0).astype(np.float32)\n    \n    for p in [0.3, 0.4, 0.5, 0.6]:\n        R_p = R @ sp.diags(np.power(idf, p).astype(np.float32))\n        svd = TruncatedSVD(n_components=160, algorithm='randomized', random_state=42)\n        U = svd.fit_transform(R_p)\n        V = svd.components_\n        evaluate_predictions(U @ V, f'H={H}, p={p}')\n\""
}

Tool response 

<untrusted_context>
Output: H=5.0, p=0.3 -> NDCG@10: 0.04074
H=5.0, p=0.4 -> NDCG@10: 0.040963
H=5.0, p=0.5 -> NDCG@10: 0.041177
H=5.0, p=0.6 -> NDCG@10: 0.041378
H=7.0, p=0.3 -> NDCG@10: 0.040681
H=7.0, p=0.4 -> NDCG@10: 0.040945
H=7.0, p=0.5 -> NDCG@10: 0.041128
H=7.0, p=0.6 -> NDCG@10: 0.041682
H=8.0, p=0.3 -> NDCG@10: 0.040347
H=8.0, p=0.4 -> NDCG@10: 0.040697
H=8.0, p=0.5 -> NDCG@10: 0.041634
H=8.0, p=0.6 -> NDCG@10: 0.04126
H=9.0, p=0.3 -> NDCG@10: 0.039766
H=9.0, p=0.4 -> NDCG@10: 0.040537
H=9.0, p=0.5 -> NDCG@10: 0.04086
H=9.0, p=0.6 -> NDCG@10: 0.040916
H=10.0, p=0.3 -> NDCG@10: 0.03948
H=10.0, p=0.4 -> NDCG@10: 0.040259
H=10.0, p=0.5 -> NDCG@10: 0.039998
H=10.0, p=0.6 -> NDCG@10: 0.040176
Process Group PGID: 2547
</untrusted_context>
47

Test negative preference weights for dislikes

+22:18
Recorded reasoning summary 34 words

Exploring Dislikes' Impact

I'm now investigating the effect of incorporating dislikes as negative signals. Specifically, I'm examining the train_dislikes.parquet dataset and validation_preference_state where the state is 'disliked' to see how this penalization influences performance.

Tool callrun_shell_commandTest negative preference weights for dislikes Returned

Tool call · Input 

{
  "command": "python3 -c \"\nimport sys\nsys.path.insert(0, '/task')\nimport gc\nimport numpy as np\nimport pandas as pd\nimport scipy.sparse as sp\nfrom sklearn.decomposition import TruncatedSVD\nfrom ranking_evaluation import CandidateSet, score_rankings\nfrom evaluate_public import validation_truth\n\n# 1. Load targets, catalog, and eligibility\ntargets_frame = pd.read_parquet('/task/data/target_users.parquet')\ntargets = set(targets_frame.uid.astype(int))\neligibility = CandidateSet('/task/data/validation_eligible_candidates.npz')\ngraded = validation_truth(eligibility, targets)\n\ntarget_uids = eligibility.uids\nitem_ids = eligibility.item_ids\nuid_to_idx = {uid: i for i, uid in enumerate(target_uids)}\nitem_to_idx = {item: i for i, item in enumerate(item_ids)}\n\nT_cut = 24786800\nt_90d = T_cut - 90 * 86400\n\nlistens = pd.read_parquet(\n    '/task/data/train_listens.parquet',\n    columns=['uid', 'item_id', 'timestamp', 'played_ratio_pct', 'is_organic'],\n    filters=[('timestamp', '>=', t_90d)]\n)\nlistens = listens[listens.uid.isin(targets) & listens.item_id.isin(item_to_idx)]\n\nlikes = pd.read_parquet('/task/data/train_likes.parquet', filters=[('timestamp', '>=', t_90d)])\nlikes = likes[likes.uid.isin(targets) & likes.item_id.isin(item_to_idx)]\n\npref = pd.read_parquet('/task/data/validation_preference_state.parquet')\npref = pref[pref.uid.isin(targets) & pref.item_id.isin(item_to_idx)]\n\npref_liked = pref[pref.state == 'liked']\nu_pref_l = pref_liked.uid.map(uid_to_idx).to_numpy()\nit_pref_l = pref_liked.item_id.map(item_to_idx).to_numpy()\nw_pref_l = np.full(len(pref_liked), 3.0, dtype=np.float32)\n\npref_disliked = pref[pref.state == 'disliked']\nu_pref_d = pref_disliked.uid.map(uid_to_idx).to_numpy()\nit_pref_d = pref_disliked.item_id.map(item_to_idx).to_numpy()\n\nage_days_l = (T_cut - listens.timestamp.to_numpy()) / 86400.0\nage_days_lk = (T_cut - likes.timestamp.to_numpy()) / 86400.0\n\nH = 7.0\ndecay_l = np.power(0.5, age_days_l / H).astype(np.float32)\ndecay_lk = np.power(0.5, age_days_lk / H).astype(np.float32)\n\nweights_l_base = np.ones(len(listens), dtype=np.float32)\nstrong = (listens.played_ratio_pct >= 80).to_numpy()\norganic = (listens.is_organic == 1).to_numpy()\nweights_l_base[~strong & ~organic] = 1.0\nweights_l_base[strong & ~organic] = 1.5\nweights_l_base[~strong & organic] = 2.0\nweights_l_base[strong & organic] = 3.0\n\nu_l = listens.uid.map(uid_to_idx).to_numpy()\nit_l = listens.item_id.map(item_to_idx).to_numpy()\nu_lk = likes.uid.map(uid_to_idx).to_numpy()\nit_lk = likes.item_id.map(item_to_idx).to_numpy()\n\nall_u = np.concatenate([u_l, u_lk, u_pref_l])\nall_it = np.concatenate([it_l, it_lk, it_pref_l])\nall_w = np.concatenate([weights_l_base * decay_l, 5.0 * decay_lk, w_pref_l])\n\ndef evaluate_predictions(scores, name=''):\n    ranked = {}\n    for i, uid in enumerate(target_uids):\n        user_scores = scores[i].copy()\n        bits = np.unpackbits(eligibility.packed[i], count=30000, bitorder='little')\n        user_scores[bits == 0] = -np.inf\n        top100_idx = np.argpartition(user_scores, -100)[-100:]\n        top100_sorted = top100_idx[np.argsort(-user_scores[top100_idx])]\n        ranked[int(uid)] = list(item_ids[top100_sorted])\n    res = score_rankings(ranked, graded)\n    print(f'{name} -> NDCG@10: {res[\\\"quality_ndcg@10\\\"]}')\n    return res[\\\"quality_ndcg@10\\\"]\n\nfor neg_w in [0.0, -1.0, -3.0, -5.0]:\n    if neg_w == 0.0:\n        cur_u, cur_it, cur_w = all_u, all_it, all_w\n    else:\n        w_pref_d = np.full(len(pref_disliked), neg_w, dtype=np.float32)\n        cur_u = np.concatenate([all_u, u_pref_d])\n        cur_it = np.concatenate([all_it, it_pref_d])\n        cur_w = np.concatenate([all_w, w_pref_d])\n    \n    R = sp.csr_matrix((cur_w, (cur_u, cur_it)), shape=(len(target_uids), len(item_ids)))\n    # Sign-preserving log1p: sign(x) * log1p(abs(x))\n    R.data = np.sign(R.data) * np.log1p(np.abs(R.data))\n    \n    N_users = len(target_uids)\n    doc_freq = np.diff(R.tocsc().indptr)\n    idf = (np.log((N_users + 1.0) / (doc_freq + 1.0)) + 1.0).astype(np.float32)\n    R_p = R @ sp.diags(np.power(idf, 0.6).astype(np.float32))\n    \n    svd = TruncatedSVD(n_components=160, algorithm='randomized', random_state=42)\n    U = svd.fit_transform(R_p)\n    V = svd.components_\n    evaluate_predictions(U @ V, f'neg_weight={neg_w}')\n\"",
  "description": "Test negative preference weights for dislikes"
}

Tool response 

<untrusted_context>
Output: neg_weight=0.0 -> NDCG@10: 0.041682
neg_weight=-1.0 -> NDCG@10: 0.041229
neg_weight=-3.0 -> NDCG@10: 0.040782
neg_weight=-5.0 -> NDCG@10: 0.041575
Process Group PGID: 2587
</untrusted_context>
48

Evaluate popularity prior blended with CF scores

+23:00
Recorded reasoning summary 41 words

Refining Preference Matrix

I've adjusted the neg_weight to 0.0, which effectively excludes disliked items from the positive preference matrix. This approach is cleaner and has shown a slight improvement in results, as explicit dislikes are already filtered out of candidate eligibility.

Tool callrun_shell_commandEvaluate popularity prior blended with CF scores Returned

Tool call · Input 

{
  "description": "Evaluate popularity prior blended with CF scores",
  "command": "python3 -c \"\nimport sys\nsys.path.insert(0, '/task')\nimport gc\nimport numpy as np\nimport pandas as pd\nimport scipy.sparse as sp\nfrom sklearn.decomposition import TruncatedSVD\nfrom ranking_evaluation import CandidateSet, score_rankings\nfrom evaluate_public import validation_truth\n\n# 1. Load targets, catalog, and eligibility\ntargets_frame = pd.read_parquet('/task/data/target_users.parquet')\ntargets = set(targets_frame.uid.astype(int))\neligibility = CandidateSet('/task/data/validation_eligible_candidates.npz')\ngraded = validation_truth(eligibility, targets)\ncatalog = pd.read_parquet('/task/data/candidate_catalog.parquet')\n\ntarget_uids = eligibility.uids\nitem_ids = eligibility.item_ids\nuid_to_idx = {uid: i for i, uid in enumerate(target_uids)}\nitem_to_idx = {item: i for i, item in enumerate(item_ids)}\n\n# Prepare catalog popularity vector aligned with item_ids\ncat_map = {row.item_id: row for row in catalog.itertuples()}\norg_pop = np.array([cat_map[it].organic_positive_listens for it in item_ids], dtype=np.float32)\ntot_pop = np.array([cat_map[it].positive_listens for it in item_ids], dtype=np.float32)\nlog_org = np.log1p(org_pop)\nlog_tot = np.log1p(tot_pop)\n\nT_cut = 24786800\nt_90d = T_cut - 90 * 86400\n\nlistens = pd.read_parquet(\n    '/task/data/train_listens.parquet',\n    columns=['uid', 'item_id', 'timestamp', 'played_ratio_pct', 'is_organic'],\n    filters=[('timestamp', '>=', t_90d)]\n)\nlistens = listens[listens.uid.isin(targets) & listens.item_id.isin(item_to_idx)]\n\nlikes = pd.read_parquet('/task/data/train_likes.parquet', filters=[('timestamp', '>=', t_90d)])\nlikes = likes[likes.uid.isin(targets) & likes.item_id.isin(item_to_idx)]\n\npref = pd.read_parquet('/task/data/validation_preference_state.parquet')\npref = pref[pref.uid.isin(targets) & pref.item_id.isin(item_to_idx)]\n\npref_liked = pref[pref.state == 'liked']\nu_pref_l = pref_liked.uid.map(uid_to_idx).to_numpy()\nit_pref_l = pref_liked.item_id.map(item_to_idx).to_numpy()\nw_pref_l = np.full(len(pref_liked), 3.0, dtype=np.float32)\n\nage_days_l = (T_cut - listens.timestamp.to_numpy()) / 86400.0\nage_days_lk = (T_cut - likes.timestamp.to_numpy()) / 86400.0\n\nH = 7.0\ndecay_l = np.power(0.5, age_days_l / H).astype(np.float32)\ndecay_lk = np.power(0.5, age_days_lk / H).astype(np.float32)\n\nweights_l_base = np.ones(len(listens), dtype=np.float32)\nstrong = (listens.played_ratio_pct >= 80).to_numpy()\norganic = (listens.is_organic == 1).to_numpy()\nweights_l_base[~strong & ~organic] = 1.0\nweights_l_base[strong & ~organic] = 1.5\nweights_l_base[~strong & organic] = 2.0\nweights_l_base[strong & organic] = 3.0\n\nu_l = listens.uid.map(uid_to_idx).to_numpy()\nit_l = listens.item_id.map(item_to_idx).to_numpy()\nu_lk = likes.uid.map(uid_to_idx).to_numpy()\nit_lk = likes.item_id.map(item_to_idx).to_numpy()\n\nall_u = np.concatenate([u_l, u_lk, u_pref_l])\nall_it = np.concatenate([it_l, it_lk, it_pref_l])\nall_w = np.concatenate([weights_l_base * decay_l, 5.0 * decay_lk, w_pref_l])\n\nR = sp.csr_matrix((all_w, (all_u, all_it)), shape=(len(target_uids), len(item_ids)))\nR.data = np.log1p(R.data)\n\nN_users = len(target_uids)\ndoc_freq = np.diff(R.tocsc().indptr)\nidf = (np.log((N_users + 1.0) / (doc_freq + 1.0)) + 1.0).astype(np.float32)\nR_p = R @ sp.diags(np.power(idf, 0.6).astype(np.float32))\n\nsvd = TruncatedSVD(n_components=160, algorithm='randomized', random_state=42)\nU = svd.fit_transform(R_p)\nV = svd.components_\ncf_scores = U @ V # (8456, 30000)\n\ndef evaluate_predictions(scores, name=''):\n    ranked = {}\n    for i, uid in enumerate(target_uids):\n        user_scores = scores[i].copy()\n        bits = np.unpackbits(eligibility.packed[i], count=30000, bitorder='little')\n        user_scores[bits == 0] = -np.inf\n        top100_idx = np.argpartition(user_scores, -100)[-100:]\n        top100_sorted = top100_idx[np.argsort(-user_scores[top100_idx])]\n        ranked[int(uid)] = list(item_ids[top100_sorted])\n    res = score_rankings(ranked, graded)\n    print(f'{name} -> NDCG@10: {res[\\\"quality_ndcg@10\\\"]}')\n    return res[\\\"quality_ndcg@10\\\"]\n\nevaluate_predictions(cf_scores, 'Pure CF scores')\n\n# Standardize CF scores\ncf_std = np.std(cf_scores)\npop_std = np.std(log_org)\n\nfor beta in [0.01, 0.02, 0.05, 0.1, 0.2]:\n    comb = (cf_scores / cf_std) + beta * (log_org / pop_std)\n    evaluate_predictions(comb, f'CF + {beta} * organic_pop')\n\""
}

Tool response 

<untrusted_context>
Output: Pure CF scores -> NDCG@10: 0.041682
CF + 0.01 * organic_pop -> NDCG@10: 0.041632
CF + 0.02 * organic_pop -> NDCG@10: 0.041607
CF + 0.05 * organic_pop -> NDCG@10: 0.041483
CF + 0.1 * organic_pop -> NDCG@10: 0.04117
CF + 0.2 * organic_pop -> NDCG@10: 0.041172
Process Group PGID: 2621
</untrusted_context>
49

Test multi-timescale model blending

+23:38
Recorded reasoning summary 33 words

Refining Recommendations

My focus has shifted to exploring ensembling and multi-scale representations. I've confirmed that pure CF effectively captures personalization and discovery, but I'm seeing how pure popularity can negatively impact discovery metrics.

Tool callrun_shell_commandTest multi-timescale model blending Returned

Tool call · Input 

{
  "description": "Test multi-timescale model blending",
  "command": "python3 -c \"\nimport sys\nsys.path.insert(0, '/task')\nimport gc\nimport numpy as np\nimport pandas as pd\nimport scipy.sparse as sp\nfrom sklearn.decomposition import TruncatedSVD\nfrom ranking_evaluation import CandidateSet, score_rankings\nfrom evaluate_public import validation_truth\n\ntargets_frame = pd.read_parquet('/task/data/target_users.parquet')\ntargets = set(targets_frame.uid.astype(int))\neligibility = CandidateSet('/task/data/validation_eligible_candidates.npz')\ngraded = validation_truth(eligibility, targets)\n\ntarget_uids = eligibility.uids\nitem_ids = eligibility.item_ids\nuid_to_idx = {uid: i for i, uid in enumerate(target_uids)}\nitem_to_idx = {item: i for i, item in enumerate(item_ids)}\n\nT_cut = 24786800\nt_90d = T_cut - 90 * 86400\n\nlistens = pd.read_parquet(\n    '/task/data/train_listens.parquet',\n    columns=['uid', 'item_id', 'timestamp', 'played_ratio_pct', 'is_organic'],\n    filters=[('timestamp', '>=', t_90d)]\n)\nlistens = listens[listens.uid.isin(targets) & listens.item_id.isin(item_to_idx)]\n\nlikes = pd.read_parquet('/task/data/train_likes.parquet', filters=[('timestamp', '>=', t_90d)])\nlikes = likes[likes.uid.isin(targets) & likes.item_id.isin(item_to_idx)]\n\npref = pd.read_parquet('/task/data/validation_preference_state.parquet')\npref = pref[pref.uid.isin(targets) & pref.item_id.isin(item_to_idx)]\n\npref_liked = pref[pref.state == 'liked']\nu_pref_l = pref_liked.uid.map(uid_to_idx).to_numpy()\nit_pref_l = pref_liked.item_id.map(item_to_idx).to_numpy()\nw_pref_l = np.full(len(pref_liked), 3.0, dtype=np.float32)\n\nage_days_l = (T_cut - listens.timestamp.to_numpy()) / 86400.0\nage_days_lk = (T_cut - likes.timestamp.to_numpy()) / 86400.0\n\nweights_l_base = np.ones(len(listens), dtype=np.float32)\nstrong = (listens.played_ratio_pct >= 80).to_numpy()\norganic = (listens.is_organic == 1).to_numpy()\nweights_l_base[~strong & ~organic] = 1.0\nweights_l_base[strong & ~organic] = 1.5\nweights_l_base[~strong & organic] = 2.0\nweights_l_base[strong & organic] = 3.0\n\nu_l = listens.uid.map(uid_to_idx).to_numpy()\nit_l = listens.item_id.map(item_to_idx).to_numpy()\nu_lk = likes.uid.map(uid_to_idx).to_numpy()\nit_lk = likes.item_id.map(item_to_idx).to_numpy()\n\ndef get_scores_for_H(H, k=160, idf_p=0.6, rand_state=42):\n    decay_l = np.power(0.5, age_days_l / H).astype(np.float32)\n    decay_lk = np.power(0.5, age_days_lk / H).astype(np.float32)\n    \n    all_u = np.concatenate([u_l, u_lk, u_pref_l])\n    all_it = np.concatenate([it_l, it_lk, it_pref_l])\n    all_w = np.concatenate([weights_l_base * decay_l, 5.0 * decay_lk, w_pref_l])\n    \n    R = sp.csr_matrix((all_w, (all_u, all_it)), shape=(len(target_uids), len(item_ids)))\n    R.data = np.log1p(R.data)\n    \n    N_users = len(target_uids)\n    doc_freq = np.diff(R.tocsc().indptr)\n    idf = (np.log((N_users + 1.0) / (doc_freq + 1.0)) + 1.0).astype(np.float32)\n    R_p = R @ sp.diags(np.power(idf, idf_p).astype(np.float32))\n    \n    svd = TruncatedSVD(n_components=k, algorithm='randomized', random_state=rand_state)\n    U = svd.fit_transform(R_p)\n    V = svd.components_\n    return U @ V\n\ndef evaluate_predictions(scores, name=''):\n    ranked = {}\n    for i, uid in enumerate(target_uids):\n        user_scores = scores[i].copy()\n        bits = np.unpackbits(eligibility.packed[i], count=30000, bitorder='little')\n        user_scores[bits == 0] = -np.inf\n        top100_idx = np.argpartition(user_scores, -100)[-100:]\n        top100_sorted = top100_idx[np.argsort(-user_scores[top100_idx])]\n        ranked[int(uid)] = list(item_ids[top100_sorted])\n    res = score_rankings(ranked, graded)\n    print(f'{name} -> NDCG@10: {res[\\\"quality_ndcg@10\\\"]}')\n    return res[\\\"quality_ndcg@10\\\"]\n\ns_short = get_scores_for_H(5.0, k=160, rand_state=42)\ns_mid = get_scores_for_H(12.0, k=160, rand_state=43)\ns_long = get_scores_for_H(30.0, k=160, rand_state=44)\n\nevaluate_predictions(s_short, 'Short H=5')\nevaluate_predictions(s_mid, 'Mid H=12')\nevaluate_predictions(s_long, 'Long H=30')\n\n# Standardize scores before blending\ns_short_norm = (s_short - np.mean(s_short, axis=1, keepdims=True)) / np.std(s_short, axis=1, keepdims=True)\ns_mid_norm = (s_mid - np.mean(s_mid, axis=1, keepdims=True)) / np.std(s_mid, axis=1, keepdims=True)\ns_long_norm = (s_long - np.mean(s_long, axis=1, keepdims=True)) / np.std(s_long, axis=1, keepdims=True)\n\nfor w_m in [0.2, 0.4, 0.6, 0.8]:\n    for w_l in [0.0, 0.1, 0.2, 0.3]:\n        blend = s_short_norm + w_m * s_mid_norm + w_l * s_long_norm\n        evaluate_predictions(blend, f'Blend: 1.0*short + {w_m}*mid + {w_l}*long')\n\""
}

Tool response 

<untrusted_context>
Output: <string>:102: RuntimeWarning: invalid value encountered in divide
<string>:103: RuntimeWarning: invalid value encountered in divide
/usr/bin/bash: line 114:  2657 Killed                  python3 -c "
import sys
sys.path.insert(0, '/task')
import gc
import numpy as np
import pandas as pd
import scipy.sparse as sp
from sklearn.decomposition import TruncatedSVD
from ranking_evaluation import CandidateSet, score_rankings
from evaluate_public import validation_truth

targets_frame = pd.read_parquet('/task/data/target_users.parquet')
targets = set(targets_frame.uid.astype(int))
eligibility = CandidateSet('/task/data/validation_eligible_candidates.npz')
graded = validation_truth(eligibility, targets)

target_uids = eligibility.uids
item_ids = eligibility.item_ids
uid_to_idx = {uid: i for i, uid in enumerate(target_uids)}
item_to_idx = {item: i for i, item in enumerate(item_ids)}

T_cut = 24786800
t_90d = T_cut - 90 * 86400

listens = pd.read_parquet(
    '/task/data/train_listens.parquet',
    columns=['uid', 'item_id', 'timestamp', 'played_ratio_pct', 'is_organic'],
    filters=[('timestamp', '>=', t_90d)]
)
listens = listens[listens.uid.isin(targets) & listens.item_id.isin(item_to_idx)]

likes = pd.read_parquet('/task/data/train_likes.parquet', filters=[('timestamp', '>=', t_90d)])
likes = likes[likes.uid.isin(targets) & likes.item_id.isin(item_to_idx)]

pref = pd.read_parquet('/task/data/validation_preference_state.parquet')
pref = pref[pref.uid.isin(targets) & pref.item_id.isin(item_to_idx)]

pref_liked = pref[pref.state == 'liked']
u_pref_l = pref_liked.uid.map(uid_to_idx).to_numpy()
it_pref_l = pref_liked.item_id.map(item_to_idx).to_numpy()
w_pref_l = np.full(len(pref_liked), 3.0, dtype=np.float32)

age_days_l = (T_cut - listens.timestamp.to_numpy()) / 86400.0
age_days_lk = (T_cut - likes.timestamp.to_numpy()) / 86400.0

weights_l_base = np.ones(len(listens), dtype=np.float32)
strong = (listens.played_ratio_pct >= 80).to_numpy()
organic = (listens.is_organic == 1).to_numpy()
weights_l_base[~strong & ~organic] = 1.0
weights_l_base[strong & ~organic] = 1.5
weights_l_base[~strong & organic] = 2.0
weights_l_base[strong & organic] = 3.0

u_l = listens.uid.map(uid_to_idx).to_numpy()
it_l = listens.item_id.map(item_to_idx).to_numpy()
u_lk = likes.uid.map(uid_to_idx).to_numpy()
it_lk = likes.item_id.map(item_to_idx).to_numpy()

def get_scores_for_H(H, k=160, idf_p=0.6, rand_state=42):
    decay_l = np.power(0.5, age_days_l / H).astype(np.float32)
    decay_lk = np.power(0.5, age_days_lk / H).astype(np.float32)
    
    all_u = np.concatenate([u_l, u_lk, u_pref_l])
    all_it = np.concatenate([it_l, it_lk, it_pref_l])
    all_w = np.concatenate([weights_l_base * decay_l, 5.0 * decay_lk, w_pref_l])
    
    R = sp.csr_matrix((all_w, (all_u, all_it)), shape=(len(target_uids), len(item_ids)))
    R.data = np.log1p(R.data)
    
    N_users = len(target_uids)
    doc_freq = np.diff(R.tocsc().indptr)
    idf = (np.log((N_users + 1.0) / (doc_freq + 1.0)) + 1.0).astype(np.float32)
    R_p = R @ sp.diags(np.power(idf, idf_p).astype(np.float32))
    
    svd = TruncatedSVD(n_components=k, algorithm='randomized', random_state=rand_state)
    U = svd.fit_transform(R_p)
    V = svd.components_
    return U @ V

def evaluate_predictions(scores, name=''):
    ranked = {}
    for i, uid in enumerate(target_uids):
        user_scores = scores[i].copy()
        bits = np.unpackbits(eligibility.packed[i], count=30000, bitorder='little')
        user_scores[bits == 0] = -np.inf
        top100_idx = np.argpartition(user_scores, -100)[-100:]
        top100_sorted = top100_idx[np.argsort(-user_scores[top100_idx])]
        ranked[int(uid)] = list(item_ids[top100_sorted])
    res = score_rankings(ranked, graded)
    print(f'{name} -> NDCG@10: {res[\"quality_ndcg@10\"]}')
    return res[\"quality_ndcg@10\"]

s_short = get_scores_for_H(5.0, k=160, rand_state=42)
s_mid = get_scores_for_H(12.0, k=160, rand_state=43)
s_long = get_scores_for_H(30.0, k=160, rand_state=44)

evaluate_predictions(s_short, 'Short H=5')
evaluate_predictions(s_mid, 'Mid H=12')
evaluate_predictions(s_long, 'Long H=30')

# Standardize scores before blending
s_short_norm = (s_short - np.mean(s_short, axis=1, keepdims=True)) / np.std(s_short, axis=1, keepdims=True)
s_mid_norm = (s_mid - np.mean(s_mid, axis=1, keepdims=True)) / np.std(s_mid, axis=1, keepdims=True)
s_long_norm = (s_long - np.mean(s_long, axis=1, keepdims=True)) / np.std(s_long, axis=1, keepdims=True)

for w_m in [0.2, 0.4, 0.6, 0.8]:
    for w_l in [0.0, 0.1, 0.2, 0.3]:
        blend = s_short_norm + w_m * s_mid_norm + w_l * s_long_norm
        evaluate_predictions(blend, f'Blend: 1.0*short + {w_m}*mid + {w_l}*long')
"
Exit Code: 137
Process Group PGID: 2655
</untrusted_context>
50

Test multi-timescale decay kernel with gamma values

+24:18
Recorded reasoning summary 51 words

Exploring Memory Bottlenecks

I'm currently investigating memory usage patterns with large arrays, specifically observing issues with mean calculations on substantial datasets. My focus is on optimizing these operations to prevent exhaustion, especially when dealing with arrays that have a standard deviation of zero for certain users, which is triggering runtime warnings.

Tool callrun_shell_commandTest multi-timescale decay kernel with gamma values Returned

Tool call · Input 

{
  "description": "Test multi-timescale decay kernel with gamma values",
  "command": "python3 -c \"\nimport sys\nsys.path.insert(0, '/task')\nimport gc\nimport numpy as np\nimport pandas as pd\nimport scipy.sparse as sp\nfrom sklearn.decomposition import TruncatedSVD\nfrom ranking_evaluation import CandidateSet, score_rankings\nfrom evaluate_public import validation_truth\n\n# 1. Load targets, catalog, and eligibility\ntargets_frame = pd.read_parquet('/task/data/target_users.parquet')\ntargets = set(targets_frame.uid.astype(int))\neligibility = CandidateSet('/task/data/validation_eligible_candidates.npz')\ngraded = validation_truth(eligibility, targets)\n\ntarget_uids = eligibility.uids\nitem_ids = eligibility.item_ids\nuid_to_idx = {uid: i for i, uid in enumerate(target_uids)}\nitem_to_idx = {item: i for i, item in enumerate(item_ids)}\n\nT_cut = 24786800\nt_90d = T_cut - 90 * 86400\n\nlistens = pd.read_parquet(\n    '/task/data/train_listens.parquet',\n    columns=['uid', 'item_id', 'timestamp', 'played_ratio_pct', 'is_organic'],\n    filters=[('timestamp', '>=', t_90d)]\n)\nlistens = listens[listens.uid.isin(targets) & listens.item_id.isin(item_to_idx)]\n\nlikes = pd.read_parquet('/task/data/train_likes.parquet', filters=[('timestamp', '>=', t_90d)])\nlikes = likes[likes.uid.isin(targets) & likes.item_id.isin(item_to_idx)]\n\npref = pd.read_parquet('/task/data/validation_preference_state.parquet')\npref = pref[pref.uid.isin(targets) & pref.item_id.isin(item_to_idx)]\n\npref_liked = pref[pref.state == 'liked']\nu_pref_l = pref_liked.uid.map(uid_to_idx).to_numpy()\nit_pref_l = pref_liked.item_id.map(item_to_idx).to_numpy()\nw_pref_l = np.full(len(pref_liked), 3.0, dtype=np.float32)\n\nage_days_l = (T_cut - listens.timestamp.to_numpy()) / 86400.0\nage_days_lk = (T_cut - likes.timestamp.to_numpy()) / 86400.0\n\nweights_l_base = np.ones(len(listens), dtype=np.float32)\nstrong = (listens.played_ratio_pct >= 80).to_numpy()\norganic = (listens.is_organic == 1).to_numpy()\nweights_l_base[~strong & ~organic] = 1.0\nweights_l_base[strong & ~organic] = 1.5\nweights_l_base[~strong & organic] = 2.0\nweights_l_base[strong & organic] = 3.0\n\nu_l = listens.uid.map(uid_to_idx).to_numpy()\nit_l = listens.item_id.map(item_to_idx).to_numpy()\ndel listens; gc.collect()\n\nu_lk = likes.uid.map(uid_to_idx).to_numpy()\nit_lk = likes.item_id.map(item_to_idx).to_numpy()\ndel likes; gc.collect()\n\ndef evaluate_predictions(scores, name=''):\n    ranked = {}\n    for i, uid in enumerate(target_uids):\n        user_scores = scores[i].copy()\n        bits = np.unpackbits(eligibility.packed[i], count=30000, bitorder='little')\n        user_scores[bits == 0] = -np.inf\n        top100_idx = np.argpartition(user_scores, -100)[-100:]\n        top100_sorted = top100_idx[np.argsort(-user_scores[top100_idx])]\n        ranked[int(uid)] = list(item_ids[top100_sorted])\n    res = score_rankings(ranked, graded)\n    print(f'{name} -> NDCG@10: {res[\\\"quality_ndcg@10\\\"]}')\n    return res[\\\"quality_ndcg@10\\\"]\n\n# Test multi-timescale decay kernel: 2^(-age/7) + gamma * 2^(-age/30)\nfor gamma in [0.0, 0.05, 0.1, 0.15, 0.2, 0.3]:\n    kernel_l = (np.power(0.5, age_days_l / 7.0) + gamma * np.power(0.5, age_days_l / 30.0)).astype(np.float32)\n    kernel_lk = (np.power(0.5, age_days_lk / 7.0) + gamma * np.power(0.5, age_days_lk / 30.0)).astype(np.float32)\n    \n    all_u = np.concatenate([u_l, u_lk, u_pref_l])\n    all_it = np.concatenate([it_l, it_lk, it_pref_l])\n    all_w = np.concatenate([weights_l_base * kernel_l, 5.0 * kernel_lk, w_pref_l])\n    \n    R = sp.csr_matrix((all_w, (all_u, all_it)), shape=(len(target_uids), len(item_ids)))\n    R.data = np.log1p(R.data)\n    \n    N_users = len(target_uids)\n    doc_freq = np.diff(R.tocsc().indptr)\n    idf = (np.log((N_users + 1.0) / (doc_freq + 1.0)) + 1.0).astype(np.float32)\n    R_p = R @ sp.diags(np.power(idf, 0.6).astype(np.float32))\n    \n    svd = TruncatedSVD(n_components=160, algorithm='randomized', random_state=42)\n    U = svd.fit_transform(R_p)\n    V = svd.components_\n    scores = U @ V\n    evaluate_predictions(scores, f'gamma={gamma}')\n\""
}

Tool response 

<untrusted_context>
Output: gamma=0.0 -> NDCG@10: 0.041682
gamma=0.05 -> NDCG@10: 0.041469
gamma=0.1 -> NDCG@10: 0.040599
gamma=0.15 -> NDCG@10: 0.040384
gamma=0.2 -> NDCG@10: 0.039918
gamma=0.3 -> NDCG@10: 0.039588
Process Group PGID: 2683
</untrusted_context>
51

Evaluate minimum played ratio threshold

+25:21
Recorded reasoning summary 36 words

Confirming Gamma Value

I've confirmed that setting Gamma to 0.0, which prioritizes recent decay patterns, yields the optimal performance. This suggests users are most effectively exploring new music based on their immediate listening phase or mood.

Recorded reasoning summary 44 words

Adjusting Skip Weighting

I'm re-evaluating our event weights, specifically how short, incomplete listens (skips) are handled. It appears tracks played for less than 30% are currently assigned a weight of 1.0, which seems too high given their nature as skips. I'm investigating this further.

Tool callrun_shell_commandEvaluate minimum played ratio threshold Returned

Tool call · Input 

{
  "command": "python3 -c \"\nimport sys\nsys.path.insert(0, '/task')\nimport gc\nimport numpy as np\nimport pandas as pd\nimport scipy.sparse as sp\nfrom sklearn.decomposition import TruncatedSVD\nfrom ranking_evaluation import CandidateSet, score_rankings\nfrom evaluate_public import validation_truth\n\n# 1. Load targets, catalog, and eligibility\ntargets_frame = pd.read_parquet('/task/data/target_users.parquet')\ntargets = set(targets_frame.uid.astype(int))\neligibility = CandidateSet('/task/data/validation_eligible_candidates.npz')\ngraded = validation_truth(eligibility, targets)\n\ntarget_uids = eligibility.uids\nitem_ids = eligibility.item_ids\nuid_to_idx = {uid: i for i, uid in enumerate(target_uids)}\nitem_to_idx = {item: i for i, item in enumerate(item_ids)}\n\nT_cut = 24786800\nt_90d = T_cut - 90 * 86400\n\nlistens = pd.read_parquet(\n    '/task/data/train_listens.parquet',\n    columns=['uid', 'item_id', 'timestamp', 'played_ratio_pct', 'is_organic'],\n    filters=[('timestamp', '>=', t_90d)]\n)\nlistens = listens[listens.uid.isin(targets) & listens.item_id.isin(item_to_idx)]\n\nlikes = pd.read_parquet('/task/data/train_likes.parquet', filters=[('timestamp', '>=', t_90d)])\nlikes = likes[likes.uid.isin(targets) & likes.item_id.isin(item_to_idx)]\n\npref = pd.read_parquet('/task/data/validation_preference_state.parquet')\npref = pref[pref.uid.isin(targets) & pref.item_id.isin(item_to_idx)]\n\npref_liked = pref[pref.state == 'liked']\nu_pref_l = pref_liked.uid.map(uid_to_idx).to_numpy()\nit_pref_l = pref_liked.item_id.map(item_to_idx).to_numpy()\nw_pref_l = np.full(len(pref_liked), 3.0, dtype=np.float32)\n\nage_days_l = (T_cut - listens.timestamp.to_numpy()) / 86400.0\nage_days_lk = (T_cut - likes.timestamp.to_numpy()) / 86400.0\ndecay_l = np.power(0.5, age_days_l / 7.0).astype(np.float32)\ndecay_lk = np.power(0.5, age_days_lk / 7.0).astype(np.float32)\n\nu_l = listens.uid.map(uid_to_idx).to_numpy()\nit_l = listens.item_id.map(item_to_idx).to_numpy()\nu_lk = likes.uid.map(uid_to_idx).to_numpy()\nit_lk = likes.item_id.map(item_to_idx).to_numpy()\n\ndef evaluate_predictions(scores, name=''):\n    ranked = {}\n    for i, uid in enumerate(target_uids):\n        user_scores = scores[i].copy()\n        bits = np.unpackbits(eligibility.packed[i], count=30000, bitorder='little')\n        user_scores[bits == 0] = -np.inf\n        top100_idx = np.argpartition(user_scores, -100)[-100:]\n        top100_sorted = top100_idx[np.argsort(-user_scores[top100_idx])]\n        ranked[int(uid)] = list(item_ids[top100_sorted])\n    res = score_rankings(ranked, graded)\n    print(f'{name} -> NDCG@10: {res[\\\"quality_ndcg@10\\\"]}')\n    return res[\\\"quality_ndcg@10\\\"]\n\n# Test different completion thresholds:\n# What if minimum played ratio is 0%, 30%, 50%, 60%, 80%?\nfor min_pct in [0, 20, 30, 40, 50]:\n    mask = listens.played_ratio_pct >= min_pct\n    cur_u_l = u_l[mask]\n    cur_it_l = it_l[mask]\n    cur_decay = decay_l[mask]\n    \n    strong = (listens.played_ratio_pct[mask] >= 80).to_numpy()\n    organic = (listens.is_organic[mask] == 1).to_numpy()\n    w_base = np.ones(len(cur_u_l), dtype=np.float32)\n    w_base[~strong & ~organic] = 1.0\n    w_base[strong & ~organic] = 1.5\n    w_base[~strong & organic] = 2.0\n    w_base[strong & organic] = 3.0\n    \n    all_u = np.concatenate([cur_u_l, u_lk, u_pref_l])\n    all_it = np.concatenate([cur_it_l, it_lk, it_pref_l])\n    all_w = np.concatenate([w_base * cur_decay, 5.0 * decay_lk, w_pref_l])\n    \n    R = sp.csr_matrix((all_w, (all_u, all_it)), shape=(len(target_uids), len(item_ids)))\n    R.data = np.log1p(R.data)\n    \n    N_users = len(target_uids)\n    doc_freq = np.diff(R.tocsc().indptr)\n    idf = (np.log((N_users + 1.0) / (doc_freq + 1.0)) + 1.0).astype(np.float32)\n    R_p = R @ sp.diags(np.power(idf, 0.6).astype(np.float32))\n    \n    svd = TruncatedSVD(n_components=160, algorithm='randomized', random_state=42)\n    U = svd.fit_transform(R_p)\n    V = svd.components_\n    evaluate_predictions(U @ V, f'min_played_ratio >= {min_pct}%')\n\"",
  "description": "Evaluate minimum played ratio threshold"
}

Tool response 

<untrusted_context>
Output: min_played_ratio >= 0% -> NDCG@10: 0.041682
min_played_ratio >= 20% -> NDCG@10: 0.042088
min_played_ratio >= 30% -> NDCG@10: 0.042272
min_played_ratio >= 40% -> NDCG@10: 0.04133
min_played_ratio >= 50% -> NDCG@10: 0.041569
Process Group PGID: 2717
</untrusted_context>
52

Scan k around 150 for min_played_ratio >= 30%

+26:04
Recorded reasoning summary 44 words

Analyzing Performance Boosts

My analysis shows a slight but positive increase in the min_played_ratio >= 30% metric, suggesting that refining playback thresholds is indeed reducing noise and improving NDCG. I'm now exploring the impact of n_components around 160 in conjunction with this improved filtering.

Tool callrun_shell_commandScan k around 150 for min_played_ratio >= 30% Returned

Tool call · Input 

{
  "description": "Scan k around 150 for min_played_ratio >= 30%",
  "command": "python3 -c \"\nimport sys\nsys.path.insert(0, '/task')\nimport gc\nimport numpy as np\nimport pandas as pd\nimport scipy.sparse as sp\nfrom sklearn.decomposition import TruncatedSVD\nfrom ranking_evaluation import CandidateSet, score_rankings\nfrom evaluate_public import validation_truth\n\ntargets_frame = pd.read_parquet('/task/data/target_users.parquet')\ntargets = set(targets_frame.uid.astype(int))\neligibility = CandidateSet('/task/data/validation_eligible_candidates.npz')\ngraded = validation_truth(eligibility, targets)\n\ntarget_uids = eligibility.uids\nitem_ids = eligibility.item_ids\nuid_to_idx = {uid: i for i, uid in enumerate(target_uids)}\nitem_to_idx = {item: i for i, item in enumerate(item_ids)}\n\nT_cut = 24786800\nt_90d = T_cut - 90 * 86400\n\nlistens = pd.read_parquet(\n    '/task/data/train_listens.parquet',\n    columns=['uid', 'item_id', 'timestamp', 'played_ratio_pct', 'is_organic'],\n    filters=[('timestamp', '>=', t_90d), ('played_ratio_pct', '>=', 30)]\n)\nlistens = listens[listens.uid.isin(targets) & listens.item_id.isin(item_to_idx)]\n\nlikes = pd.read_parquet('/task/data/train_likes.parquet', filters=[('timestamp', '>=', t_90d)])\nlikes = likes[likes.uid.isin(targets) & likes.item_id.isin(item_to_idx)]\n\npref = pd.read_parquet('/task/data/validation_preference_state.parquet')\npref = pref[pref.uid.isin(targets) & pref.item_id.isin(item_to_idx)]\n\npref_liked = pref[pref.state == 'liked']\nu_pref_l = pref_liked.uid.map(uid_to_idx).to_numpy()\nit_pref_l = pref_liked.item_id.map(item_to_idx).to_numpy()\nw_pref_l = np.full(len(pref_liked), 3.0, dtype=np.float32)\n\nage_days_l = (T_cut - listens.timestamp.to_numpy()) / 86400.0\nage_days_lk = (T_cut - likes.timestamp.to_numpy()) / 86400.0\ndecay_l = np.power(0.5, age_days_l / 7.0).astype(np.float32)\ndecay_lk = np.power(0.5, age_days_lk / 7.0).astype(np.float32)\n\nstrong = (listens.played_ratio_pct >= 80).to_numpy()\norganic = (listens.is_organic == 1).to_numpy()\nw_base = np.ones(len(listens), dtype=np.float32)\nw_base[~strong & ~organic] = 1.0\nw_base[strong & ~organic] = 1.5\nw_base[~strong & organic] = 2.0\nw_base[strong & organic] = 3.0\n\nu_l = listens.uid.map(uid_to_idx).to_numpy()\nit_l = listens.item_id.map(item_to_idx).to_numpy()\nu_lk = likes.uid.map(uid_to_idx).to_numpy()\nit_lk = likes.item_id.map(item_to_idx).to_numpy()\n\nall_u = np.concatenate([u_l, u_lk, u_pref_l])\nall_it = np.concatenate([it_l, it_lk, it_pref_l])\nall_w = np.concatenate([w_base * decay_l, 5.0 * decay_lk, w_pref_l])\n\nR = sp.csr_matrix((all_w, (all_u, all_it)), shape=(len(target_uids), len(item_ids)))\nR.data = np.log1p(R.data)\n\nN_users = len(target_uids)\ndoc_freq = np.diff(R.tocsc().indptr)\nidf = (np.log((N_users + 1.0) / (doc_freq + 1.0)) + 1.0).astype(np.float32)\nR_p = R @ sp.diags(np.power(idf, 0.6).astype(np.float32))\n\ndef evaluate_predictions(scores, name=''):\n    ranked = {}\n    for i, uid in enumerate(target_uids):\n        user_scores = scores[i].copy()\n        bits = np.unpackbits(eligibility.packed[i], count=30000, bitorder='little')\n        user_scores[bits == 0] = -np.inf\n        top100_idx = np.argpartition(user_scores, -100)[-100:]\n        top100_sorted = top100_idx[np.argsort(-user_scores[top100_idx])]\n        ranked[int(uid)] = list(item_ids[top100_sorted])\n    res = score_rankings(ranked, graded)\n    print(f'{name} -> NDCG@10: {res[\\\"quality_ndcg@10\\\"]}')\n    return res[\\\"quality_ndcg@10\\\"]\n\nfor k in [100, 120, 140, 150, 160, 170, 180, 200]:\n    svd = TruncatedSVD(n_components=k, algorithm='randomized', random_state=42)\n    U = svd.fit_transform(R_p)\n    V = svd.components_\n    evaluate_predictions(U @ V, f'k={k}')\n\""
}

Tool response 

<untrusted_context>
Output: k=100 -> NDCG@10: 0.041147
k=120 -> NDCG@10: 0.041725
k=140 -> NDCG@10: 0.041758
k=150 -> NDCG@10: 0.04131
k=160 -> NDCG@10: 0.042272
k=170 -> NDCG@10: 0.040027
k=180 -> NDCG@10: 0.040822
k=200 -> NDCG@10: 0.04042
Process Group PGID: 2751
</untrusted_context>
53

Test n_iter power iterations in TruncatedSVD

+26:54
Recorded reasoning summary 45 words

Exploring Randomized SVD

I'm currently investigating the impact of the random seed on Randomized SVD, particularly its relationship with the n_iter parameter. My recent tests with k=160 yielded a promising result of 0.042272, and I'm now keen to explore variations in n_iter to optimize performance.

Tool callrun_shell_commandTest n_iter power iterations in TruncatedSVD Returned

Tool call · Input 

{
  "command": "python3 -c \"\nimport sys\nsys.path.insert(0, '/task')\nimport gc\nimport numpy as np\nimport pandas as pd\nimport scipy.sparse as sp\nfrom sklearn.decomposition import TruncatedSVD\nfrom ranking_evaluation import CandidateSet, score_rankings\nfrom evaluate_public import validation_truth\n\ntargets_frame = pd.read_parquet('/task/data/target_users.parquet')\ntargets = set(targets_frame.uid.astype(int))\neligibility = CandidateSet('/task/data/validation_eligible_candidates.npz')\ngraded = validation_truth(eligibility, targets)\n\ntarget_uids = eligibility.uids\nitem_ids = eligibility.item_ids\nuid_to_idx = {uid: i for i, uid in enumerate(target_uids)}\nitem_to_idx = {item: i for i, item in enumerate(item_ids)}\n\nT_cut = 24786800\nt_90d = T_cut - 90 * 86400\n\nlistens = pd.read_parquet(\n    '/task/data/train_listens.parquet',\n    columns=['uid', 'item_id', 'timestamp', 'played_ratio_pct', 'is_organic'],\n    filters=[('timestamp', '>=', t_90d), ('played_ratio_pct', '>=', 30)]\n)\nlistens = listens[listens.uid.isin(targets) & listens.item_id.isin(item_to_idx)]\n\nlikes = pd.read_parquet('/task/data/train_likes.parquet', filters=[('timestamp', '>=', t_90d)])\nlikes = likes[likes.uid.isin(targets) & likes.item_id.isin(item_to_idx)]\n\npref = pd.read_parquet('/task/data/validation_preference_state.parquet')\npref = pref[pref.uid.isin(targets) & pref.item_id.isin(item_to_idx)]\n\npref_liked = pref[pref.state == 'liked']\nu_pref_l = pref_liked.uid.map(uid_to_idx).to_numpy()\nit_pref_l = pref_liked.item_id.map(item_to_idx).to_numpy()\nw_pref_l = np.full(len(pref_liked), 3.0, dtype=np.float32)\n\nage_days_l = (T_cut - listens.timestamp.to_numpy()) / 86400.0\nage_days_lk = (T_cut - likes.timestamp.to_numpy()) / 86400.0\ndecay_l = np.power(0.5, age_days_l / 7.0).astype(np.float32)\ndecay_lk = np.power(0.5, age_days_lk / 7.0).astype(np.float32)\n\nstrong = (listens.played_ratio_pct >= 80).to_numpy()\norganic = (listens.is_organic == 1).to_numpy()\nw_base = np.ones(len(listens), dtype=np.float32)\nw_base[~strong & ~organic] = 1.0\nw_base[strong & ~organic] = 1.5\nw_base[~strong & organic] = 2.0\nw_base[strong & organic] = 3.0\n\nu_l = listens.uid.map(uid_to_idx).to_numpy()\nit_l = listens.item_id.map(item_to_idx).to_numpy()\nu_lk = likes.uid.map(uid_to_idx).to_numpy()\nit_lk = likes.item_id.map(item_to_idx).to_numpy()\n\nall_u = np.concatenate([u_l, u_lk, u_pref_l])\nall_it = np.concatenate([it_l, it_lk, it_pref_l])\nall_w = np.concatenate([w_base * decay_l, 5.0 * decay_lk, w_pref_l])\n\nR = sp.csr_matrix((all_w, (all_u, all_it)), shape=(len(target_uids), len(item_ids)))\nR.data = np.log1p(R.data)\n\nN_users = len(target_uids)\ndoc_freq = np.diff(R.tocsc().indptr)\nidf = (np.log((N_users + 1.0) / (doc_freq + 1.0)) + 1.0).astype(np.float32)\nR_p = R @ sp.diags(np.power(idf, 0.6).astype(np.float32))\n\ndef evaluate_predictions(scores, name=''):\n    ranked = {}\n    for i, uid in enumerate(target_uids):\n        user_scores = scores[i].copy()\n        bits = np.unpackbits(eligibility.packed[i], count=30000, bitorder='little')\n        user_scores[bits == 0] = -np.inf\n        top100_idx = np.argpartition(user_scores, -100)[-100:]\n        top100_sorted = top100_idx[np.argsort(-user_scores[top100_idx])]\n        ranked[int(uid)] = list(item_ids[top100_sorted])\n    res = score_rankings(ranked, graded)\n    print(f'{name} -> NDCG@10: {res[\\\"quality_ndcg@10\\\"]}')\n    return res[\\\"quality_ndcg@10\\\"]\n\nfor n_iter in [3, 5, 7, 10]:\n    svd = TruncatedSVD(n_components=160, algorithm='randomized', n_iter=n_iter, random_state=42)\n    U = svd.fit_transform(R_p)\n    V = svd.components_\n    evaluate_predictions(U @ V, f'n_iter={n_iter}')\n\"",
  "description": "Test n_iter power iterations in TruncatedSVD"
}

Tool response 

<untrusted_context>
Output: n_iter=3 -> NDCG@10: 0.042084
n_iter=5 -> NDCG@10: 0.042272
n_iter=7 -> NDCG@10: 0.041766
n_iter=10 -> NDCG@10: 0.041552
Process Group PGID: 2785
</untrusted_context>
54

Test multi-seed/multi-k ensemble average

+27:29
Recorded reasoning summary 38 words

Testing ensemble gains

I'm exploring ensemble methods to boost performance. My current focus is on averaging predictions from different random seeds and varying the k parameter. This should reveal if combining these slight variations yields a higher NDCG@10.

Tool callrun_shell_commandTest multi-seed/multi-k ensemble average Returned

Tool call · Input 

{
  "command": "python3 -c \"\nimport sys\nsys.path.insert(0, '/task')\nimport gc\nimport numpy as np\nimport pandas as pd\nimport scipy.sparse as sp\nfrom sklearn.decomposition import TruncatedSVD\nfrom ranking_evaluation import CandidateSet, score_rankings\nfrom evaluate_public import validation_truth\n\ntargets_frame = pd.read_parquet('/task/data/target_users.parquet')\ntargets = set(targets_frame.uid.astype(int))\neligibility = CandidateSet('/task/data/validation_eligible_candidates.npz')\ngraded = validation_truth(eligibility, targets)\n\ntarget_uids = eligibility.uids\nitem_ids = eligibility.item_ids\nuid_to_idx = {uid: i for i, uid in enumerate(target_uids)}\nitem_to_idx = {item: i for i, item in enumerate(item_ids)}\n\nT_cut = 24786800\nt_90d = T_cut - 90 * 86400\n\nlistens = pd.read_parquet(\n    '/task/data/train_listens.parquet',\n    columns=['uid', 'item_id', 'timestamp', 'played_ratio_pct', 'is_organic'],\n    filters=[('timestamp', '>=', t_90d), ('played_ratio_pct', '>=', 30)]\n)\nlistens = listens[listens.uid.isin(targets) & listens.item_id.isin(item_to_idx)]\n\nlikes = pd.read_parquet('/task/data/train_likes.parquet', filters=[('timestamp', '>=', t_90d)])\nlikes = likes[likes.uid.isin(targets) & likes.item_id.isin(item_to_idx)]\n\npref = pd.read_parquet('/task/data/validation_preference_state.parquet')\npref = pref[pref.uid.isin(targets) & pref.item_id.isin(item_to_idx)]\n\npref_liked = pref[pref.state == 'liked']\nu_pref_l = pref_liked.uid.map(uid_to_idx).to_numpy()\nit_pref_l = pref_liked.item_id.map(item_to_idx).to_numpy()\nw_pref_l = np.full(len(pref_liked), 3.0, dtype=np.float32)\n\nage_days_l = (T_cut - listens.timestamp.to_numpy()) / 86400.0\nage_days_lk = (T_cut - likes.timestamp.to_numpy()) / 86400.0\ndecay_l = np.power(0.5, age_days_l / 7.0).astype(np.float32)\ndecay_lk = np.power(0.5, age_days_lk / 7.0).astype(np.float32)\n\nstrong = (listens.played_ratio_pct >= 80).to_numpy()\norganic = (listens.is_organic == 1).to_numpy()\nw_base = np.ones(len(listens), dtype=np.float32)\nw_base[~strong & ~organic] = 1.0\nw_base[strong & ~organic] = 1.5\nw_base[~strong & organic] = 2.0\nw_base[strong & organic] = 3.0\n\nu_l = listens.uid.map(uid_to_idx).to_numpy()\nit_l = listens.item_id.map(item_to_idx).to_numpy()\nu_lk = likes.uid.map(uid_to_idx).to_numpy()\nit_lk = likes.item_id.map(item_to_idx).to_numpy()\n\nall_u = np.concatenate([u_l, u_lk, u_pref_l])\nall_it = np.concatenate([it_l, it_lk, it_pref_l])\nall_w = np.concatenate([w_base * decay_l, 5.0 * decay_lk, w_pref_l])\n\nR = sp.csr_matrix((all_w, (all_u, all_it)), shape=(len(target_uids), len(item_ids)))\nR.data = np.log1p(R.data)\n\nN_users = len(target_uids)\ndoc_freq = np.diff(R.tocsc().indptr)\nidf = (np.log((N_users + 1.0) / (doc_freq + 1.0)) + 1.0).astype(np.float32)\nR_p = R @ sp.diags(np.power(idf, 0.6).astype(np.float32))\n\ndef evaluate_predictions(scores, name=''):\n    ranked = {}\n    for i, uid in enumerate(target_uids):\n        user_scores = scores[i].copy()\n        bits = np.unpackbits(eligibility.packed[i], count=30000, bitorder='little')\n        user_scores[bits == 0] = -np.inf\n        top100_idx = np.argpartition(user_scores, -100)[-100:]\n        top100_sorted = top100_idx[np.argsort(-user_scores[top100_idx])]\n        ranked[int(uid)] = list(item_ids[top100_sorted])\n    res = score_rankings(ranked, graded)\n    print(f'{name} -> NDCG@10: {res[\\\"quality_ndcg@10\\\"]}')\n    return res[\\\"quality_ndcg@10\\\"]\n\n# Ensemble 3 SVDs with different seeds / k\nsum_scores = np.zeros((len(target_uids), len(item_ids)), dtype=np.float32)\nconfigs = [(160, 42), (160, 101), (140, 202), (160, 303)]\nfor k, seed in configs:\n    svd = TruncatedSVD(n_components=k, algorithm='randomized', random_state=seed)\n    U = svd.fit_transform(R_p)\n    V = svd.components_\n    score_k = U @ V\n    evaluate_predictions(score_k, f'Single model k={k}, seed={seed}')\n    sum_scores += score_k\n\nevaluate_predictions(sum_scores, 'Ensemble average of 4 SVD models')\n\"",
  "description": "Test multi-seed/multi-k ensemble average"
}

Tool response 

<untrusted_context>
Output: Single model k=160, seed=42 -> NDCG@10: 0.042272
Single model k=160, seed=101 -> NDCG@10: 0.041304
Single model k=140, seed=202 -> NDCG@10: 0.04302
Single model k=160, seed=303 -> NDCG@10: 0.042018
Ensemble average of 4 SVD models -> NDCG@10: 0.042823
Process Group PGID: 2813
</untrusted_context>
55

Evaluate 3-seed ensemble for k around 140

+28:19
Tool callrun_shell_commandEvaluate 3-seed ensemble for k around 140 Returned

Tool call · Input 

{
  "description": "Evaluate 3-seed ensemble for k around 140",
  "command": "python3 -c \"\nimport sys\nsys.path.insert(0, '/task')\nimport numpy as np\nimport pandas as pd\nimport scipy.sparse as sp\nfrom sklearn.decomposition import TruncatedSVD\nfrom ranking_evaluation import CandidateSet, score_rankings\nfrom evaluate_public import validation_truth\n\ntargets_frame = pd.read_parquet('/task/data/target_users.parquet')\ntargets = set(targets_frame.uid.astype(int))\neligibility = CandidateSet('/task/data/validation_eligible_candidates.npz')\ngraded = validation_truth(eligibility, targets)\n\ntarget_uids = eligibility.uids\nitem_ids = eligibility.item_ids\nuid_to_idx = {uid: i for i, uid in enumerate(target_uids)}\nitem_to_idx = {item: i for i, item in enumerate(item_ids)}\n\nT_cut = 24786800\nt_90d = T_cut - 90 * 86400\n\nlistens = pd.read_parquet(\n    '/task/data/train_listens.parquet',\n    columns=['uid', 'item_id', 'timestamp', 'played_ratio_pct', 'is_organic'],\n    filters=[('timestamp', '>=', t_90d), ('played_ratio_pct', '>=', 30)]\n)\nlistens = listens[listens.uid.isin(targets) & listens.item_id.isin(item_to_idx)]\n\nlikes = pd.read_parquet('/task/data/train_likes.parquet', filters=[('timestamp', '>=', t_90d)])\nlikes = likes[likes.uid.isin(targets) & likes.item_id.isin(item_to_idx)]\n\npref = pd.read_parquet('/task/data/validation_preference_state.parquet')\npref = pref[pref.uid.isin(targets) & pref.item_id.isin(item_to_idx)]\n\npref_liked = pref[pref.state == 'liked']\nu_pref_l = pref_liked.uid.map(uid_to_idx).to_numpy()\nit_pref_l = pref_liked.item_id.map(item_to_idx).to_numpy()\nw_pref_l = np.full(len(pref_liked), 3.0, dtype=np.float32)\n\nage_days_l = (T_cut - listens.timestamp.to_numpy()) / 86400.0\nage_days_lk = (T_cut - likes.timestamp.to_numpy()) / 86400.0\ndecay_l = np.power(0.5, age_days_l / 7.0).astype(np.float32)\ndecay_lk = np.power(0.5, age_days_lk / 7.0).astype(np.float32)\n\nstrong = (listens.played_ratio_pct >= 80).to_numpy()\norganic = (listens.is_organic == 1).to_numpy()\nw_base = np.ones(len(listens), dtype=np.float32)\nw_base[~strong & ~organic] = 1.0\nw_base[strong & ~organic] = 1.5\nw_base[~strong & organic] = 2.0\nw_base[strong & organic] = 3.0\n\nu_l = listens.uid.map(uid_to_idx).to_numpy()\nit_l = listens.item_id.map(item_to_idx).to_numpy()\nu_lk = likes.uid.map(uid_to_idx).to_numpy()\nit_lk = likes.item_id.map(item_to_idx).to_numpy()\n\nall_u = np.concatenate([u_l, u_lk, u_pref_l])\nall_it = np.concatenate([it_l, it_lk, it_pref_l])\nall_w = np.concatenate([w_base * decay_l, 5.0 * decay_lk, w_pref_l])\n\nR = sp.csr_matrix((all_w, (all_u, all_it)), shape=(len(target_uids), len(item_ids)))\nR.data = np.log1p(R.data)\n\nN_users = len(target_uids)\ndoc_freq = np.diff(R.tocsc().indptr)\nidf = (np.log((N_users + 1.0) / (doc_freq + 1.0)) + 1.0).astype(np.float32)\nR_p = R @ sp.diags(np.power(idf, 0.6).astype(np.float32))\n\ndef evaluate_predictions(scores, name=''):\n    ranked = {}\n    for i, uid in enumerate(target_uids):\n        user_scores = scores[i].copy()\n        bits = np.unpackbits(eligibility.packed[i], count=30000, bitorder='little')\n        user_scores[bits == 0] = -np.inf\n        top100_idx = np.argpartition(user_scores, -100)[-100:]\n        top100_sorted = top100_idx[np.argsort(-user_scores[top100_idx])]\n        ranked[int(uid)] = list(item_ids[top100_sorted])\n    res = score_rankings(ranked, graded)\n    print(f'{name} -> NDCG@10: {res[\\\"quality_ndcg@10\\\"]}')\n    return res[\\\"quality_ndcg@10\\\"]\n\nfor k in [130, 135, 140, 145]:\n    scores_k = np.zeros((len(target_uids), len(item_ids)), dtype=np.float32)\n    for seed in [42, 101, 202]:\n        svd = TruncatedSVD(n_components=k, algorithm='randomized', random_state=seed)\n        U = svd.fit_transform(R_p)\n        V = svd.components_\n        scores_k += (U @ V) / 3.0\n    evaluate_predictions(scores_k, f'Ensemble of 3 seeds for k={k}')\n\""
}

Tool response 

<untrusted_context>
Output: Ensemble of 3 seeds for k=130 -> NDCG@10: 0.041599
Ensemble of 3 seeds for k=135 -> NDCG@10: 0.041803
Ensemble of 3 seeds for k=140 -> NDCG@10: 0.043033
Ensemble of 3 seeds for k=145 -> NDCG@10: 0.042618
Process Group PGID: 2853
</untrusted_context>
56

Evaluate multi-rank 5-component ensemble

+29:28
Recorded reasoning summary 56 words

Achieving Enhanced NDCG

I've successfully developed an ensemble of three seeds for k=140, achieving an NDCG@10 score of 0.043033. This represents a significant improvement, more than quadrupling the baseline of 0.01071, and this impressive gain was realized in a mere 10 seconds of computation. My next step is to investigate variations in k, specifically exploring k=14.

Tool callrun_shell_commandEvaluate multi-rank 5-component ensemble Returned

Tool call · Input 

{
  "command": "python3 -c \"\nimport sys\nsys.path.insert(0, '/task')\nimport numpy as np\nimport pandas as pd\nimport scipy.sparse as sp\nfrom sklearn.decomposition import TruncatedSVD\nfrom ranking_evaluation import CandidateSet, score_rankings\nfrom evaluate_public import validation_truth\n\ntargets_frame = pd.read_parquet('/task/data/target_users.parquet')\ntargets = set(targets_frame.uid.astype(int))\neligibility = CandidateSet('/task/data/validation_eligible_candidates.npz')\ngraded = validation_truth(eligibility, targets)\n\ntarget_uids = eligibility.uids\nitem_ids = eligibility.item_ids\nuid_to_idx = {uid: i for i, uid in enumerate(target_uids)}\nitem_to_idx = {item: i for i, item in enumerate(item_ids)}\n\nT_cut = 24786800\nt_90d = T_cut - 90 * 86400\n\nlistens = pd.read_parquet(\n    '/task/data/train_listens.parquet',\n    columns=['uid', 'item_id', 'timestamp', 'played_ratio_pct', 'is_organic'],\n    filters=[('timestamp', '>=', t_90d), ('played_ratio_pct', '>=', 30)]\n)\nlistens = listens[listens.uid.isin(targets) & listens.item_id.isin(item_to_idx)]\n\nlikes = pd.read_parquet('/task/data/train_likes.parquet', filters=[('timestamp', '>=', t_90d)])\nlikes = likes[likes.uid.isin(targets) & likes.item_id.isin(item_to_idx)]\n\npref = pd.read_parquet('/task/data/validation_preference_state.parquet')\npref = pref[pref.uid.isin(targets) & pref.item_id.isin(item_to_idx)]\n\npref_liked = pref[pref.state == 'liked']\nu_pref_l = pref_liked.uid.map(uid_to_idx).to_numpy()\nit_pref_l = pref_liked.item_id.map(item_to_idx).to_numpy()\nw_pref_l = np.full(len(pref_liked), 3.0, dtype=np.float32)\n\nage_days_l = (T_cut - listens.timestamp.to_numpy()) / 86400.0\nage_days_lk = (T_cut - likes.timestamp.to_numpy()) / 86400.0\ndecay_l = np.power(0.5, age_days_l / 7.0).astype(np.float32)\ndecay_lk = np.power(0.5, age_days_lk / 7.0).astype(np.float32)\n\nstrong = (listens.played_ratio_pct >= 80).to_numpy()\norganic = (listens.is_organic == 1).to_numpy()\nw_base = np.ones(len(listens), dtype=np.float32)\nw_base[~strong & ~organic] = 1.0\nw_base[strong & ~organic] = 1.5\nw_base[~strong & organic] = 2.0\nw_base[strong & organic] = 3.0\n\nu_l = listens.uid.map(uid_to_idx).to_numpy()\nit_l = listens.item_id.map(item_to_idx).to_numpy()\nu_lk = likes.uid.map(uid_to_idx).to_numpy()\nit_lk = likes.item_id.map(item_to_idx).to_numpy()\n\nall_u = np.concatenate([u_l, u_lk, u_pref_l])\nall_it = np.concatenate([it_l, it_lk, it_pref_l])\nall_w = np.concatenate([w_base * decay_l, 5.0 * decay_lk, w_pref_l])\n\nR = sp.csr_matrix((all_w, (all_u, all_it)), shape=(len(target_uids), len(item_ids)))\nR.data = np.log1p(R.data)\n\nN_users = len(target_uids)\ndoc_freq = np.diff(R.tocsc().indptr)\nidf = (np.log((N_users + 1.0) / (doc_freq + 1.0)) + 1.0).astype(np.float32)\nR_p = R @ sp.diags(np.power(idf, 0.6).astype(np.float32))\n\ndef evaluate_predictions(scores, name=''):\n    ranked = {}\n    for i, uid in enumerate(target_uids):\n        user_scores = scores[i].copy()\n        bits = np.unpackbits(eligibility.packed[i], count=30000, bitorder='little')\n        user_scores[bits == 0] = -np.inf\n        top100_idx = np.argpartition(user_scores, -100)[-100:]\n        top100_sorted = top100_idx[np.argsort(-user_scores[top100_idx])]\n        ranked[int(uid)] = list(item_ids[top100_sorted])\n    res = score_rankings(ranked, graded)\n    print(f'{name} -> NDCG@10: {res[\\\"quality_ndcg@10\\\"]}')\n    return res[\\\"quality_ndcg@10\\\"]\n\n# Multi-rank ensemble: k in [120, 140, 160] with different seeds\nscores_multi = np.zeros((len(target_uids), len(item_ids)), dtype=np.float32)\nfor k, seed in [(120, 42), (140, 101), (140, 202), (160, 303), (140, 404)]:\n    svd = TruncatedSVD(n_components=k, algorithm='randomized', random_state=seed)\n    U = svd.fit_transform(R_p)\n    V = svd.components_\n    scores_multi += (U @ V) / 5.0\n\nevaluate_predictions(scores_multi, 'Multi-rank ensemble (5 components)')\n\"",
  "description": "Evaluate multi-rank 5-component ensemble"
}

Tool response 

<untrusted_context>
Output: Multi-rank ensemble (5 components) -> NDCG@10: 0.042633
Process Group PGID: 2887
</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-basic-fixed-r01-d0433654
Recorded model
gemini-3.8-flash
Harness version
0.61.0
Session
60e34f21-2eba-4fe1-822f-83a1c914ba13
Source SHA-256
b12751d5b7f016d5450f1ad25a53648f1b21b412448b495b5b626d2cc4ec2992

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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