Basic recommender gemini-3-8-flash-high-10
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<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
Showing up to 200 items (files + folders).
/app/ └───solution/
</session_context>
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.
All inputs are under /task/data/. Training data ends at timestamp 24786800. A 30-minute gap precedes a seven-day public validation window. The hidden test is the following seven-day window after another 30-minute gap.
For each event family there is a train_*.parquet and val_*.parquet file:
| Family | Meaning | Columns |
|---|---|---|
listens | Playback events | uid, timestamp, item_id, is_organic, played_ratio_pct, track_length_seconds |
likes | Explicit positive actions | uid, timestamp, item_id, is_organic |
dislikes | Explicit negative actions | uid, timestamp, item_id, is_organic |
unlikes | Reversal of a prior like | uid, timestamp, item_id, is_organic |
undislikes | Reversal of a prior dislike | uid, timestamp, item_id, is_organic |
is_organic=1 means the listener sought out the track; 0 means the action came through a recommendation surface. A listen is a conventional positive at 50% completion. The discovery-quality objective below uses the stronger 80% threshold.
The two public windows have different roles:
train_* as history and val_* as subsequent outcomes.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.
Rank only the candidates returned for the relevant cutoff. Relevance is based on organic behavior in the immediately following window:
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.
/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.parquetStage-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.
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.
/app/solution/./task/ and the installed Python environment as read-only.Focusing on Requirements
I'm currently dissecting the core task and its associated demands, prioritizing immediate action and strict adherence to all stipulated guidelines to ensure security, system integrity, and efficient context delivery.
{
"strategic_intent": "Explore environment, data files, evaluation script, and baseline requirements.",
"title": "Environment and Data Exploration",
"summary": "Starting the discovery ranker project. Exploring available data in /task/data/, the evaluation scripts /task/evaluate_public.py and /task/ranking_evaluation.py, and checking environment capabilities."
}Current topic: "Environment and Data Exploration"
Topic summary: Starting the discovery ranker project. Exploring available data in /task/data/, the evaluation scripts /task/evaluate_public.py and /task/ranking_evaluation.py, and checking environment capabilities.
Strategic Intent: Explore environment, data files, evaluation script, and baseline requirements.{
"command": "ls -la /task && ls -la /task/data && nvidia-smi || true && lscpu | grep \"Model name\\|CPU(s):\" && free -h",
"description": "Check files in /task, /task/data, GPU availability, CPU info, and RAM."
}<untrusted_context>
Output: total 36
dr-xr-xr-x 1 root root 4096 Sep 29 02:01 .
drwxr-xr-x 1 root root 4096 Sep 29 02:03 ..
dr-xr-xr-x 1 root root 4096 Sep 29 01:58 data
-r--r--r-- 1 root root 5774 Sep 29 01:57 evaluate_public.py
-r--r--r-- 1 root root 8265 Sep 29 01:57 ranking_evaluation.py
-r--r--r-- 1 root root 1278 Sep 29 01:57 requirements.txt
total 347392
dr-xr-xr-x 1 root root 4096 Sep 29 01:58 .
dr-xr-xr-x 1 root root 4096 Sep 29 02:01 ..
-r--r--r-- 1 root root 349352 Sep 29 01:57 candidate_catalog.parquet
-r--r--r-- 1 root root 84127 Sep 29 01:57 target_users.parquet
-r--r--r-- 1 root root 7736341 Sep 29 01:57 test_eligible_candidates.npz
-r--r--r-- 1 root root 1092329 Sep 29 01:58 test_preference_state.parquet
-r--r--r-- 1 root root 857213 Sep 29 01:57 train_dislikes.parquet
-r--r--r-- 1 root root 5374652 Sep 29 01:57 train_likes.parquet
-r--r--r-- 1 root root 319183255 Sep 29 01:57 train_listens.parquet
-r--r--r-- 1 root root 155988 Sep 29 01:57 train_undislikes.parquet
-r--r--r-- 1 root root 1855885 Sep 29 01:57 train_unlikes.parquet
-r--r--r-- 1 root root 36571 Sep 29 01:57 val_dislikes.parquet
-r--r--r-- 1 root root 298484 Sep 29 01:57 val_likes.parquet
-r--r--r-- 1 root root 9947609 Sep 29 01:57 val_listens.parquet
-r--r--r-- 1 root root 7845 Sep 29 01:57 val_undislikes.parquet
-r--r--r-- 1 root root 72536 Sep 29 01:57 val_unlikes.parquet
-r--r--r-- 1 root root 7573821 Sep 29 01:57 validation_eligible_candidates.npz
-r--r--r-- 1 root root 1061761 Sep 29 01:58 validation_preference_state.parquet
/usr/bin/bash: line 4: nvidia-smi: command not found
CPU(s): 8
Model name: AMD EPYC 7R13 Processor
NUMA node0 CPU(s): 0-7
/usr/bin/bash: line 4: free: command not found
Exit Code: 127
Process Group PGID: 1612
</untrusted_context>{
"description": "Check memory total and requirements.txt",
"command": "cat /proc/meminfo | grep MemTotal && cat /task/requirements.txt"
}<untrusted_context>
Output: MemTotal: 15988968 kB
# 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
Process Group PGID: 1620
</untrusted_context>{
"command": "python3 -c \"import sklearn, scipy, numpy, pandas; print('All core ml packages imported')\"",
"description": "Verify Python core imports"
}<untrusted_context>
Output: All core ml packages imported
Process Group PGID: 1625
</untrusted_context>{
"end_line": 200,
"file_path": "/task/evaluate_public.py",
"start_line": 1
}Path not in workspace: Attempted path "/task/evaluate_public.py" resolves outside the allowed workspace directories: /app or the project temp directory: /home/agent/.gemini/tmp/app{
"description": "Read evaluate_public.py",
"command": "cat /task/evaluate_public.py"
}<untrusted_context>
Output: #!/usr/bin/env python3
"""Score a validation-stage ranking against public discovery labels.
``build_task.py`` publishes this module with the solver-visible task files. It
loads target users, validation eligibility, and validation-window events from
the adjacent ``data`` directory, then evaluates one ranking supplied on the
command line. Stage-independent mechanics come from the adjacent public
``ranking_evaluation.py`` module. The builder installs the same checked-in
module in the private judge, so public and private scoring mechanics cannot
drift. A relevant candidate receives grade 3 for an organic like or grade 1 for
an organic listen with at least 80 percent completion; a like takes precedence
when both events exist.
Before scoring, the ranking must contain exactly 100 distinct eligible items
for every target user, with integer-valued ``uid``, ``item_id``, and ``rank``
columns and ranks 1 through 100 exactly once per user. Extra columns are
ignored. Quality is graded NDCG@10, averaged only over users who have at least
one eligible relevant item in the validation window.
The evaluator prints a JSON object containing the model path and scores. It
does not apply the task's pass threshold; its purpose is model development and
selection. The private judge applies the threshold to the final submission.
Usage::
python evaluate_public.py <validation-ranking.parquet|csv>
"""
from __future__ import annotations
import json
import sys
from pathlib import Path
import pandas as pd
from ranking_evaluation import (
CandidateSet,
RankingValidationError,
ranked_items,
read_ranking,
score_rankings,
validate_ranking,
)
HERE = Path(__file__).resolve().parent
DATA = HERE / "data"
LISTEN_STRONG = 80
def validation_truth(
eligibility: CandidateSet, targets: set[int]
) -> dict[int, dict[int, int]]:
"""Build cutoff-correct graded relevance from public validation events.
Only target-user events for eligible candidates are retained. Organic
listens completed to at least 80 percent receive grade 1, and organic likes
receive grade 3. Repeated user-item events are collapsed, and processing
likes second ensures that grade 3 wins over grade 1.
Args:
eligibility: Validation-cutoff candidate membership service.
targets: Complete set of target user IDs.
Returns:
A nested ``uid -> item_id -> grade`` mapping. Users without an eligible
relevant event are omitted.
"""
listens = pd.read_parquet(DATA / "val_listens.parquet")
likes = pd.read_parquet(DATA / "val_likes.parquet")
strong = listens[
listens.uid.isin(targets)
& (listens.is_organic == 1)
& (listens.played_ratio_pct >= LISTEN_STRONG)
][["uid", "item_id"]].drop_duplicates()
organic_likes = likes[
likes.uid.isin(targets) & (likes.is_organic == 1)
][["uid", "item_id"]].drop_duplicates()
graded: dict[int, dict[int, int]] = {}
for raw_uid, raw_item in strong.itertuples(index=False, name=None):
uid, item = int(raw_uid), int(raw_item)
if eligibility.is_eligible(uid, item):
graded.setdefault(uid, {})[item] = 1
for raw_uid, raw_item in organic_likes.itertuples(index=False, name=None):
uid, item = int(raw_uid), int(raw_item)
if eligibility.is_eligible(uid, item):
graded.setdefault(uid, {})[item] = 3
return graded
def load_submission(
path: Path, eligibility: CandidateSet, targets: set[int]
) -> dict[int, list[int]]:
"""Load and validate a public validation ranking.
Files ending in ``.parquet`` are read as parquet; every other suffix is
read as CSV. The required columns must be integer-valued, cover exactly the
target-user set, contain 100 rows and ranks 1 through 100 per user, avoid
duplicate user-item pairs, and contain only cutoff-eligible items.
Args:
path: Ranking file to read.
eligibility: Validation-cutoff candidate membership service.
targets: Exact set of users the ranking must cover.
Returns:
A ``uid -> ranked item IDs`` mapping ordered by ascending rank.
Raises:
ValueError: If the ranking violates the required schema or invariants.
"""
frame = validate_ranking(read_ranking(path), targets)
if not all(
eligibility.is_eligible(uid, item)
for uid, item in frame[["uid", "item_id"]].itertuples(index=False, name=None)
):
raise RankingValidationError(
"submission contains an item rejected by eligibility service"
)
return ranked_items(frame)
def score(
ranked: dict[int, list[int]],
graded: dict[int, dict[int, int]],
) -> dict[str, float | int]:
"""Aggregate per-user NDCG@10 over users with public relevance labels.
Args:
ranked: Ranked item IDs for every target user.
graded: Eligible validation relevance keyed by user and item.
Returns:
The six-decimal ``quality_ndcg@10`` value and number of scored users.
"""
return score_rankings(ranked, graded)
def main() -> None:
"""Run the public evaluator CLI and print its JSON score report."""
targets_frame = pd.read_parquet(DATA / "target_users.parquet")
targets = set(targets_frame.uid.astype(int))
eligibility = CandidateSet(DATA / "validation_eligible_candidates.npz")
graded = validation_truth(eligibility, targets)
if len(sys.argv) != 2:
raise SystemExit(
"usage: evaluate_public.py <validation-ranking.parquet|csv>"
)
path = Path(sys.argv[1])
ranked = load_submission(path, eligibility, targets)
print(json.dumps({"model": str(path), "scores": score(ranked, graded)}, indent=2))
if __name__ == "__main__":
main()
Process Group PGID: 1643
</untrusted_context>{
"description": "Read ranking_evaluation.py",
"command": "cat /task/ranking_evaluation.py"
}<untrusted_context>
Output: """Shared public primitives for Yambda discovery-ranking evaluation.
Both the solver-visible validation evaluator and the private submission judge
import this module from the generated task's ``public`` directory. It owns the
stage-independent contract: reading a ranking, validating its shape, converting
it to per-user rank order, looking up packed candidate eligibility, and
computing graded NDCG@10.
The module deliberately contains no event-derived labels, private artifact
paths, or pass thresholds. Each evaluator supplies its own relevance data and
per-cutoff eligibility checks around these common operations.
"""
from __future__ import annotations
from pathlib import Path
import numpy as np
import pandas as pd
__all__ = [
"CandidateSet",
"QUALITY_K",
"RankingValidationError",
"REQUIRED_COLUMNS",
"TOP_N",
"ndcg_at_k",
"ranked_items",
"read_ranking",
"score_rankings",
"validate_ranking",
]
TOP_N = 100
QUALITY_K = 10
REQUIRED_COLUMNS = ("uid", "item_id", "rank")
class RankingValidationError(ValueError):
"""Report a ranking-schema or structural-contract violation."""
class CandidateSet:
"""Provide membership lookups over one packed candidate-set archive.
The archive contains aligned ``uids``, ``item_ids``, and
``packed_eligible`` arrays. Candidate column ``c`` is represented by bit
``c % 8`` of byte ``c // 8`` using little-endian bit order. Arrays are
copied into memory so they remain valid after the archive is closed.
"""
def __init__(self, path: Path):
"""Load an eligibility archive and build ID-to-position indexes.
Args:
path: Path to a ``.npz`` archive with the candidate-set schema.
"""
with np.load(path, allow_pickle=False) as artifact:
self.uids = artifact["uids"].astype(np.int64, copy=True)
self.item_ids = artifact["item_ids"].astype(np.int64, copy=True)
self.packed = artifact["packed_eligible"].astype(np.uint8, copy=True)
self.uid_row = {int(uid): row for row, uid in enumerate(self.uids)}
self.item_column = {
int(item): column for column, item in enumerate(self.item_ids)
}
def is_eligible(self, uid: int, item_id: int) -> bool:
"""Return whether ``item_id`` is eligible for ``uid``.
IDs absent from the target-user or candidate-item arrays are treated as
ineligible rather than raising an exception.
Args:
uid: Target user ID to query.
item_id: Candidate item ID to query.
Returns:
``True`` only when the corresponding packed eligibility bit is set.
"""
row = self.uid_row.get(int(uid))
column = self.item_column.get(int(item_id))
if row is None or column is None:
return False
byte, bit = divmod(column, 8)
return bool((int(self.packed[row, byte]) >> bit) & 1)
def read_ranking(path: Path) -> pd.DataFrame:
"""Read a parquet or CSV ranking into a pandas frame.
Args:
path: Input path. A ``.parquet`` suffix selects parquet; every other
suffix is interpreted as CSV.
Returns:
The ranking frame as loaded by pandas.
"""
return pd.read_parquet(path) if path.suffix == ".parquet" else pd.read_csv(path)
def validate_ranking(
frame: pd.DataFrame,
targets: set[int],
) -> pd.DataFrame:
"""Normalize and validate the stage-independent ranking contract.
Required values must be finite integers. The user set must exactly match
``targets``; every user must have 100 rows, distinct items, and each rank
from 1 through 100 exactly once. Cutoff-specific candidate eligibility is
intentionally left to the calling evaluator.
Args:
frame: Raw ranking frame. Extra columns are discarded from an internal
copy; the caller's frame is not modified.
targets: Exact set of target user IDs the ranking must cover.
Returns:
A normalized frame containing only the required ``int64`` columns.
Raises:
RankingValidationError: If the ranking violates the shared contract.
"""
required = list(REQUIRED_COLUMNS)
if not all(column in frame for column in required):
raise RankingValidationError(f"submission requires columns {required}")
normalized = frame[required].copy()
for column in required:
numeric = pd.to_numeric(normalized[column], errors="coerce")
values = numeric.to_numpy(dtype=float)
if numeric.isna().any() or not np.isfinite(values).all():
raise RankingValidationError(
f"submission {column} must be finite numeric values"
)
if not np.equal(numeric, np.floor(numeric)).all():
raise RankingValidationError(
f"submission {column} must contain integers"
)
normalized[column] = numeric.astype(np.int64)
submitted_users = set(map(int, normalized.uid.unique()))
if submitted_users != targets:
raise RankingValidationError(
f"submission user set mismatch: missing={len(targets - submitted_users)} "
f"extra={len(submitted_users - targets)}"
)
counts = normalized.groupby("uid").size()
if not (counts == TOP_N).all():
raise RankingValidationError(
f"submission must contain exactly {TOP_N} rows for every target user"
)
if normalized.duplicated(["uid", "item_id"]).any():
raise RankingValidationError(
"submission contains duplicate (uid, item_id) pairs"
)
valid_ranks = set(range(1, TOP_N + 1))
ranks_are_valid = normalized.groupby("uid")["rank"].apply(
lambda values: set(map(int, values)) == valid_ranks
)
if not ranks_are_valid.all():
raise RankingValidationError(
f"each user must have every rank from 1 through {TOP_N} exactly once"
)
return normalized
def ranked_items(frame: pd.DataFrame) -> dict[int, list[int]]:
"""Convert a validated ranking frame to ordered items keyed by user.
Args:
frame: Frame that has passed :func:`validate_ranking`.
Returns:
A ``uid -> item IDs`` mapping ordered by ascending rank.
"""
ordered = frame.sort_values(["uid", "rank"])
return {
int(uid): list(map(int, values))
for uid, values in ordered.groupby("uid").item_id
}
def ndcg_at_k(ranked: list[int], gold: dict[int, int], k: int) -> float:
"""Compute graded normalized discounted cumulative gain at ``k``.
Relevance gain is ``2**grade - 1`` and the discount at one-based rank
``r`` is ``1 / log2(r + 1)``. Items absent from ``gold`` have grade zero.
Args:
ranked: Item IDs in predicted rank order.
gold: Mapping from relevant item IDs to integer relevance grades.
k: Maximum number of predicted positions to score.
Returns:
For a valid duplicate-free ranking, NDCG in ``[0, 1]``; returns
``0.0`` when the ideal gain is zero.
"""
gains = np.asarray([gold.get(item, 0) for item in ranked[:k]], dtype=np.float64)
discounts = 1.0 / np.log2(np.arange(2, len(gains) + 2))
dcg = float(np.sum((np.power(2.0, gains) - 1.0) * discounts))
ideal = np.asarray(sorted(gold.values(), reverse=True)[:k], dtype=np.float64)
ideal_discounts = 1.0 / np.log2(np.arange(2, len(ideal) + 2))
idcg = float(np.sum((np.power(2.0, ideal) - 1.0) * ideal_discounts))
return dcg / idcg if idcg else 0.0
def score_rankings(
ranked: dict[int, list[int]],
graded: dict[int, dict[int, int]],
) -> dict[str, float | int]:
"""Aggregate per-user NDCG@10 over users with relevance labels.
Args:
ranked: Ranked item IDs for every target user.
graded: Relevant item grades keyed by user and item.
Returns:
The six-decimal ``quality_ndcg@10`` value and number of scored users.
"""
quality = float(
np.mean(
[
ndcg_at_k(ranked[uid], gold, QUALITY_K)
for uid, gold in graded.items()
]
)
)
return {
"quality_ndcg@10": round(quality, 6),
"quality_users": len(graded),
}
Process Group PGID: 1646
</untrusted_context>Examining Data Structures
I'm now focusing on understanding the fundamental data shapes and characteristics. My next step involves executing a Python script to inspect key aspects of target_users.parquet, candidate_catalog.parquet, and the train_*.parquet and val_*.parquet files, specifically their column sets and item/user counts.
{
"description": "Inspect dataset statistics and structure",
"command": "python3 -c '\nimport pandas as pd\nimport numpy as np\nfrom pathlib import Path\n\nDATA = Path(\"/task/data\")\ntargets = pd.read_parquet(DATA / \"target_users.parquet\")\nprint(f\"Target users: {len(targets)}, cols: {targets.columns.tolist()}, sample uids: {targets.uid.head(3).tolist()}\")\n\ncatalog = pd.read_parquet(DATA / \"candidate_catalog.parquet\")\nprint(f\"Catalog items: {len(catalog)}, cols: {catalog.columns.tolist()}, head: {catalog.head(2).to_dict(orient=\\\"records\\\")}\")\n\nfor f in sorted(DATA.glob(\"*.parquet\")):\n df = pd.read_parquet(f)\n print(f\"{f.name}: {len(df)} rows, cols: {df.columns.tolist()}\")\n\nval_elig = np.load(DATA / \"validation_eligible_candidates.npz\")\nprint(f\"Val elig uids: {len(val_elig[\\\"uids\\\"])}, items: {len(val_elig[\\\"item_ids\\\"])}, packed: {val_elig[\\\"packed_eligible\\\"].shape}\")\n\ntest_elig = np.load(DATA / \"test_eligible_candidates.npz\")\nprint(f\"Test elig uids: {len(test_elig[\\\"uids\\\"])}, items: {len(test_elig[\\\"item_ids\\\"])}, packed: {test_elig[\\\"packed_eligible\\\"].shape}\")\n'"
}<untrusted_context>
Output: File "<string>", line 11
print(f"Catalog items: {len(catalog)}, cols: {catalog.columns.tolist()}, head: {catalog.head(2).to_dict(orient=\"records\")}")
^
SyntaxError: unexpected character after line continuation character
Exit Code: 1
Process Group PGID: 1649
</untrusted_context>{
"command": "python3 -c '\nimport pandas as pd\nimport numpy as np\nfrom pathlib import Path\n\nDATA = Path(\"/task/data\")\ntargets = pd.read_parquet(DATA / \"target_users.parquet\")\nprint(\"Target users:\", len(targets), targets.columns.tolist(), targets.uid.head(3).tolist())\n\ncatalog = pd.read_parquet(DATA / \"candidate_catalog.parquet\")\nprint(\"Catalog items:\", len(catalog), catalog.columns.tolist(), catalog.head(2).to_dict(orient=\"records\"))\n\nfor f in sorted(DATA.glob(\"*.parquet\")):\n df = pd.read_parquet(f)\n print(f\"{f.name}: {len(df)} rows, cols: {df.columns.tolist()}\")\n\nval_elig = np.load(DATA / \"validation_eligible_candidates.npz\")\nprint(\"Val elig uids:\", len(val_elig[\"uids\"]), \"items:\", len(val_elig[\"item_ids\"]), \"packed:\", val_elig[\"packed_eligible\"].shape)\n\ntest_elig = np.load(DATA / \"test_eligible_candidates.npz\")\nprint(\"Test elig uids:\", len(test_elig[\"uids\"]), \"items:\", len(test_elig[\"item_ids\"]), \"packed:\", test_elig[\"packed_eligible\"].shape)\n'",
"description": "Inspect dataset shapes and column names"
}<untrusted_context>
Output: Target users: 8456 ['uid', 'recent_positive_listens', 'history_positive_listens'] [100, 200, 300]
Catalog items: 30000 ['item_id', 'positive_listens', 'organic_positive_listens', 'popularity_rank'] [{'item_id': 5862961, 'positive_listens': 26615, 'organic_positive_listens': 20620, 'popularity_rank': 1}, {'item_id': 6901374, 'positive_listens': 25536, 'organic_positive_listens': 17347, 'popularity_rank': 2}]
candidate_catalog.parquet: 30000 rows, cols: ['item_id', 'positive_listens', 'organic_positive_listens', 'popularity_rank']
target_users.parquet: 8456 rows, cols: ['uid', 'recent_positive_listens', 'history_positive_listens']
test_preference_state.parquet: 484776 rows, cols: ['uid', 'item_id', 'state']
train_dislikes.parquet: 101075 rows, cols: ['uid', 'timestamp', 'item_id', 'is_organic']
train_likes.parquet: 814831 rows, cols: ['uid', 'timestamp', 'item_id', 'is_organic']
train_listens.parquet: 43484159 rows, cols: ['uid', 'timestamp', 'item_id', 'is_organic', 'played_ratio_pct', 'track_length_seconds']
train_undislikes.parquet: 19960 rows, cols: ['uid', 'timestamp', 'item_id', 'is_organic']
train_unlikes.parquet: 292665 rows, cols: ['uid', 'timestamp', 'item_id', 'is_organic']
val_dislikes.parquet: 3563 rows, cols: ['uid', 'timestamp', 'item_id', 'is_organic']
val_likes.parquet: 37795 rows, cols: ['uid', 'timestamp', 'item_id', 'is_organic']
val_listens.parquet: 1501697 rows, cols: ['uid', 'timestamp', 'item_id', 'is_organic', 'played_ratio_pct', 'track_length_seconds']
val_undislikes.parquet: 606 rows, cols: ['uid', 'timestamp', 'item_id', 'is_organic']
val_unlikes.parquet: 9346 rows, cols: ['uid', 'timestamp', 'item_id', 'is_organic']
validation_preference_state.parquet: 469124 rows, cols: ['uid', 'item_id', 'state']
Val elig uids: 8456 items: 30000 packed: (8456, 3750)
Test elig uids: 8456 items: 30000 packed: (8456, 3750)
Process Group PGID: 1652
</untrusted_context>Assessing Data Scale
My current focus is on understanding the sheer volume of data we're dealing with. The train_listens.parquet file is immense, clocking in at over 43 million rows, with val_listens.parquet also substantial. I'm analyzing how our target users and candidate catalog items fit within this scale and the implications for storage.
{
"command": "python3 -c '\nimport pandas as pd\nimport numpy as np\nimport pyarrow.parquet as pq\nfrom pathlib import Path\n\nDATA = Path(\"/task/data\")\ntargets = pd.read_parquet(DATA / \"target_users.parquet\")\ntarget_uids = set(targets.uid)\n\n# Check eligible items count\nval_elig = np.load(DATA / \"validation_eligible_candidates.npz\")\npacked = val_elig[\"packed_eligible\"]\nunpacked = np.unpackbits(packed, axis=1, count=30000, bitorder=\"little\")\nprint(\"Val eligible counts per user: min\", unpacked.sum(axis=1).min(), \"mean\", unpacked.sum(axis=1).mean(), \"max\", unpacked.sum(axis=1).max())\n\n# Check schema and metadata of train_listens\npf = pq.ParquetFile(DATA / \"train_listens.parquet\")\nprint(\"train_listens num row groups:\", pf.num_row_groups)\nprint(\"train_listens schema:\", pf.schema)\n\n# Read 1 row group or sample\nsample = pf.read_row_group(0, columns=[\"uid\", \"timestamp\", \"is_organic\", \"played_ratio_pct\"]).to_pandas()\nprint(\"train_listens sample uids in targets:\", sample.uid.isin(target_uids).mean())\nprint(\"train_listens sample timestamp min/max:\", sample.timestamp.min(), sample.timestamp.max())\n\n# Preference state\npref = pd.read_parquet(DATA / \"validation_preference_state.parquet\")\nprint(\"Validation pref state unique states:\", pref.state.value_counts().to_dict())\nprint(\"Validation pref uids in targets:\", pref.uid.isin(target_uids).mean())\n'",
"description": "Check eligibility counts, parquet row groups, and preference states"
}<untrusted_context>
Output: Val eligible counts per user: min 24657 mean 29067.306291390727 max 30000
train_listens num row groups: 42
train_listens schema: <pyarrow._parquet.ParquetSchema object at 0x7766ddb33c40>
required group field_id=-1 schema {
optional int32 field_id=-1 uid (Int(bitWidth=32, isSigned=false));
optional int32 field_id=-1 timestamp (Int(bitWidth=32, isSigned=false));
optional int32 field_id=-1 item_id (Int(bitWidth=32, isSigned=false));
optional int32 field_id=-1 is_organic (Int(bitWidth=8, isSigned=false));
optional int32 field_id=-1 played_ratio_pct (Int(bitWidth=16, isSigned=false));
optional int32 field_id=-1 track_length_seconds (Int(bitWidth=32, isSigned=false));
}
train_listens sample uids in targets: 0.9954624176025391
train_listens sample timestamp min/max: 15 24786690
Validation pref state unique states: {'liked': 418435, 'disliked': 50689}
Validation pref uids in targets: 1.0
Process Group PGID: 1672
</untrusted_context>{
"description": "Check test eligibility and preference state",
"command": "python3 -c '\nimport numpy as np\nimport pandas as pd\nDATA = \"/task/data/\"\ntest_elig = np.load(DATA + \"test_eligible_candidates.npz\")\npacked = test_elig[\"packed_eligible\"]\nunpacked = np.unpackbits(packed, axis=1, count=30000, bitorder=\"little\")\nprint(\"Test eligible counts per user: min\", unpacked.sum(axis=1).min(), \"mean\", unpacked.sum(axis=1).mean(), \"max\", unpacked.sum(axis=1).max())\npref = pd.read_parquet(DATA + \"test_preference_state.parquet\")\nprint(\"Test pref state:\", pref.state.value_counts().to_dict(), len(pref))\n'"
}<untrusted_context>
Output: Test eligible counts per user: min 24552 mean 29041.3178807947 max 30000
Test pref state: {'liked': 432659, 'disliked': 52117} 484776
Process Group PGID: 1690
</untrusted_context>Exploring Baseline Performance
I'm starting by establishing a simple baseline using candidate catalog popularity. The goal is to rank items for each user based on their existing popularity_rank from the candidate_catalog.parquet dataset to see how it performs.
{
"description": "Ensure /app/solution exists and is writable",
"command": "mkdir -p /app/solution && ls -la /app/solution"
}<untrusted_context>
Output: total 8
drwxr-xr-x 2 agent agent 4096 Sep 29 02:01 .
drwxr-xr-x 3 agent agent 4096 Sep 29 02:01 ..
Process Group PGID: 1707
</untrusted_context>{
"title": "Baseline Evaluation and Benchmarking",
"strategic_intent": "Implement popularity baseline on validation set and evaluate with evaluate_public.py.",
"summary": "Verified data directory, schemas, candidate eligibility format, and evaluation protocol. Now implementing a popularity baseline on validation data and evaluating it using the official evaluate_public.py script. This will establish an initial benchmark and verify end-to-end evaluation."
}Current topic: "Baseline Evaluation and Benchmarking"
Topic summary: Verified data directory, schemas, candidate eligibility format, and evaluation protocol. Now implementing a popularity baseline on validation data and evaluating it using the official evaluate_public.py script. This will establish an initial benchmark and verify end-to-end evaluation.
Strategic Intent: Implement popularity baseline on validation set and evaluate with evaluate_public.py.{
"command": "python3 -c '\nimport numpy as np\nimport pandas as pd\nfrom pathlib import Path\n\nDATA = Path(\"/task/data\")\ncatalog = pd.read_parquet(DATA / \"candidate_catalog.parquet\")\nval_elig = np.load(DATA / \"validation_eligible_candidates.npz\")\nprint(\"Catalog item_ids == val_elig item_ids?\", np.array_equal(catalog.item_id.values, val_elig[\"item_ids\"]))\nprint(\"Catalog item_ids head:\", catalog.item_id.head().tolist())\nprint(\"val_elig item_ids head:\", val_elig[\"item_ids\"][:5].tolist())\n'",
"description": "Check if candidate catalog and npz item_ids match"
}<untrusted_context>
Output: Catalog item_ids == val_elig item_ids? True
Catalog item_ids head: [5862961, 6901374, 5635052, 3542184, 9378983]
val_elig item_ids head: [5862961, 6901374, 5635052, 3542184, 9378983]
Process Group PGID: 1711
</untrusted_context>{
"description": "Check test_elig item_ids match catalog",
"command": "python3 -c '\nimport numpy as np\nimport pandas as pd\nfrom pathlib import Path\nDATA = Path(\"/task/data\")\ncatalog = pd.read_parquet(DATA / \"candidate_catalog.parquet\")\ntest_elig = np.load(DATA / \"test_eligible_candidates.npz\")\nprint(\"Catalog item_ids == test_elig item_ids?\", np.array_equal(catalog.item_id.values, test_elig[\"item_ids\"]))\n'"
}<untrusted_context>
Output: Catalog item_ids == test_elig item_ids? True
Process Group PGID: 1728
</untrusted_context>{
"command": "python3 -c '\nimport numpy as np\nimport pandas as pd\nfrom pathlib import Path\nDATA = Path(\"/task/data\")\ntargets = pd.read_parquet(DATA / \"target_users.parquet\")\nval_elig = np.load(DATA / \"validation_eligible_candidates.npz\")\ntest_elig = np.load(DATA / \"test_eligible_candidates.npz\")\nprint(\"Targets == val_elig uids?\", np.array_equal(targets.uid.values, val_elig[\"uids\"]))\nprint(\"Targets == test_elig uids?\", np.array_equal(targets.uid.values, test_elig[\"uids\"]))\n'",
"description": "Check target users uids match npz uids"
}<untrusted_context>
Output: Targets == val_elig uids? True
Targets == test_elig uids? True
Process Group PGID: 1745
</untrusted_context>Verifying User Data
My focus has shifted to examining validation_preference_state.parquet and test_preference_state.parquet. I’m particularly interested in identifying the unique users present within the validation dataset and understanding the various states that are recorded for them.
Analyzing Preference States
I’ve been looking at the validation_preference_state.parquet and test_preference_state.parquet files. My current thinking is that active dislikes for an item should significantly lower its ranking, potentially even filtering it out entirely. I’m also considering how active likes factor into the discovery objective.
{
"command": "python3 -c '\nimport pandas as pd\nimport numpy as np\nfrom pathlib import Path\nfrom ranking_evaluation import CandidateSet\n\nDATA = Path(\"/task/data\")\ntargets_frame = pd.read_parquet(DATA / \"target_users.parquet\")\ntargets = set(targets_frame.uid.astype(int))\neligibility = CandidateSet(DATA / \"validation_eligible_candidates.npz\")\n\nlistens = pd.read_parquet(DATA / \"val_listens.parquet\")\nlikes = pd.read_parquet(DATA / \"val_likes.parquet\")\nstrong = listens[\n listens.uid.isin(targets)\n & (listens.is_organic == 1)\n & (listens.played_ratio_pct >= 80)\n][[\"uid\", \"item_id\"]].drop_duplicates()\norganic_likes = likes[\n likes.uid.isin(targets) & (likes.is_organic == 1)\n][[\"uid\", \"item_id\"]].drop_duplicates()\n\nprint(\"Eligible organic strong listens count:\", sum(eligibility.is_eligible(u, i) for u, i in strong.itertuples(index=False)))\nprint(\"Total organic strong listens count:\", len(strong))\nprint(\"Eligible organic likes count:\", sum(eligibility.is_eligible(u, i) for u, i in organic_likes.itertuples(index=False)))\nprint(\"Total organic likes count:\", len(organic_likes))\n\npref = pd.read_parquet(DATA / \"validation_preference_state.parquet\")\npref_likes = set(zip(pref[pref.state == \"liked\"].uid, pref[pref.state == \"liked\"].item_id))\npref_dislikes = set(zip(pref[pref.state == \"disliked\"].uid, pref[pref.state == \"disliked\"].item_id))\n\nstrong_pairs = set(zip(strong.uid, strong.item_id))\nlike_pairs = set(zip(organic_likes.uid, organic_likes.item_id))\n\nprint(\"Strong in pref_likes:\", len(strong_pairs.intersection(pref_likes)))\nprint(\"Strong in pref_dislikes:\", len(strong_pairs.intersection(pref_dislikes)))\nprint(\"Likes in pref_likes:\", len(like_pairs.intersection(pref_likes)))\nprint(\"Likes in pref_dislikes:\", len(like_pairs.intersection(pref_dislikes)))\n'",
"description": "Check overlap of ground truth with preference state"
}<untrusted_context>
Output: Traceback (most recent call last):
File "<string>", line 5, in <module>
ModuleNotFoundError: No module named 'ranking_evaluation'
Exit Code: 1
Process Group PGID: 1761
</untrusted_context>{
"command": "python3 -c '\nimport sys\nsys.path.append(\"/task\")\nimport pandas as pd\nimport numpy as np\nfrom pathlib import Path\nfrom ranking_evaluation import CandidateSet\n\nDATA = Path(\"/task/data\")\ntargets_frame = pd.read_parquet(DATA / \"target_users.parquet\")\ntargets = set(targets_frame.uid.astype(int))\neligibility = CandidateSet(DATA / \"validation_eligible_candidates.npz\")\n\nlistens = pd.read_parquet(DATA / \"val_listens.parquet\")\nlikes = pd.read_parquet(DATA / \"val_likes.parquet\")\nstrong = listens[\n listens.uid.isin(targets)\n & (listens.is_organic == 1)\n & (listens.played_ratio_pct >= 80)\n][[\"uid\", \"item_id\"]].drop_duplicates()\norganic_likes = likes[\n likes.uid.isin(targets) & (likes.is_organic == 1)\n][[\"uid\", \"item_id\"]].drop_duplicates()\n\nprint(\"Eligible organic strong listens count:\", sum(eligibility.is_eligible(u, i) for u, i in strong.itertuples(index=False)))\nprint(\"Total organic strong listens count:\", len(strong))\nprint(\"Eligible organic likes count:\", sum(eligibility.is_eligible(u, i) for u, i in organic_likes.itertuples(index=False)))\nprint(\"Total organic likes count:\", len(organic_likes))\n\npref = pd.read_parquet(DATA / \"validation_preference_state.parquet\")\npref_likes = set(zip(pref[pref.state == \"liked\"].uid, pref[pref.state == \"liked\"].item_id))\npref_dislikes = set(zip(pref[pref.state == \"disliked\"].uid, pref[pref.state == \"disliked\"].item_id))\n\nstrong_pairs = set(zip(strong.uid, strong.item_id))\nlike_pairs = set(zip(organic_likes.uid, organic_likes.item_id))\n\nprint(\"Strong in pref_likes:\", len(strong_pairs.intersection(pref_likes)))\nprint(\"Strong in pref_dislikes:\", len(strong_pairs.intersection(pref_dislikes)))\nprint(\"Likes in pref_likes:\", len(like_pairs.intersection(pref_likes)))\nprint(\"Likes in pref_dislikes:\", len(like_pairs.intersection(pref_dislikes)))\n'",
"description": "Check overlap of ground truth with preference state with pythonpath"
}<untrusted_context>
Output: Eligible organic strong listens count: 35597
Total organic strong listens count: 255608
Eligible organic likes count: 4417
Total organic likes count: 17195
Strong in pref_likes: 55440
Strong in pref_dislikes: 427
Likes in pref_likes: 672
Likes in pref_dislikes: 16
Process Group PGID: 1772
</untrusted_context>Analyzing Eligible Counts
I'm currently diving into the eligible organic strong listens and likes to pinpoint how many fall within pref_likes. It's crucial to isolate this subset for our analysis.
{
"description": "Check how many eligible positive pairs are in pref_likes and pref_dislikes",
"command": "python3 -c '\nimport sys\nsys.path.append(\"/task\")\nimport pandas as pd\nimport numpy as np\nfrom pathlib import Path\nfrom ranking_evaluation import CandidateSet\n\nDATA = Path(\"/task/data\")\ntargets_frame = pd.read_parquet(DATA / \"target_users.parquet\")\ntargets = set(targets_frame.uid.astype(int))\neligibility = CandidateSet(DATA / \"validation_eligible_candidates.npz\")\n\nlistens = pd.read_parquet(DATA / \"val_listens.parquet\")\nlikes = pd.read_parquet(DATA / \"val_likes.parquet\")\nstrong = listens[\n listens.uid.isin(targets)\n & (listens.is_organic == 1)\n & (listens.played_ratio_pct >= 80)\n][[\"uid\", \"item_id\"]].drop_duplicates()\norganic_likes = likes[\n likes.uid.isin(targets) & (likes.is_organic == 1)\n][[\"uid\", \"item_id\"]].drop_duplicates()\n\npref = pd.read_parquet(DATA / \"validation_preference_state.parquet\")\npref_likes = set(zip(pref[pref.state == \"liked\"].uid, pref[pref.state == \"liked\"].item_id))\npref_dislikes = set(zip(pref[pref.state == \"disliked\"].uid, pref[pref.state == \"disliked\"].item_id))\n\nstrong_pairs_elig = set((u, i) for u, i in strong.itertuples(index=False) if eligibility.is_eligible(u, i))\nlike_pairs_elig = set((u, i) for u, i in organic_likes.itertuples(index=False) if eligibility.is_eligible(u, i))\n\nprint(\"Eligible strong in pref_likes:\", len(strong_pairs_elig.intersection(pref_likes)), \"out of\", len(strong_pairs_elig))\nprint(\"Eligible strong in pref_dislikes:\", len(strong_pairs_elig.intersection(pref_dislikes)))\nprint(\"Eligible likes in pref_likes:\", len(like_pairs_elig.intersection(pref_likes)), \"out of\", len(like_pairs_elig))\nprint(\"Eligible likes in pref_dislikes:\", len(like_pairs_elig.intersection(pref_dislikes)))\n'"
}<untrusted_context>
Output: Eligible strong in pref_likes: 0 out of 35597
Eligible strong in pref_dislikes: 0
Eligible likes in pref_likes: 0 out of 4417
Eligible likes in pref_dislikes: 0
Process Group PGID: 1792
</untrusted_context>Analyzing Current Results
My analysis has revealed a complete lack of eligible entries across all preference categories. I'm now investigating the underlying data to understand why no matches are being found.
{
"description": "Check if any pref state item is eligible in validation",
"command": "python3 -c '\nimport sys\nsys.path.append(\"/task\")\nimport pandas as pd\nimport numpy as np\nfrom pathlib import Path\nfrom ranking_evaluation import CandidateSet\n\nDATA = Path(\"/task/data\")\neligibility = CandidateSet(DATA / \"validation_eligible_candidates.npz\")\npref = pd.read_parquet(DATA / \"validation_preference_state.parquet\")\nprint(\"Are any pref items eligible?\", any(eligibility.is_eligible(u, i) for u, i in pref.itertuples(index=False)))\n'"
}<untrusted_context>
Output: Traceback (most recent call last):
File "<string>", line 12, in <module>
File "<string>", line 12, in <genexpr>
ValueError: too many values to unpack (expected 2)
Exit Code: 1
Process Group PGID: 1812
</untrusted_context>{
"description": "Check if any pref state item is eligible",
"command": "python3 -c '\nimport sys\nsys.path.append(\"/task\")\nimport pandas as pd\nimport numpy as np\nfrom pathlib import Path\nfrom ranking_evaluation import CandidateSet\n\nDATA = Path(\"/task/data\")\neligibility = CandidateSet(DATA / \"validation_eligible_candidates.npz\")\npref = pd.read_parquet(DATA / \"validation_preference_state.parquet\")\nprint(\"Are any pref items eligible?\", any(eligibility.is_eligible(u, i) for u, i, s in pref.itertuples(index=False)))\n'"
}<untrusted_context>
Output: Are any pref items eligible? False
Process Group PGID: 1830
</untrusted_context>Exploring Eligibility Factors
I'm currently investigating why no items are appearing as eligible for validation. It seems the existing eligibility logic already filters out items the user has interacted with. I need to delve deeper into what other factors might be impacting eligibility.
{
"description": "Check if train_listens items are eligible",
"command": "python3 -c '\nimport sys\nsys.path.append(\"/task\")\nimport pandas as pd\nimport numpy as np\nimport pyarrow.parquet as pq\nfrom pathlib import Path\nfrom ranking_evaluation import CandidateSet\n\nDATA = Path(\"/task/data\")\neligibility = CandidateSet(DATA / \"validation_eligible_candidates.npz\")\npf = pq.ParquetFile(DATA / \"train_listens.parquet\")\n# check row group 0\nsample = pf.read_row_group(0, columns=[\"uid\", \"item_id\", \"played_ratio_pct\"]).to_pandas()\nprint(\"Total rows in sample:\", len(sample))\nsample_elig = [eligibility.is_eligible(u, i) for u, i in zip(sample.uid, sample.item_id)]\nprint(\"Eligible in train_listens sample:\", sum(sample_elig), \"out of\", len(sample_elig))\n'"
}<untrusted_context>
Output: Total rows in sample: 1048576
Eligible in train_listens sample: 0 out of 1048576
Process Group PGID: 1848
</untrusted_context>{
"description": "Check first 5 row groups of train_listens for eligibility",
"command": "python3 -c '\nimport sys\nsys.path.append(\"/task\")\nimport pandas as pd\nimport numpy as np\nimport pyarrow.parquet as pq\nfrom pathlib import Path\nfrom ranking_evaluation import CandidateSet\n\nDATA = Path(\"/task/data\")\neligibility = CandidateSet(DATA / \"validation_eligible_candidates.npz\")\npf = pq.ParquetFile(DATA / \"train_listens.parquet\")\nfound_elig = 0\ntotal_checked = 0\nfor i in range(5):\n rg = pf.read_row_group(i, columns=[\"uid\", \"item_id\"]).to_pandas()\n for u, item in zip(rg.uid, rg.item_id):\n if eligibility.is_eligible(u, item):\n found_elig += 1\n total_checked += len(rg)\n print(f\"Group {i}: checked {total_checked}, found {found_elig}\")\n'"
}<untrusted_context>
Output: Group 0: checked 1048576, found 0
Group 1: checked 2097152, found 0
Group 2: checked 3145728, found 0
Group 3: checked 4194304, found 0
Group 4: checked 5242880, found 0
Process Group PGID: 1862
</untrusted_context>Understanding Discovery Models
My thoughts are currently focused on defining what "discovery" entails in this context. I'm working through the requirements for building a robust ranking model for a music streaming service's 'Made for You' shelf, specifically considering per-user candidate eligibility, cutoff-specific explicit preference states, and temporal backtesting.
{
"command": "python3 -c '\nimport numpy as np\nimport pandas as pd\nfrom pathlib import Path\nimport time\n\nt0 = time.time()\nDATA = Path(\"/task/data\")\ntargets = pd.read_parquet(DATA / \"target_users.parquet\")\nuids = targets.uid.values\n\nval_elig = np.load(DATA / \"validation_eligible_candidates.npz\")\nitem_ids = val_elig[\"item_ids\"]\npacked = val_elig[\"packed_eligible\"]\ncatalog = pd.read_parquet(DATA / \"candidate_catalog.parquet\")\n\nprint(\"Catalog cols:\", catalog.columns.tolist())\n# Catalog is already ordered by positive_listens popularity: catalog.popularity_rank == 1..30000\n# And catalog.item_id matches val_elig item_ids!\n# So column index 0 is rank 1, column index 1 is rank 2, etc.\n\n# For each user, we unpack bits and pick the first 100 eligible items!\n# Let us vectorize or do fast batch extraction.\nrows = []\nfor r in range(len(uids)):\n uid = uids[r]\n # unpack\n mask = np.unpackbits(packed[r], count=len(item_ids), bitorder=\"little\")\n # indices of eligible items\n eligible_idx = np.where(mask == 1)[0][:100]\n top_items = item_ids[eligible_idx]\n for rank_idx, item in enumerate(top_items, 1):\n rows.append((uid, item, rank_idx))\n\ndf = pd.DataFrame(rows, columns=[\"uid\", \"item_id\", \"rank\"])\ndf.to_parquet(\"/app/solution/val_pop_baseline.parquet\")\nprint(f\"Saved baseline in {time.time()-t0:.2f}s, shape: {df.shape}\")\n' && python3 /task/evaluate_public.py /app/solution/val_pop_baseline.parquet",
"description": "Build and evaluate popularity baseline on validation set"
}<untrusted_context>
Output: Catalog cols: ['item_id', 'positive_listens', 'organic_positive_listens', 'popularity_rank']
Saved baseline in 1.32s, shape: (845600, 3)
{
"model": "/app/solution/val_pop_baseline.parquet",
"scores": {
"quality_ndcg@10": 0.01071,
"quality_users": 4263
}
}
Process Group PGID: 1875
</untrusted_context>Generating Baseline Submission
My baseline popularity model is performing. It completes its run in 1.32 seconds, achieving a quality_ndcg@10 of 0.01071 and engaging 4263 out of 8456 users. I'm also generating a baseline submission for the test set to ensure a valid submission is always available.
{
"command": "python3 -c '\nimport numpy as np\nimport pandas as pd\nfrom pathlib import Path\nimport time\nimport sys\nsys.path.append(\"/task\")\nfrom ranking_evaluation import validate_ranking, CandidateSet\n\nt0 = time.time()\nDATA = Path(\"/task/data\")\ntargets = pd.read_parquet(DATA / \"target_users.parquet\")\nuids = targets.uid.values\n\ntest_elig = np.load(DATA / \"test_eligible_candidates.npz\")\nitem_ids = test_elig[\"item_ids\"]\npacked = test_elig[\"packed_eligible\"]\n\nrows = []\nfor r in range(len(uids)):\n uid = uids[r]\n mask = np.unpackbits(packed[r], count=len(item_ids), bitorder=\"little\")\n eligible_idx = np.where(mask == 1)[0][:100]\n top_items = item_ids[eligible_idx]\n for rank_idx, item in enumerate(top_items, 1):\n rows.append((uid, item, rank_idx))\n\ndf = pd.DataFrame(rows, columns=[\"uid\", \"item_id\", \"rank\"])\nsub_path = \"/app/solution/submission.parquet\"\ndf.to_parquet(sub_path)\nprint(f\"Saved initial submission in {time.time()-t0:.2f}s, shape: {df.shape}\")\n\n# Validate using ranking_evaluation\ntarget_set = set(targets.uid.astype(int))\nvalidated = validate_ranking(df, target_set)\nelig = CandidateSet(DATA / \"test_eligible_candidates.npz\")\nfor uid, item in validated[[\"uid\", \"item_id\"]].itertuples(index=False, name=None):\n if not elig.is_eligible(uid, item):\n raise ValueError(f\"Ineligible: {uid}, {item}\")\nprint(\"Submission verified valid against test_eligible_candidates!\")\n'",
"description": "Generate and verify initial baseline submission for test set"
}<untrusted_context>
Output: Saved initial submission in 1.33s, shape: (845600, 3)
Submission verified valid against test_eligible_candidates!
Process Group PGID: 1910
</untrusted_context>Confirming Initial Baseline
My initial baseline submission is now saved and has been successfully verified against both validate_ranking and test_eligible_candidates.npz. The next phase involves a deep dive into the data to inform the design of robust modeling strategies aimed at optimizing quality_ndcg@10.
{
"title": "Data Analysis and Recommender Architecture Research",
"strategic_intent": "Research data distributions, interaction types, and candidate recommendation algorithms.",
"summary": "Baseline established: candidate catalog popularity achieves quality_ndcg@10 of 0.01071. Initial valid test submission created and verified against ranking_evaluation. Now researching user listening histories, item metadata/associations, implicit collaborative filtering, matrix factorization, and graph-based / co-occurrence methods to improve recommendation quality."
}Current topic: "Data Analysis and Recommender Architecture Research"
Topic summary: Baseline established: candidate catalog popularity achieves quality_ndcg@10 of 0.01071. Initial valid test submission created and verified against ranking_evaluation. Now researching user listening histories, item metadata/associations, implicit collaborative filtering, matrix factorization, and graph-based / co-occurrence methods to improve recommendation quality.
Strategic Intent: Research data distributions, interaction types, and candidate recommendation algorithms.Analyzing Candidate Features
I'm currently examining the available features within the candidate catalog. My focus is on understanding the distributions and relationships between item_id, positive_listens, organic_positive_listens, and popularity_rank to identify potent signals for recommendation.
{
"description": "Test organic popularity baseline on validation set",
"command": "python3 -c '\nimport numpy as np\nimport pandas as pd\nfrom pathlib import Path\nimport time\nimport sys\nsys.path.append(\"/task\")\nfrom ranking_evaluation import CandidateSet, ranked_items, score_rankings, validate_ranking\nfrom evaluate_public import validation_truth\n\nDATA = Path(\"/task/data\")\ntargets_frame = pd.read_parquet(DATA / \"target_users.parquet\")\ntargets = set(targets_frame.uid.astype(int))\nuids = targets_frame.uid.values\neligibility = CandidateSet(DATA / \"validation_eligible_candidates.npz\")\ngraded = validation_truth(eligibility, targets)\n\ncatalog = pd.read_parquet(DATA / \"candidate_catalog.parquet\")\nval_elig = np.load(DATA / \"validation_eligible_candidates.npz\")\nitem_ids = val_elig[\"item_ids\"]\npacked = val_elig[\"packed_eligible\"]\n\n# Try ordering by organic_positive_listens\ncatalog_organic_order = catalog.sort_values(\"organic_positive_listens\", ascending=False).index.values\n\n# Test global popularity by organic_positive_listens\nrows = []\nfor r in range(len(uids)):\n uid = uids[r]\n mask = np.unpackbits(packed[r], count=len(item_ids), bitorder=\"little\")\n # mask in organic order\n eligible_in_order = catalog_organic_order[mask[catalog_organic_order] == 1][:100]\n top_items = item_ids[eligible_in_order]\n for rank_idx, item in enumerate(top_items, 1):\n rows.append((uid, item, rank_idx))\n\ndf = pd.DataFrame(rows, columns=[\"uid\", \"item_id\", \"rank\"])\nranked = ranked_items(df)\nscores = score_rankings(ranked, graded)\nprint(\"Organic popularity baseline score:\", scores)\n'"
}<untrusted_context>
Output: Organic popularity baseline score: {'quality_ndcg@10': 0.010578, 'quality_users': 4263}
Process Group PGID: 1926
</untrusted_context>Exploring Personalization Metrics
My latest analysis shows positive_listens slightly outperformed organic_positive_listens. I'm now shifting focus to personalization. I'm examining available user history, specifically the train_likes.parquet dataset with its uid, timestamp, and item_id columns, to inform this next step.
{
"command": "python3 -c '\nimport pandas as pd\nimport numpy as np\nimport pyarrow.parquet as pq\nfrom pathlib import Path\n\nDATA = Path(\"/task/data\")\ncatalog = pd.read_parquet(DATA / \"candidate_catalog.parquet\")\ncandidate_items = set(catalog.item_id)\n\ntrain_likes = pd.read_parquet(DATA / \"train_likes.parquet\")\nprint(\"Train likes:\", len(train_likes), \"unique users:\", train_likes.uid.nunique(), \"unique items:\", train_likes.item_id.nunique())\nprint(\"Train likes candidate overlap:\", train_likes.item_id.isin(candidate_items).mean())\n\npref = pd.read_parquet(DATA / \"validation_preference_state.parquet\")\nprint(\"Validation pref:\", len(pref), \"unique users:\", pref.uid.nunique(), \"unique items:\", pref.item_id.nunique())\nprint(\"Validation pref candidate overlap:\", pref.item_id.isin(candidate_items).mean())\n\n# Let us check timestamps across files\nprint(\"Train likes min/max timestamp:\", train_likes.timestamp.min(), train_likes.timestamp.max())\n'",
"description": "Check train likes and preference items vs candidate catalog"
}<untrusted_context>
Output: Train likes: 814831 unique users: 8144 unique items: 172303
Train likes candidate overlap: 0.6809068383505291
Validation pref: 469124 unique users: 7038 unique items: 28814
Validation pref candidate overlap: 1.0
Train likes min/max timestamp: 80 24786790
Process Group PGID: 1946
</untrusted_context>Analyzing Data Intersections
I'm currently examining the overlap between training listens and the candidate catalog. Initial validation data shows a significant number of rows and unique users/items. The strong overlap in liked items is promising, but I need to understand how the training listen data fits into this.
{
"command": "python3 -c '\nimport pyarrow.parquet as pq\nimport time\nfrom pathlib import Path\n\nDATA = Path(\"/task/data\")\nt0 = time.time()\ntable = pq.read_table(DATA / \"train_listens.parquet\", columns=[\"uid\", \"item_id\", \"is_organic\", \"played_ratio_pct\", \"timestamp\"])\nt1 = time.time()\nprint(f\"Read train_listens in {t1-t0:.2f}s, num rows: {table.num_rows}\")\n'",
"description": "Benchmark train_listens read speed"
}<untrusted_context>
Output: Read train_listens in 0.71s, num rows: 43484159
Process Group PGID: 1963
</untrusted_context>Exploring PyArrow Speed
I'm impressed by PyArrow's speed, processing over 43 million rows in under a second. My focus is shifting to understanding the core mechanics of recommender systems, specifically what drives successful discovery recommendations. I'm currently considering the necessity of user historical interaction data for effective training.
Considering Item Similarity
My current focus is on item-item similarity for recommendations. I'm evaluating whether a dense 30,000x30,000 matrix is feasible with 16GB RAM, or if a sparse representation is necessary given the memory constraints for this scale.
Evaluating Matrix Factorization
My current thoughts are exploring matrix factorization techniques. I'm realizing that with embeddings of dimensions 64 or 128, the resulting user and item matrices are quite manageable in size and can be used for rapid prediction generation, significantly outperforming previous estimations.
{
"description": "Check fraction of train_listens in candidate catalog",
"command": "python3 -c '\nimport pyarrow.parquet as pq\nimport pandas as pd\nfrom pathlib import Path\n\nDATA = Path(\"/task/data\")\ncatalog = pd.read_parquet(DATA / \"candidate_catalog.parquet\")\ncandidate_items = set(catalog.item_id)\n\ntable = pq.read_table(DATA / \"train_listens.parquet\", columns=[\"item_id\"])\nitems = table[\"item_id\"].to_numpy()\nin_cat = pd.Series(items).isin(candidate_items).mean()\nprint(f\"Fraction of train_listens in candidate_catalog: {in_cat:.4f}\")\n'"
}<untrusted_context>
Output: Fraction of train_listens in candidate_catalog: 0.7186
Process Group PGID: 1985
</untrusted_context>Analyzing User Preferences
I'm focusing on understanding user preferences within our candidate catalog. Initial analysis shows a strong concentration of listens in this area. I'm currently investigating explicit likes and the validation preference state as key signals.
{
"description": "Check timestamp ranges and duration",
"command": "python3 -c '\nimport pandas as pd\nfrom pathlib import Path\n\nDATA = Path(\"/task/data\")\nval_listens = pd.read_parquet(DATA / \"val_listens.parquet\", columns=[\"timestamp\"])\nval_likes = pd.read_parquet(DATA / \"val_likes.parquet\", columns=[\"timestamp\"])\n\nprint(\"Val listens min/max timestamp:\", val_listens.timestamp.min(), val_listens.timestamp.max())\nprint(\"Val duration (days):\", (val_listens.timestamp.max() - val_listens.timestamp.min()) / 86400)\nprint(\"Gap after train (s):\", val_listens.timestamp.min() - 24786800)\n'"
}<untrusted_context>
Output: Val listens min/max timestamp: 24788600 25393395
Val duration (days): 6.99994212962963
Gap after train (s): 1800
Process Group PGID: 2006
</untrusted_context>{
"command": "python3 -c '\nimport pyarrow.parquet as pq\nimport numpy as np\n\ntable = pq.read_table(\"/task/data/train_listens.parquet\", columns=[\"timestamp\"])\nts = table[\"timestamp\"].to_numpy()\nprint(\"Min timestamp:\", ts.min(), \"Max timestamp:\", ts.max())\nprint(\"Total train range in days:\", (ts.max() - ts.min()) / 86400)\n# Percentiles:\nfor p in [10, 25, 50, 75, 90, 95]:\n print(f\"p{p}: {(ts.max() - np.percentile(ts, p)) / 86400:.1f} days before cutoff\")\n'",
"description": "Check train timestamp distribution"
}<untrusted_context>
Output: Min timestamp: 0 Max timestamp: 24786795
Total train range in days: 286.88420138888887
p10: 248.4 days before cutoff
p25: 197.3 days before cutoff
p50: 122.0 days before cutoff
p75: 55.3 days before cutoff
p90: 21.1 days before cutoff
p95: 10.3 days before cutoff
Process Group PGID: 2027
</untrusted_context>Analyzing Validation Performance
My current focus is on dissecting the validation evaluation. I'm examining evaluate_public.py and its reliance on validation_truth to understand the model's performance on unseen data.
Assessing Data Eligibility
I'm analyzing the validation set, specifically focusing on users with eligible organic listens or likes. Around 50% of target users meet this criterion within the validation window. I'm also starting to explore how to predict user engagement with candidate items.
Exploring Collaborative Filtering
I'm now delving into collaborative filtering signals, considering user likes, organic listens, listen frequency, dislikes, and track recency. I'm also investigating how candidate items' popularity and user affinities influence recommendations. I'm starting to assess the feasibility of an item-item collaborative filtering model.
{
"command": "python3 -c '\nimport pyarrow.parquet as pq\ntable = pq.read_table(\"/task/data/train_listens.parquet\", columns=[\"uid\"])\nuids = set(table[\"uid\"].to_numpy())\nprint(\"Total unique users in train_listens:\", len(uids))\ntargets = set(pq.read_table(\"/task/data/target_users.parquet\", columns=[\"uid\"])[\"uid\"].to_numpy())\nprint(\"Target users count:\", len(targets))\nprint(\"Target users in train_listens:\", len(targets.intersection(uids)))\n'",
"description": "Check unique users in train_listens vs targets"
}<untrusted_context>
Output: Total unique users in train_listens: 9238
Target users count: 8456
Target users in train_listens: 8456
Process Group PGID: 2049
</untrusted_context>Confirming User Coverage
It's great to see that all 8,456 target users are accounted for within the train_listens dataset, which contains a total of 9,238 unique users. My next step is to determine the dimensions of our user-item matrix, $X$.
{
"description": "Count unique user-item pairs in train_listens",
"command": "python3 -c '\nimport pyarrow.parquet as pq\nimport pandas as pd\nfrom pathlib import Path\n\nDATA = Path(\"/task/data\")\ntargets = set(pd.read_parquet(DATA / \"target_users.parquet\").uid)\ncatalog = pd.read_parquet(DATA / \"candidate_catalog.parquet\")\ncat_items = set(catalog.item_id)\n\ntable = pq.read_table(DATA / \"train_listens.parquet\", columns=[\"uid\", \"item_id\", \"played_ratio_pct\"])\ndf = table.to_pandas()\ndf_cat = df[df.uid.isin(targets) & df.item_id.isin(cat_items)]\nprint(\"Total listens in catalog for targets:\", len(df_cat))\nunique_pairs = df_cat[[\"uid\", \"item_id\"]].drop_duplicates()\nprint(\"Unique (uid, item) pairs in catalog:\", len(unique_pairs))\n'"
}<untrusted_context>
Output: Total listens in catalog for targets: 31062540
Unique (uid, item) pairs in catalog: 7868664
Process Group PGID: 2069
</untrusted_context>Analyzing Data Density
My analysis reveals a 3.1% density from 7.86 million unique (uid, item) pairs out of a possible 253.68 million. I'm now considering incorporating train_likes and validation_preference_state to potentially increase this density.
Exploring Algorithmic Pathways
I'm now exploring various algorithms to leverage the user-item matrix. My current focus is on how to best apply SVD with potential weighting schemes like TF-IDF, BM25, or even incorporating recency, and also on item-item collaborative filtering approaches.
Optimizing Matrix Computations
I've realized we can avoid forming a large $30000 \times 30000$ matrix. By using the associative property of matrix multiplication, I can compute $X X_{norm}^T$, resulting in a much smaller $8456 \times 8456$ matrix. This approach significantly reduces memory and computation time compared to direct SVD on the full matrix.
Evaluating User-User Similarity
I've determined that computing user-user cosine similarity is highly feasible given only 8,456 target users. The resulting similarity matrix, $W$, will be a manageable $8456 \times 8456$, requiring approximately 286 MB, which is well within our computational limits. This contrasts sharply with the memory demands of dense EASE.
{
"description": "Benchmark User-User CF sparse matrix operations",
"command": "python3 -c '\nimport numpy as np\nimport scipy.sparse as sp\nimport time\n\n# Create a sparse matrix with 8456 rows, 30000 cols, ~8 million nonzeros (~3% dense)\nn_users = 8456\nn_items = 30000\nnnz = 8000000\nrows = np.random.randint(0, n_users, size=nnz, dtype=np.int32)\ncols = np.random.randint(0, n_items, size=nnz, dtype=np.int32)\ndata = np.random.rand(nnz).astype(np.float32)\n\nt0 = time.time()\nX = sp.csr_matrix((data, (rows, cols)), shape=(n_users, n_items), dtype=np.float32)\nprint(\"Built CSR in\", time.time() - t0)\n\n# Normalize rows\nt0 = time.time()\nnorm = sp.linalg.norm(X, axis=1)\nnorm[norm == 0] = 1.0\nX_norm = sp.diags(1.0 / norm.ravel()) @ X\nprint(\"Normalized in\", time.time() - t0)\n\n# Multiply X_norm @ X_norm.T\nt0 = time.time()\nW = X_norm @ X_norm.T\nprint(\"W = X_norm @ X_norm.T took:\", time.time() - t0, \"seconds! Shape:\", W.shape)\n\n# Now predict: W @ X\nt0 = time.time()\n# For each user, predicted score is W_u @ X\n# We can do this in chunks or full!\nP = W @ X\nprint(\"P = W @ X took:\", time.time() - t0, \"seconds! Shape:\", P.shape)\n'"
}<untrusted_context>
Output: Built CSR in 0.33393287658691406
Normalized in 0.07563352584838867
W = X_norm @ X_norm.T took: 6.302556276321411 seconds! Shape: (8456, 8456)
P = W @ X took: 166.22440767288208 seconds! Shape: (8456, 30000)
Process Group PGID: 2095
</untrusted_context>{
"command": "python3 -c '\nimport numpy as np\nimport scipy.sparse as sp\nimport time\n\nn_users = 8456\nn_items = 30000\nnnz = 8000000\nrows = np.random.randint(0, n_users, size=nnz, dtype=np.int32)\ncols = np.random.randint(0, n_items, size=nnz, dtype=np.int32)\ndata = np.random.rand(nnz).astype(np.float32)\n\nX = sp.csr_matrix((data, (rows, cols)), shape=(n_users, n_items), dtype=np.float32)\n\n# Simulate top-100 neighbors per user\nk = 100\nw_rows = np.repeat(np.arange(n_users, dtype=np.int32), k)\nw_cols = np.random.randint(0, n_users, size=n_users * k, dtype=np.int32)\nw_data = np.random.rand(n_users * k).astype(np.float32)\n\nW_topk = sp.csr_matrix((w_data, (w_rows, w_cols)), shape=(n_users, n_users), dtype=np.float32)\n\nt0 = time.time()\nP = W_topk @ X\nprint(f\"W_topk @ X took: {time.time() - t0:.2f}s! Result shape: {P.shape}, nnz: {P.nnz}\")\n'",
"description": "Benchmark sparse top-k user CF"
}<untrusted_context>
Output: W_topk @ X took: 6.16s! Result shape: (8456, 30000), nnz: 242649503
Process Group PGID: 2112
</untrusted_context>{
"command": "python3 -c '\nimport numpy as np\nimport scipy.sparse as sp\nfrom sklearn.decomposition import TruncatedSVD\nimport time\n\nn_users = 8456\nn_items = 30000\nnnz = 8000000\nrows = np.random.randint(0, n_users, size=nnz, dtype=np.int32)\ncols = np.random.randint(0, n_items, size=nnz, dtype=np.int32)\ndata = np.random.rand(nnz).astype(np.float32)\n\nX = sp.csr_matrix((data, (rows, cols)), shape=(n_users, n_items), dtype=np.float32)\n\nfor dim in [64, 128]:\n t0 = time.time()\n svd = TruncatedSVD(n_components=dim, random_state=42)\n U = svd.fit_transform(X) # (8456, dim)\n V = svd.components_ # (dim, 30000)\n print(f\"TruncatedSVD(dim={dim}) fit in {time.time()-t0:.2f}s\")\n t0 = time.time()\n # Compute dot product for top-100 items per user\n # In batches of users:\n preds = U[:1000] @ V\n print(f\"Batch 1000 users dot product took: {time.time()-t0:.4f}s\")\n'",
"description": "Benchmark TruncatedSVD and batch dot products"
}<untrusted_context>
Output: TruncatedSVD(dim=64) fit in 2.87s
Batch 1000 users dot product took: 0.0178s
TruncatedSVD(dim=128) fit in 4.35s
Batch 1000 users dot product took: 0.0278s
Process Group PGID: 2129
</untrusted_context>Analyzing Model Performance
I've successfully fit the TruncatedSVD model in under 5 seconds. Batch dot product calculations are also proving efficient, coming in at a fraction of a second for all users. Now, I'm shifting focus to compare Item-Item CF with the current user-user and SVD approaches, with an eye towards discovery capabilities.
{
"description": "Analyze validation ground truth items and their popularity ranks",
"command": "python3 -c '\nimport sys\nsys.path.append(\"/task\")\nimport pandas as pd\nimport numpy as np\nfrom pathlib import Path\nfrom evaluate_public import validation_truth\nfrom ranking_evaluation import CandidateSet\n\nDATA = Path(\"/task/data\")\ntargets_frame = pd.read_parquet(DATA / \"target_users.parquet\")\ntargets = set(targets_frame.uid.astype(int))\neligibility = CandidateSet(DATA / \"validation_eligible_candidates.npz\")\ngraded = validation_truth(eligibility, targets)\n\n# graded is uid -> {item_id: grade}\nprint(f\"Num users with graded items: {len(graded)}\")\ntotal_grade3 = sum(sum(1 for g in items.values() if g == 3) for items in graded.values())\ntotal_grade1 = sum(sum(1 for g in items.values() if g == 1) for items in graded.values())\nprint(f\"Total grade 3: {total_grade3}, total grade 1: {total_grade1}\")\n\n# Check popularity rank of graded items\ncatalog = pd.read_parquet(DATA / \"candidate_catalog.parquet\").set_index(\"item_id\")\nranks_grade3 = [catalog.loc[item, \"popularity_rank\"] for items in graded.values() for item, g in items.items() if g == 3 and item in catalog.index]\nranks_grade1 = [catalog.loc[item, \"popularity_rank\"] for items in graded.values() for item, g in items.items() if g == 1 and item in catalog.index]\n\nprint(f\"Grade 3 items popularity rank: mean {np.mean(ranks_grade3):.1f}, median {np.median(ranks_grade3):.1f}, p25 {np.percentile(ranks_grade3, 25):.1f}, p75 {np.percentile(ranks_grade3, 75):.1f}\")\nprint(f\"Grade 1 items popularity rank: mean {np.mean(ranks_grade1):.1f}, median {np.median(ranks_grade1):.1f}, p25 {np.percentile(ranks_grade1, 25):.1f}, p75 {np.percentile(ranks_grade1, 75):.1f}\")\n'"
}<untrusted_context>
Output: Num users with graded items: 4263
Total grade 3: 4417, total grade 1: 33352
Grade 3 items popularity rank: mean 8892.0, median 6371.0, p25 2107.0, p75 14181.0
Grade 1 items popularity rank: mean 8406.3, median 5545.0, p25 1693.8, p75 13274.0
Process Group PGID: 2147
</untrusted_context>{
"strategic_intent": "Build matrix factorization (SVD) prototype on validation set and measure NDCG@10.",
"summary": "Analyzed ground truth distribution: relevant items are widely distributed across catalog popularity (median rank ~6000), confirming that pure popularity ranking misses most user discoveries. Designing and implementing a TruncatedSVD matrix factorization model on user-item interaction matrix, evaluating with official validation metrics.",
"title": "Matrix Factorization Prototype and Validation"
}Current topic: "Matrix Factorization Prototype and Validation"
Topic summary: Analyzed ground truth distribution: relevant items are widely distributed across catalog popularity (median rank ~6000), confirming that pure popularity ranking misses most user discoveries. Designing and implementing a TruncatedSVD matrix factorization model on user-item interaction matrix, evaluating with official validation metrics.
Strategic Intent: Build matrix factorization (SVD) prototype on validation set and measure NDCG@10.Designing Interaction Matrix
I'm currently focusing on constructing the user-item interaction matrix. My immediate plan involves defining the features that will populate each (user, item) cell, considering users $u \in \{0, \dots, 8455\}$ and items $i \in \{0, \dots, 2\}$.
{
"description": "Prototype TruncatedSVD collaborative filtering and evaluate on validation",
"command": "python3 -c '\nimport sys\nsys.path.append(\"/task\")\nimport time\nimport numpy as np\nimport pandas as pd\nimport scipy.sparse as sp\nimport pyarrow.parquet as pq\nfrom pathlib import Path\nfrom sklearn.decomposition import TruncatedSVD\nfrom evaluate_public import validation_truth\nfrom ranking_evaluation import CandidateSet, ranked_items, score_rankings\n\nDATA = Path(\"/task/data\")\nt0 = time.time()\n\n# 1. Target users and catalog items\ntargets_df = pd.read_parquet(DATA / \"target_users.parquet\")\nuids = targets_df.uid.values\nuid_to_idx = {uid: idx for idx, uid in enumerate(uids)}\nn_users = len(uids)\n\ncatalog_df = pd.read_parquet(DATA / \"candidate_catalog.parquet\")\nitem_ids = catalog_df.item_id.values\nitem_to_idx = {item: idx for idx, item in enumerate(item_ids)}\nn_items = len(item_ids)\n\neligibility = CandidateSet(DATA / \"validation_eligible_candidates.npz\")\ngraded = validation_truth(eligibility, set(uids))\n\n# 2. Load listens\nlistens_table = pq.read_table(DATA / \"train_listens.parquet\", columns=[\"uid\", \"item_id\", \"timestamp\", \"is_organic\", \"played_ratio_pct\"])\nprint(f\"Loaded train_listens in {time.time()-t0:.2f}s\")\n\nl_uid = listens_table[\"uid\"].to_numpy()\nl_item = listens_table[\"item_id\"].to_numpy()\nl_ts = listens_table[\"timestamp\"].to_numpy()\nl_org = listens_table[\"is_organic\"].to_numpy()\nl_ratio = listens_table[\"played_ratio_pct\"].to_numpy()\n\n# Filter only target users and catalog items\nmask = np.isin(l_uid, uids) & np.isin(l_item, item_ids)\nprint(f\"Masked {mask.sum()} / {len(mask)} listens in {time.time()-t0:.2f}s\")\n\nu_idx = np.array([uid_to_idx[u] for u in l_uid[mask]], dtype=np.int32)\ni_idx = np.array([item_to_idx[i] for i in l_item[mask]], dtype=np.int32)\n\n# Weight calculation:\n# listens with played_ratio_pct >= 80 get 2.0, >= 50 get 1.0, < 20 get 0.1\n# organic gets 1.5x boost\n# time decay: exp(-days_ago / 60)\nt_max = 24786800.0\ndays_ago = (t_max - l_ts[mask]) / 86400.0\ntime_weight = np.exp(-days_ago / 60.0)\n\nlisten_weight = np.where(l_ratio[mask] >= 80, 2.0, np.where(l_ratio[mask] >= 50, 1.0, 0.2))\norg_weight = np.where(l_org[mask] == 1, 1.5, 1.0)\nweights = listen_weight * org_weight * time_weight\n\n# Build sparse matrix\nX = sp.csr_matrix((weights, (u_idx, i_idx)), shape=(n_users, n_items), dtype=np.float32)\nprint(f\"Built sparse user-item matrix in {time.time()-t0:.2f}s, nnz={X.nnz}\")\n\n# Add likes from train_likes and pref state\nlikes_df = pd.read_parquet(DATA / \"train_likes.parquet\")\nlikes_df = likes_df[likes_df.uid.isin(uids) & likes_df.item_id.isin(item_ids)]\nl_u = [uid_to_idx[u] for u in likes_df.uid]\nl_i = [item_to_idx[i] for i in likes_df.item_id]\nlikes_days = (t_max - likes_df.timestamp.values) / 86400.0\nl_w = 5.0 * np.exp(-likes_days / 60.0)\n\nX_likes = sp.csr_matrix((l_w, (l_u, l_i)), shape=(n_users, n_items), dtype=np.float32)\nX = X + X_likes\n\n# Preference state likes\npref_df = pd.read_parquet(DATA / \"validation_preference_state.parquet\")\npref_likes = pref_df[(pref_df.state == \"liked\") & pref_df.uid.isin(uids) & pref_df.item_id.isin(item_ids)]\np_u = [uid_to_idx[u] for u in pref_likes.uid]\np_i = [item_to_idx[i] for i in pref_likes.item_id]\np_w = np.full(len(p_u), 5.0, dtype=np.float32)\nX = X + sp.csr_matrix((p_w, (p_u, p_i)), shape=(n_users, n_items), dtype=np.float32)\n\nprint(f\"Total matrix with likes: nnz={X.nnz}\")\n\n# Apply IDF weighting\n# idf = log(1 + n_users / (1 + df))\ndf = np.bincount(X.nonzero()[1], minlength=n_items)\nidf = np.log1p(n_users / (1.0 + df)).astype(np.float32)\nX_idf = X.multiply(idf).tocsr()\n\n# L2 normalize user rows\nuser_norms = sp.linalg.norm(X_idf, axis=1)\nuser_norms[user_norms == 0] = 1.0\nX_norm = sp.diags(1.0 / user_norms.ravel()) @ X_idf\n\n# Run TruncatedSVD\ndim = 128\nprint(f\"Fitting TruncatedSVD(dim={dim})...\")\nsvd = TruncatedSVD(n_components=dim, random_state=42)\nU = svd.fit_transform(X_norm) # (8456, dim)\nV = svd.components_ # (dim, 30000)\nprint(f\"TruncatedSVD fitted in {time.time()-t0:.2f}s\")\n\n# Evaluate ranking\nval_elig = np.load(DATA / \"validation_eligible_candidates.npz\")\npacked = val_elig[\"packed_eligible\"]\n\nrows = []\n# Batch score prediction\nbatch_size = 500\nfor start in range(0, n_users, batch_size):\n end = min(start + batch_size, n_users)\n scores_batch = U[start:end] @ V # shape (batch, 30000)\n for b_idx in range(end - start):\n u_idx_curr = start + b_idx\n uid = uids[u_idx_curr]\n mask = np.unpackbits(packed[u_idx_curr], count=n_items, bitorder=\"little\")\n # Set ineligible scores to -inf\n user_scores = scores_batch[b_idx]\n user_scores[mask == 0] = -np.inf\n # Top 100 items: argpartition\n top100_idx = np.argpartition(user_scores, -100)[-100:]\n top100_idx = top100_idx[np.argsort(-user_scores[top100_idx])]\n top_items = item_ids[top100_idx]\n for rank_idx, item in enumerate(top_items, 1):\n rows.append((uid, item, rank_idx))\n\ndf_sub = pd.DataFrame(rows, columns=[\"uid\", \"item_id\", \"rank\"])\nranked = ranked_items(df_sub)\nscores = score_rankings(ranked, graded)\nprint(f\"SVD (dim={dim}) NDCG@10 score: {scores} in {time.time()-t0:.2f}s\")\n'"
}<untrusted_context>
Output: Loaded train_listens in 0.94s
Masked 31062540 / 43484159 listens in 1.65s
Built sparse user-item matrix in 11.66s, nnz=7868664
Total matrix with likes: nnz=7876748
Fitting TruncatedSVD(dim=128)...
TruncatedSVD fitted in 16.63s
SVD (dim=128) NDCG@10 score: {'quality_ndcg@10': 0.030842, 'quality_users': 4263} in 20.47s
Process Group PGID: 2167
</untrusted_context>Improving Recommendation Performance
I've just achieved a nearly threefold improvement on the popularity baseline by implementing TruncatedSVD with 128 dimensions, which took only 20 seconds. My next step is to analyze the contributing factors to this success and explore additional methods to further elevate the recommendation score.
{
"command": "python3 -c '\nimport sys\nsys.path.append(\"/task\")\nimport time\nimport numpy as np\nimport pandas as pd\nimport scipy.sparse as sp\nimport pyarrow.parquet as pq\nfrom pathlib import Path\nfrom sklearn.decomposition import TruncatedSVD\nfrom evaluate_public import validation_truth\nfrom ranking_evaluation import CandidateSet, ranked_items, score_rankings\n\nDATA = Path(\"/task/data\")\ntargets_df = pd.read_parquet(DATA / \"target_users.parquet\")\nuids = targets_df.uid.values\nuid_to_idx = {uid: idx for idx, uid in enumerate(uids)}\nn_users = len(uids)\n\ncatalog_df = pd.read_parquet(DATA / \"candidate_catalog.parquet\")\nitem_ids = catalog_df.item_id.values\nitem_to_idx = {item: idx for idx, item in enumerate(item_ids)}\nn_items = len(item_ids)\n\neligibility = CandidateSet(DATA / \"validation_eligible_candidates.npz\")\ngraded = validation_truth(eligibility, set(uids))\n\n# 1. Build matrix\nlistens_table = pq.read_table(DATA / \"train_listens.parquet\", columns=[\"uid\", \"item_id\", \"timestamp\", \"is_organic\", \"played_ratio_pct\"])\nl_uid = listens_table[\"uid\"].to_numpy()\nl_item = listens_table[\"item_id\"].to_numpy()\nl_ts = listens_table[\"timestamp\"].to_numpy()\nl_org = listens_table[\"is_organic\"].to_numpy()\nl_ratio = listens_table[\"played_ratio_pct\"].to_numpy()\n\nmask = np.isin(l_uid, uids) & np.isin(l_item, item_ids)\nu_idx = np.array([uid_to_idx[u] for u in l_uid[mask]], dtype=np.int32)\ni_idx = np.array([item_to_idx[i] for i in l_item[mask]], dtype=np.int32)\n\nt_max = 24786800.0\ndays_ago = (t_max - l_ts[mask]) / 86400.0\ntime_weight = np.exp(-days_ago / 60.0)\nlisten_weight = np.where(l_ratio[mask] >= 80, 2.0, np.where(l_ratio[mask] >= 50, 1.0, 0.2))\norg_weight = np.where(l_org[mask] == 1, 1.5, 1.0)\nweights = listen_weight * org_weight * time_weight\n\nX = sp.csr_matrix((weights, (u_idx, i_idx)), shape=(n_users, n_items), dtype=np.float32)\n\n# Likes\nlikes_df = pd.read_parquet(DATA / \"train_likes.parquet\")\nlikes_df = likes_df[likes_df.uid.isin(uids) & likes_df.item_id.isin(item_ids)]\nl_u = [uid_to_idx[u] for u in likes_df.uid]\nl_i = [item_to_idx[i] for i in likes_df.item_id]\nlikes_days = (t_max - likes_df.timestamp.values) / 86400.0\nl_w = 5.0 * np.exp(-likes_days / 60.0)\nX = X + sp.csr_matrix((l_w, (l_u, l_i)), shape=(n_users, n_items), dtype=np.float32)\n\npref_df = pd.read_parquet(DATA / \"validation_preference_state.parquet\")\npref_likes = pref_df[(pref_df.state == \"liked\") & pref_df.uid.isin(uids) & pref_df.item_id.isin(item_ids)]\np_u = [uid_to_idx[u] for u in pref_likes.uid]\np_i = [item_to_idx[i] for i in pref_likes.item_id]\nX = X + sp.csr_matrix((np.full(len(p_u), 5.0, dtype=np.float32), (p_u, p_i)), shape=(n_users, n_items), dtype=np.float32)\n\n# IDF\ndf = np.bincount(X.nonzero()[1], minlength=n_items)\nidf = np.log1p(n_users / (1.0 + df)).astype(np.float32)\nX_idf = X.multiply(idf).tocsr()\n\nuser_norms = sp.linalg.norm(X_idf, axis=1)\nuser_norms[user_norms == 0] = 1.0\nX_norm = sp.diags(1.0 / user_norms.ravel()) @ X_idf\n\n# Log popularity of catalog items\npop = np.log1p(catalog_df.positive_listens.values.astype(np.float32))\npop_norm = pop / pop.max()\n\nval_elig = np.load(DATA / \"validation_eligible_candidates.npz\")\npacked = val_elig[\"packed_eligible\"]\n\nfor dim in [128, 256]:\n svd = TruncatedSVD(n_components=dim, random_state=42)\n U = svd.fit_transform(X_norm)\n V = svd.components_\n\n for alpha in [0.0, 0.05, 0.1, 0.2]:\n rows = []\n batch_size = 1000\n for start in range(0, n_users, batch_size):\n end = min(start + batch_size, n_users)\n scores_batch = U[start:end] @ V\n # Min-max normalize per row before adding popularity\n row_mins = scores_batch.min(axis=1, keepdims=True)\n row_maxs = scores_batch.max(axis=1, keepdims=True)\n row_ptp = np.where(row_maxs - row_mins > 1e-6, row_maxs - row_mins, 1.0)\n scores_norm = (scores_batch - row_mins) / row_ptp\n if alpha > 0:\n scores_norm = scores_norm + alpha * pop_norm\n\n for b_idx in range(end - start):\n u_idx_curr = start + b_idx\n uid = uids[u_idx_curr]\n mask = np.unpackbits(packed[u_idx_curr], count=n_items, bitorder=\"little\")\n user_scores = scores_norm[b_idx]\n user_scores[mask == 0] = -np.inf\n top100_idx = np.argpartition(user_scores, -100)[-100:]\n top100_idx = top100_idx[np.argsort(-user_scores[top100_idx])]\n top_items = item_ids[top100_idx]\n for rank_idx, item in enumerate(top_items, 1):\n rows.append((uid, item, rank_idx))\n\n df_sub = pd.DataFrame(rows, columns=[\"uid\", \"item_id\", \"rank\"])\n ranked = ranked_items(df_sub)\n scores = score_rankings(ranked, graded)\n print(f\"dim={dim}, alpha={alpha} -> NDCG@10: {scores[\\\"quality_ndcg@10\\\"]}\")\n'",
"description": "Experiment with SVD dimensions and popularity weighting"
}<untrusted_context>
Output: File "<string>", line 114
print(f"dim={dim}, alpha={alpha} -> NDCG@10: {scores[\"quality_ndcg@10\"]}")
^
SyntaxError: unexpected character after line continuation character
Exit Code: 1
Process Group PGID: 2196
</untrusted_context>{
"description": "Run SVD dimension and popularity grid search",
"command": "python3 -c '\nimport sys\nsys.path.append(\"/task\")\nimport time\nimport numpy as np\nimport pandas as pd\nimport scipy.sparse as sp\nimport pyarrow.parquet as pq\nfrom pathlib import Path\nfrom sklearn.decomposition import TruncatedSVD\nfrom evaluate_public import validation_truth\nfrom ranking_evaluation import CandidateSet, ranked_items, score_rankings\n\nDATA = Path(\"/task/data\")\ntargets_df = pd.read_parquet(DATA / \"target_users.parquet\")\nuids = targets_df.uid.values\nuid_to_idx = {uid: idx for idx, uid in enumerate(uids)}\nn_users = len(uids)\n\ncatalog_df = pd.read_parquet(DATA / \"candidate_catalog.parquet\")\nitem_ids = catalog_df.item_id.values\nitem_to_idx = {item: idx for idx, item in enumerate(item_ids)}\nn_items = len(item_ids)\n\neligibility = CandidateSet(DATA / \"validation_eligible_candidates.npz\")\ngraded = validation_truth(eligibility, set(uids))\n\n# 1. Build matrix\nlistens_table = pq.read_table(DATA / \"train_listens.parquet\", columns=[\"uid\", \"item_id\", \"timestamp\", \"is_organic\", \"played_ratio_pct\"])\nl_uid = listens_table[\"uid\"].to_numpy()\nl_item = listens_table[\"item_id\"].to_numpy()\nl_ts = listens_table[\"timestamp\"].to_numpy()\nl_org = listens_table[\"is_organic\"].to_numpy()\nl_ratio = listens_table[\"played_ratio_pct\"].to_numpy()\n\nmask = np.isin(l_uid, uids) & np.isin(l_item, item_ids)\nu_idx = np.array([uid_to_idx[u] for u in l_uid[mask]], dtype=np.int32)\ni_idx = np.array([item_to_idx[i] for i in l_item[mask]], dtype=np.int32)\n\nt_max = 24786800.0\ndays_ago = (t_max - l_ts[mask]) / 86400.0\ntime_weight = np.exp(-days_ago / 60.0)\nlisten_weight = np.where(l_ratio[mask] >= 80, 2.0, np.where(l_ratio[mask] >= 50, 1.0, 0.2))\norg_weight = np.where(l_org[mask] == 1, 1.5, 1.0)\nweights = listen_weight * org_weight * time_weight\n\nX = sp.csr_matrix((weights, (u_idx, i_idx)), shape=(n_users, n_items), dtype=np.float32)\n\n# Likes\nlikes_df = pd.read_parquet(DATA / \"train_likes.parquet\")\nlikes_df = likes_df[likes_df.uid.isin(uids) & likes_df.item_id.isin(item_ids)]\nl_u = [uid_to_idx[u] for u in likes_df.uid]\nl_i = [item_to_idx[i] for i in likes_df.item_id]\nlikes_days = (t_max - likes_df.timestamp.values) / 86400.0\nl_w = 5.0 * np.exp(-likes_days / 60.0)\nX = X + sp.csr_matrix((l_w, (l_u, l_i)), shape=(n_users, n_items), dtype=np.float32)\n\npref_df = pd.read_parquet(DATA / \"validation_preference_state.parquet\")\npref_likes = pref_df[(pref_df.state == \"liked\") & pref_df.uid.isin(uids) & pref_df.item_id.isin(item_ids)]\np_u = [uid_to_idx[u] for u in pref_likes.uid]\np_i = [item_to_idx[i] for i in pref_likes.item_id]\nX = X + sp.csr_matrix((np.full(len(p_u), 5.0, dtype=np.float32), (p_u, p_i)), shape=(n_users, n_items), dtype=np.float32)\n\n# IDF\ndf = np.bincount(X.nonzero()[1], minlength=n_items)\nidf = np.log1p(n_users / (1.0 + df)).astype(np.float32)\nX_idf = X.multiply(idf).tocsr()\n\nuser_norms = sp.linalg.norm(X_idf, axis=1)\nuser_norms[user_norms == 0] = 1.0\nX_norm = sp.diags(1.0 / user_norms.ravel()) @ X_idf\n\n# Log popularity of catalog items\npop = np.log1p(catalog_df.positive_listens.values.astype(np.float32))\npop_norm = pop / pop.max()\n\nval_elig = np.load(DATA / \"validation_eligible_candidates.npz\")\npacked = val_elig[\"packed_eligible\"]\n\nfor dim in [128, 256]:\n svd = TruncatedSVD(n_components=dim, random_state=42)\n U = svd.fit_transform(X_norm)\n V = svd.components_\n\n for alpha in [0.0, 0.05, 0.1, 0.2]:\n rows = []\n batch_size = 1000\n for start in range(0, n_users, batch_size):\n end = min(start + batch_size, n_users)\n scores_batch = U[start:end] @ V\n row_mins = scores_batch.min(axis=1, keepdims=True)\n row_maxs = scores_batch.max(axis=1, keepdims=True)\n row_ptp = np.where(row_maxs - row_mins > 1e-6, row_maxs - row_mins, 1.0)\n scores_norm = (scores_batch - row_mins) / row_ptp\n if alpha > 0:\n scores_norm = scores_norm + alpha * pop_norm\n\n for b_idx in range(end - start):\n u_idx_curr = start + b_idx\n uid = uids[u_idx_curr]\n mask = np.unpackbits(packed[u_idx_curr], count=n_items, bitorder=\"little\")\n user_scores = scores_norm[b_idx]\n user_scores[mask == 0] = -np.inf\n top100_idx = np.argpartition(user_scores, -100)[-100:]\n top100_idx = top100_idx[np.argsort(-user_scores[top100_idx])]\n top_items = item_ids[top100_idx]\n for rank_idx, item in enumerate(top_items, 1):\n rows.append((uid, item, rank_idx))\n\n df_sub = pd.DataFrame(rows, columns=[\"uid\", \"item_id\", \"rank\"])\n ranked = ranked_items(df_sub)\n scores = score_rankings(ranked, graded)\n score_val = scores[\"quality_ndcg@10\"]\n print(f\"dim={dim}, alpha={alpha} -> NDCG@10: {score_val}\")\n'"
}<untrusted_context>
Output: dim=128, alpha=0.0 -> NDCG@10: 0.030842
dim=128, alpha=0.05 -> NDCG@10: 0.03095
dim=128, alpha=0.1 -> NDCG@10: 0.030598
dim=128, alpha=0.2 -> NDCG@10: 0.030346
dim=256, alpha=0.0 -> NDCG@10: 0.029693
dim=256, alpha=0.05 -> NDCG@10: 0.029324
dim=256, alpha=0.1 -> NDCG@10: 0.029219
dim=256, alpha=0.2 -> NDCG@10: 0.028426
Process Group PGID: 2199
</untrusted_context>Exploring Collaborative Filtering
I'm currently delving into collaborative filtering approaches. My recent exploration with dim=128 yielded a score of 0.03095, and now I'm transitioning to investigate User-User k-NN CF and Item-Item CF techniques. My immediate focus is on calculating user similarity for a target user to identify the most akin users.
{
"description": "Test User-User k-NN Collaborative Filtering",
"command": "python3 -c '\nimport sys\nsys.path.append(\"/task\")\nimport time\nimport numpy as np\nimport pandas as pd\nimport scipy.sparse as sp\nimport pyarrow.parquet as pq\nfrom pathlib import Path\nfrom evaluate_public import validation_truth\nfrom ranking_evaluation import CandidateSet, ranked_items, score_rankings\n\nDATA = Path(\"/task/data\")\ntargets_df = pd.read_parquet(DATA / \"target_users.parquet\")\nuids = targets_df.uid.values\nuid_to_idx = {uid: idx for idx, uid in enumerate(uids)}\nn_users = len(uids)\n\ncatalog_df = pd.read_parquet(DATA / \"candidate_catalog.parquet\")\nitem_ids = catalog_df.item_id.values\nitem_to_idx = {item: idx for idx, item in enumerate(item_ids)}\nn_items = len(item_ids)\n\neligibility = CandidateSet(DATA / \"validation_eligible_candidates.npz\")\ngraded = validation_truth(eligibility, set(uids))\n\n# 1. Build matrix\nlistens_table = pq.read_table(DATA / \"train_listens.parquet\", columns=[\"uid\", \"item_id\", \"timestamp\", \"is_organic\", \"played_ratio_pct\"])\nl_uid = listens_table[\"uid\"].to_numpy()\nl_item = listens_table[\"item_id\"].to_numpy()\nl_ts = listens_table[\"timestamp\"].to_numpy()\nl_org = listens_table[\"is_organic\"].to_numpy()\nl_ratio = listens_table[\"played_ratio_pct\"].to_numpy()\n\nmask = np.isin(l_uid, uids) & np.isin(l_item, item_ids)\nu_idx = np.array([uid_to_idx[u] for u in l_uid[mask]], dtype=np.int32)\ni_idx = np.array([item_to_idx[i] for i in l_item[mask]], dtype=np.int32)\n\nt_max = 24786800.0\ndays_ago = (t_max - l_ts[mask]) / 86400.0\ntime_weight = np.exp(-days_ago / 60.0)\nlisten_weight = np.where(l_ratio[mask] >= 80, 2.0, np.where(l_ratio[mask] >= 50, 1.0, 0.2))\norg_weight = np.where(l_org[mask] == 1, 1.5, 1.0)\nweights = listen_weight * org_weight * time_weight\n\nX = sp.csr_matrix((weights, (u_idx, i_idx)), shape=(n_users, n_items), dtype=np.float32)\n\n# Likes\nlikes_df = pd.read_parquet(DATA / \"train_likes.parquet\")\nlikes_df = likes_df[likes_df.uid.isin(uids) & likes_df.item_id.isin(item_ids)]\nl_u = [uid_to_idx[u] for u in likes_df.uid]\nl_i = [item_to_idx[i] for i in likes_df.item_id]\nlikes_days = (t_max - likes_df.timestamp.values) / 86400.0\nl_w = 5.0 * np.exp(-likes_days / 60.0)\nX = X + sp.csr_matrix((l_w, (l_u, l_i)), shape=(n_users, n_items), dtype=np.float32)\n\npref_df = pd.read_parquet(DATA / \"validation_preference_state.parquet\")\npref_likes = pref_df[(pref_df.state == \"liked\") & pref_df.uid.isin(uids) & pref_df.item_id.isin(item_ids)]\np_u = [uid_to_idx[u] for u in pref_likes.uid]\np_i = [item_to_idx[i] for i in pref_likes.item_id]\nX = X + sp.csr_matrix((np.full(len(p_u), 5.0, dtype=np.float32), (p_u, p_i)), shape=(n_users, n_items), dtype=np.float32)\n\n# IDF\ndf = np.bincount(X.nonzero()[1], minlength=n_items)\nidf = np.log1p(n_users / (1.0 + df)).astype(np.float32)\nX_idf = X.multiply(idf).tocsr()\n\nuser_norms = sp.linalg.norm(X_idf, axis=1)\nuser_norms[user_norms == 0] = 1.0\nX_norm = sp.diags(1.0 / user_norms.ravel()) @ X_idf\n\nval_elig = np.load(DATA / \"validation_eligible_candidates.npz\")\npacked = val_elig[\"packed_eligible\"]\n\n# Compute User-User similarity in batches to find top-K neighbors\nt0 = time.time()\nprint(\"Computing User-User similarity...\")\n# We can do this in user batches:\nfor K in [50, 100, 200]:\n t_start = time.time()\n rows = []\n batch_size = 500\n for b_start in range(0, n_users, batch_size):\n b_end = min(b_start + batch_size, n_users)\n # Cosine similarity between batch and all users: (batch, n_users)\n sim_batch = (X_norm[b_start:b_end] @ X_norm.T).toarray()\n # Set self-similarity to 0\n for i in range(b_end - b_start):\n sim_batch[i, b_start + i] = 0.0\n\n # Find top K neighbors\n topk_idx = np.argpartition(sim_batch, -K, axis=1)[:, -K:]\n # Gather top K weights\n # Build batch sparse matrix of weights\n row_indices = np.repeat(np.arange(b_end - b_start), K)\n col_indices = topk_idx.ravel()\n weights_k = sim_batch[row_indices, col_indices]\n # Optional: zero out negative or very small weights\n weights_k[weights_k < 0] = 0\n\n # Sparse batch W_k: (batch, n_users)\n W_batch = sp.csr_matrix((weights_k, (row_indices, col_indices)), shape=(b_end - b_start, n_users), dtype=np.float32)\n\n # Predict scores: W_batch @ X_idf\n scores_batch = (W_batch @ X_idf).toarray()\n\n for i in range(b_end - b_start):\n u_curr = b_start + i\n uid = uids[u_curr]\n mask = np.unpackbits(packed[u_curr], count=n_items, bitorder=\"little\")\n u_scores = scores_batch[i]\n u_scores[mask == 0] = -np.inf\n top100 = np.argpartition(u_scores, -100)[-100:]\n top100 = top100[np.argsort(-u_scores[top100])]\n top_items = item_ids[top100]\n for rank_idx, item in enumerate(top_items, 1):\n rows.append((uid, item, rank_idx))\n\n df_sub = pd.DataFrame(rows, columns=[\"uid\", \"item_id\", \"rank\"])\n ranked = ranked_items(df_sub)\n scores = score_rankings(ranked, graded)\n score_val = scores[\"quality_ndcg@10\"]\n print(f\"User-User k-NN (K={K}) -> NDCG@10: {score_val} in {time.time()-t_start:.1f}s\")\n'"
}<untrusted_context>
Output: Computing User-User similarity...
User-User k-NN (K=50) -> NDCG@10: 0.02183 in 18.4s
User-User k-NN (K=100) -> NDCG@10: 0.02683 in 19.9s
User-User k-NN (K=200) -> NDCG@10: 0.029547 in 22.6s
Process Group PGID: 2228
</untrusted_context>{
"description": "Evaluate ensemble of SVD + User-User kNN",
"command": "python3 -c '\nimport sys\nsys.path.append(\"/task\")\nimport time\nimport numpy as np\nimport pandas as pd\nimport scipy.sparse as sp\nimport pyarrow.parquet as pq\nfrom pathlib import Path\nfrom sklearn.decomposition import TruncatedSVD\nfrom evaluate_public import validation_truth\nfrom ranking_evaluation import CandidateSet, ranked_items, score_rankings\n\nDATA = Path(\"/task/data\")\ntargets_df = pd.read_parquet(DATA / \"target_users.parquet\")\nuids = targets_df.uid.values\nuid_to_idx = {uid: idx for idx, uid in enumerate(uids)}\nn_users = len(uids)\n\ncatalog_df = pd.read_parquet(DATA / \"candidate_catalog.parquet\")\nitem_ids = catalog_df.item_id.values\nitem_to_idx = {item: idx for idx, item in enumerate(item_ids)}\nn_items = len(item_ids)\n\neligibility = CandidateSet(DATA / \"validation_eligible_candidates.npz\")\ngraded = validation_truth(eligibility, set(uids))\n\n# 1. Build matrix\nlistens_table = pq.read_table(DATA / \"train_listens.parquet\", columns=[\"uid\", \"item_id\", \"timestamp\", \"is_organic\", \"played_ratio_pct\"])\nl_uid = listens_table[\"uid\"].to_numpy()\nl_item = listens_table[\"item_id\"].to_numpy()\nl_ts = listens_table[\"timestamp\"].to_numpy()\nl_org = listens_table[\"is_organic\"].to_numpy()\nl_ratio = listens_table[\"played_ratio_pct\"].to_numpy()\n\nmask = np.isin(l_uid, uids) & np.isin(l_item, item_ids)\nu_idx = np.array([uid_to_idx[u] for u in l_uid[mask]], dtype=np.int32)\ni_idx = np.array([item_to_idx[i] for i in l_item[mask]], dtype=np.int32)\n\nt_max = 24786800.0\ndays_ago = (t_max - l_ts[mask]) / 86400.0\ntime_weight = np.exp(-days_ago / 60.0)\nlisten_weight = np.where(l_ratio[mask] >= 80, 2.0, np.where(l_ratio[mask] >= 50, 1.0, 0.2))\norg_weight = np.where(l_org[mask] == 1, 1.5, 1.0)\nweights = listen_weight * org_weight * time_weight\n\nX = sp.csr_matrix((weights, (u_idx, i_idx)), shape=(n_users, n_items), dtype=np.float32)\n\nlikes_df = pd.read_parquet(DATA / \"train_likes.parquet\")\nlikes_df = likes_df[likes_df.uid.isin(uids) & likes_df.item_id.isin(item_ids)]\nl_u = [uid_to_idx[u] for u in likes_df.uid]\nl_i = [item_to_idx[i] for i in likes_df.item_id]\nlikes_days = (t_max - likes_df.timestamp.values) / 86400.0\nl_w = 5.0 * np.exp(-likes_days / 60.0)\nX = X + sp.csr_matrix((l_w, (l_u, l_i)), shape=(n_users, n_items), dtype=np.float32)\n\npref_df = pd.read_parquet(DATA / \"validation_preference_state.parquet\")\npref_likes = pref_df[(pref_df.state == \"liked\") & pref_df.uid.isin(uids) & pref_df.item_id.isin(item_ids)]\np_u = [uid_to_idx[u] for u in pref_likes.uid]\np_i = [item_to_idx[i] for i in pref_likes.item_id]\nX = X + sp.csr_matrix((np.full(len(p_u), 5.0, dtype=np.float32), (p_u, p_i)), shape=(n_users, n_items), dtype=np.float32)\n\ndf = np.bincount(X.nonzero()[1], minlength=n_items)\nidf = np.log1p(n_users / (1.0 + df)).astype(np.float32)\nX_idf = X.multiply(idf).tocsr()\n\nuser_norms = sp.linalg.norm(X_idf, axis=1)\nuser_norms[user_norms == 0] = 1.0\nX_norm = sp.diags(1.0 / user_norms.ravel()) @ X_idf\n\n# SVD\nsvd = TruncatedSVD(n_components=128, random_state=42)\nU = svd.fit_transform(X_norm)\nV = svd.components_\n\nval_elig = np.load(DATA / \"validation_eligible_candidates.npz\")\npacked = val_elig[\"packed_eligible\"]\n\nK = 300\nfor w_knn in [0.0, 0.2, 0.4, 0.6, 0.8]:\n rows = []\n batch_size = 500\n for b_start in range(0, n_users, batch_size):\n b_end = min(b_start + batch_size, n_users)\n b_len = b_end - b_start\n\n # SVD score\n s_svd = U[b_start:b_end] @ V\n s_svd_min = s_svd.min(axis=1, keepdims=True)\n s_svd_max = s_svd.max(axis=1, keepdims=True)\n s_svd = (s_svd - s_svd_min) / (s_svd_max - s_svd_min + 1e-6)\n\n if w_knn > 0:\n sim_batch = (X_norm[b_start:b_end] @ X_norm.T).toarray()\n for i in range(b_len):\n sim_batch[i, b_start + i] = 0.0\n topk_idx = np.argpartition(sim_batch, -K, axis=1)[:, -K:]\n row_idx = np.repeat(np.arange(b_len), K)\n col_idx = topk_idx.ravel()\n w_k = sim_batch[row_idx, col_idx]\n w_k[w_k < 0] = 0\n W_batch = sp.csr_matrix((w_k, (row_idx, col_idx)), shape=(b_len, n_users), dtype=np.float32)\n s_knn = (W_batch @ X_idf).toarray()\n s_knn_min = s_knn.min(axis=1, keepdims=True)\n s_knn_max = s_knn.max(axis=1, keepdims=True)\n s_knn = (s_knn - s_knn_min) / (s_knn_max - s_knn_min + 1e-6)\n s_final = (1.0 - w_knn) * s_svd + w_knn * s_knn\n else:\n s_final = s_svd\n\n for i in range(b_len):\n u_curr = b_start + i\n uid = uids[u_curr]\n mask = np.unpackbits(packed[u_curr], count=n_items, bitorder=\"little\")\n u_scores = s_final[i]\n u_scores[mask == 0] = -np.inf\n top100 = np.argpartition(u_scores, -100)[-100:]\n top100 = top100[np.argsort(-u_scores[top100])]\n top_items = item_ids[top100]\n for rank_idx, item in enumerate(top_items, 1):\n rows.append((uid, item, rank_idx))\n\n df_sub = pd.DataFrame(rows, columns=[\"uid\", \"item_id\", \"rank\"])\n ranked = ranked_items(df_sub)\n scores = score_rankings(ranked, graded)\n score_val = scores[\"quality_ndcg@10\"]\n print(f\"w_knn={w_knn} -> NDCG@10: {score_val}\")\n'"
}<untrusted_context>
Output: w_knn=0.0 -> NDCG@10: 0.030842
w_knn=0.2 -> NDCG@10: 0.03214
w_knn=0.4 -> NDCG@10: 0.033015
w_knn=0.6 -> NDCG@10: 0.032672
w_knn=0.8 -> NDCG@10: 0.032124
Process Group PGID: 2257
</untrusted_context>Improving NDCG Score
My latest exploration into signal weighting, specifically at w_knn=0.4, yielded a significant improvement in NDCG@10, boosting it to 0.033015. I'm now shifting focus to investigate the efficacy of Item-Item Collaborative Filtering and its potential synergy with User-User CF.
Exploring Item-Item Similarity
I'm now diving into Item-Item Collaborative Filtering. My current thought process is centered on efficiently computing item similarity, potentially leveraging matrix operations like $Y^T Y$ where $Y$ is column-normalized IDF data. I'm also considering the calculation $(X Y^T) Y$ for scoring.
{
"command": "python3 -c '\nimport numpy as np\nimport time\n\nA = np.random.randn(8456, 8456).astype(np.float32)\n# test on a block of 30000 cols, say 5000 at a time\nB = np.random.randn(8456, 5000).astype(np.float32)\n\nt0 = time.time()\nC = A @ B\nprint(f\"Dense (8456, 8456) @ (8456, 5000) took {time.time()-t0:.2f}s\")\n'",
"description": "Benchmark BLAS dense matrix multiplication"
}<untrusted_context>
Output: Dense (8456, 8456) @ (8456, 5000) took 3.31s
Process Group PGID: 2286
</untrusted_context>Calculating User-User Similarity
I've just confirmed the computational feasibility of generating the User-User similarity matrix. At this rate, processing all 30,000 columns should only take around 20 seconds, which is excellent news. Now I need to clarify the definition of $A$.
Refining Similarity Calculations
I've confirmed that block-wise dense matrix multiplication significantly accelerates computation by leveraging multithreaded OpenBLAS. I am now investigating whether limiting neighbor count (k-NN) or applying power transforms to similarity weights yields superior recommendation results compared to using the entire similarity matrix.
{
"command": "python3 -c '\nimport sys\nsys.path.append(\"/task\")\nimport time\nimport numpy as np\nimport pandas as pd\nimport scipy.sparse as sp\nimport pyarrow.parquet as pq\nfrom pathlib import Path\nfrom evaluate_public import validation_truth\nfrom ranking_evaluation import CandidateSet, ranked_items, score_rankings\n\nDATA = Path(\"/task/data\")\ntargets_df = pd.read_parquet(DATA / \"target_users.parquet\")\nuids = targets_df.uid.values\nuid_to_idx = {uid: idx for idx, uid in enumerate(uids)}\nn_users = len(uids)\n\ncatalog_df = pd.read_parquet(DATA / \"candidate_catalog.parquet\")\nitem_ids = catalog_df.item_id.values\nitem_to_idx = {item: idx for idx, item in enumerate(item_ids)}\nn_items = len(item_ids)\n\neligibility = CandidateSet(DATA / \"validation_eligible_candidates.npz\")\ngraded = validation_truth(eligibility, set(uids))\n\n# 1. Build matrix\nlistens_table = pq.read_table(DATA / \"train_listens.parquet\", columns=[\"uid\", \"item_id\", \"timestamp\", \"is_organic\", \"played_ratio_pct\"])\nl_uid = listens_table[\"uid\"].to_numpy()\nl_item = listens_table[\"item_id\"].to_numpy()\nl_ts = listens_table[\"timestamp\"].to_numpy()\nl_org = listens_table[\"is_organic\"].to_numpy()\nl_ratio = listens_table[\"played_ratio_pct\"].to_numpy()\n\nmask = np.isin(l_uid, uids) & np.isin(l_item, item_ids)\nu_idx = np.array([uid_to_idx[u] for u in l_uid[mask]], dtype=np.int32)\ni_idx = np.array([item_to_idx[i] for i in l_item[mask]], dtype=np.int32)\n\nt_max = 24786800.0\ndays_ago = (t_max - l_ts[mask]) / 86400.0\ntime_weight = np.exp(-days_ago / 60.0)\nlisten_weight = np.where(l_ratio[mask] >= 80, 2.0, np.where(l_ratio[mask] >= 50, 1.0, 0.2))\norg_weight = np.where(l_org[mask] == 1, 1.5, 1.0)\nweights = listen_weight * org_weight * time_weight\n\nX = sp.csr_matrix((weights, (u_idx, i_idx)), shape=(n_users, n_items), dtype=np.float32)\n\nlikes_df = pd.read_parquet(DATA / \"train_likes.parquet\")\nlikes_df = likes_df[likes_df.uid.isin(uids) & likes_df.item_id.isin(item_ids)]\nl_u = [uid_to_idx[u] for u in likes_df.uid]\nl_i = [item_to_idx[i] for i in likes_df.item_id]\nlikes_days = (t_max - likes_df.timestamp.values) / 86400.0\nl_w = 5.0 * np.exp(-likes_days / 60.0)\nX = X + sp.csr_matrix((l_w, (l_u, l_i)), shape=(n_users, n_items), dtype=np.float32)\n\npref_df = pd.read_parquet(DATA / \"validation_preference_state.parquet\")\npref_likes = pref_df[(pref_df.state == \"liked\") & pref_df.uid.isin(uids) & pref_df.item_id.isin(item_ids)]\np_u = [uid_to_idx[u] for u in pref_likes.uid]\np_i = [item_to_idx[i] for i in pref_likes.item_id]\nX = X + sp.csr_matrix((np.full(len(p_u), 5.0, dtype=np.float32), (p_u, p_i)), shape=(n_users, n_items), dtype=np.float32)\n\ndf = np.bincount(X.nonzero()[1], minlength=n_items)\nidf = np.log1p(n_users / (1.0 + df)).astype(np.float32)\nX_idf = X.multiply(idf).tocsr()\n\nuser_norms = sp.linalg.norm(X_idf, axis=1)\nuser_norms[user_norms == 0] = 1.0\nX_norm = sp.diags(1.0 / user_norms.ravel()) @ X_idf\n\nval_elig = np.load(DATA / \"validation_eligible_candidates.npz\")\npacked = val_elig[\"packed_eligible\"]\n\nfor K in [200, 300, 500]:\n for power in [1.0, 2.0]:\n rows = []\n batch_size = 500\n for b_start in range(0, n_users, batch_size):\n b_end = min(b_start + batch_size, n_users)\n b_len = b_end - b_start\n sim_batch = (X_norm[b_start:b_end] @ X_norm.T).toarray()\n for i in range(b_len):\n sim_batch[i, b_start + i] = 0.0\n topk_idx = np.argpartition(sim_batch, -K, axis=1)[:, -K:]\n row_idx = np.repeat(np.arange(b_len), K)\n col_idx = topk_idx.ravel()\n w_k = sim_batch[row_idx, col_idx]\n w_k[w_k < 0] = 0\n if power != 1.0:\n w_k = w_k ** power\n W_batch = sp.csr_matrix((w_k, (row_idx, col_idx)), shape=(b_len, n_users), dtype=np.float32)\n scores_batch = (W_batch @ X_idf).toarray()\n\n for i in range(b_len):\n u_curr = b_start + i\n uid = uids[u_curr]\n mask = np.unpackbits(packed[u_curr], count=n_items, bitorder=\"little\")\n u_scores = scores_batch[i]\n u_scores[mask == 0] = -np.inf\n top100 = np.argpartition(u_scores, -100)[-100:]\n top100 = top100[np.argsort(-u_scores[top100])]\n top_items = item_ids[top100]\n for rank_idx, item in enumerate(top_items, 1):\n rows.append((uid, item, rank_idx))\n\n df_sub = pd.DataFrame(rows, columns=[\"uid\", \"item_id\", \"rank\"])\n ranked = ranked_items(df_sub)\n scores = score_rankings(ranked, graded)\n score_val = scores[\"quality_ndcg@10\"]\n print(f\"K={K}, power={power} -> NDCG@10: {score_val}\")\n'",
"description": "Test KNN K and similarity power"
}<untrusted_context>
Output: K=200, power=1.0 -> NDCG@10: 0.029547
K=200, power=2.0 -> NDCG@10: 0.026768
K=300, power=1.0 -> NDCG@10: 0.030823
K=300, power=2.0 -> NDCG@10: 0.027552
K=500, power=1.0 -> NDCG@10: 0.032421
K=500, power=2.0 -> NDCG@10: 0.0295
Process Group PGID: 2296
</untrusted_context>Exploring K-Value Effects
I'm currently investigating the impact of varying the K value, specifically testing K=1000 and K=1500 to see how they influence the output compared to K=500. Additionally, I'm exploring the implications of not truncating K at all, or setting the similarity threshold to a minimum of 0, by analyzing results after setting sim_batch[sim_batch < 0] = 0.
{
"command": "python3 -c '\nimport sys\nsys.path.append(\"/task\")\nimport time\nimport numpy as np\nimport pandas as pd\nimport scipy.sparse as sp\nimport pyarrow.parquet as pq\nfrom pathlib import Path\nfrom evaluate_public import validation_truth\nfrom ranking_evaluation import CandidateSet, ranked_items, score_rankings\n\nDATA = Path(\"/task/data\")\ntargets_df = pd.read_parquet(DATA / \"target_users.parquet\")\nuids = targets_df.uid.values\nuid_to_idx = {uid: idx for idx, uid in enumerate(uids)}\nn_users = len(uids)\n\ncatalog_df = pd.read_parquet(DATA / \"candidate_catalog.parquet\")\nitem_ids = catalog_df.item_id.values\nitem_to_idx = {item: idx for idx, item in enumerate(item_ids)}\nn_items = len(item_ids)\n\neligibility = CandidateSet(DATA / \"validation_eligible_candidates.npz\")\ngraded = validation_truth(eligibility, set(uids))\n\n# 1. Build matrix\nlistens_table = pq.read_table(DATA / \"train_listens.parquet\", columns=[\"uid\", \"item_id\", \"timestamp\", \"is_organic\", \"played_ratio_pct\"])\nl_uid = listens_table[\"uid\"].to_numpy()\nl_item = listens_table[\"item_id\"].to_numpy()\nl_ts = listens_table[\"timestamp\"].to_numpy()\nl_org = listens_table[\"is_organic\"].to_numpy()\nl_ratio = listens_table[\"played_ratio_pct\"].to_numpy()\n\nmask = np.isin(l_uid, uids) & np.isin(l_item, item_ids)\nu_idx = np.array([uid_to_idx[u] for u in l_uid[mask]], dtype=np.int32)\ni_idx = np.array([item_to_idx[i] for i in l_item[mask]], dtype=np.int32)\n\nt_max = 24786800.0\ndays_ago = (t_max - l_ts[mask]) / 86400.0\ntime_weight = np.exp(-days_ago / 60.0)\nlisten_weight = np.where(l_ratio[mask] >= 80, 2.0, np.where(l_ratio[mask] >= 50, 1.0, 0.2))\norg_weight = np.where(l_org[mask] == 1, 1.5, 1.0)\nweights = listen_weight * org_weight * time_weight\n\nX = sp.csr_matrix((weights, (u_idx, i_idx)), shape=(n_users, n_items), dtype=np.float32)\n\nlikes_df = pd.read_parquet(DATA / \"train_likes.parquet\")\nlikes_df = likes_df[likes_df.uid.isin(uids) & likes_df.item_id.isin(item_ids)]\nl_u = [uid_to_idx[u] for u in likes_df.uid]\nl_i = [item_to_idx[i] for i in likes_df.item_id]\nlikes_days = (t_max - likes_df.timestamp.values) / 86400.0\nl_w = 5.0 * np.exp(-likes_days / 60.0)\nX = X + sp.csr_matrix((l_w, (l_u, l_i)), shape=(n_users, n_items), dtype=np.float32)\n\npref_df = pd.read_parquet(DATA / \"validation_preference_state.parquet\")\npref_likes = pref_df[(pref_df.state == \"liked\") & pref_df.uid.isin(uids) & pref_df.item_id.isin(item_ids)]\np_u = [uid_to_idx[u] for u in pref_likes.uid]\np_i = [item_to_idx[i] for i in pref_likes.item_id]\nX = X + sp.csr_matrix((np.full(len(p_u), 5.0, dtype=np.float32), (p_u, p_i)), shape=(n_users, n_items), dtype=np.float32)\n\ndf = np.bincount(X.nonzero()[1], minlength=n_items)\nidf = np.log1p(n_users / (1.0 + df)).astype(np.float32)\nX_idf = X.multiply(idf).tocsr()\n\nuser_norms = sp.linalg.norm(X_idf, axis=1)\nuser_norms[user_norms == 0] = 1.0\nX_norm = sp.diags(1.0 / user_norms.ravel()) @ X_idf\n\nval_elig = np.load(DATA / \"validation_eligible_candidates.npz\")\npacked = val_elig[\"packed_eligible\"]\n\nfor K in [800, 1200]:\n rows = []\n batch_size = 500\n for b_start in range(0, n_users, batch_size):\n b_end = min(b_start + batch_size, n_users)\n b_len = b_end - b_start\n sim_batch = (X_norm[b_start:b_end] @ X_norm.T).toarray()\n for i in range(b_len):\n sim_batch[i, b_start + i] = 0.0\n topk_idx = np.argpartition(sim_batch, -K, axis=1)[:, -K:]\n row_idx = np.repeat(np.arange(b_len), K)\n col_idx = topk_idx.ravel()\n w_k = sim_batch[row_idx, col_idx]\n w_k[w_k < 0] = 0\n W_batch = sp.csr_matrix((w_k, (row_idx, col_idx)), shape=(b_len, n_users), dtype=np.float32)\n scores_batch = (W_batch @ X_idf).toarray()\n\n for i in range(b_len):\n u_curr = b_start + i\n uid = uids[u_curr]\n mask = np.unpackbits(packed[u_curr], count=n_items, bitorder=\"little\")\n u_scores = scores_batch[i]\n u_scores[mask == 0] = -np.inf\n top100 = np.argpartition(u_scores, -100)[-100:]\n top100 = top100[np.argsort(-u_scores[top100])]\n top_items = item_ids[top100]\n for rank_idx, item in enumerate(top_items, 1):\n rows.append((uid, item, rank_idx))\n\n df_sub = pd.DataFrame(rows, columns=[\"uid\", \"item_id\", \"rank\"])\n ranked = ranked_items(df_sub)\n scores = score_rankings(ranked, graded)\n score_val = scores[\"quality_ndcg@10\"]\n print(f\"K={K} -> NDCG@10: {score_val}\")\n'",
"description": "Test K=800 and K=1200 User-User CF"
}<untrusted_context>
Output: K=800 -> NDCG@10: 0.032383
K=1200 -> NDCG@10: 0.032886
Process Group PGID: 2325
</untrusted_context>Evaluating Combinations
My recent focus has been on exploring hybrid recommendation approaches. I'm observing promising results when combining User-User similarities with Singular Value Decomposition, particularly with higher K values for User-User and varying dimensionality for SVD. I'm keen to push these combinations further.
Investigating Item-Item
I'm now examining Item-Item Collaborative Filtering. I've mapped out the process, realizing item embeddings can be derived from the transposed normalized user-item matrix, enabling direct calculation of item-item similarities and, subsequently, recommendation scores using a concise matrix operation.
{
"command": "python3 -c '\nimport sys\nsys.path.append(\"/task\")\nimport time\nimport numpy as np\nimport pandas as pd\nimport scipy.sparse as sp\nimport pyarrow.parquet as pq\nfrom pathlib import Path\nfrom evaluate_public import validation_truth\nfrom ranking_evaluation import CandidateSet, ranked_items, score_rankings\n\nDATA = Path(\"/task/data\")\ntargets_df = pd.read_parquet(DATA / \"target_users.parquet\")\nuids = targets_df.uid.values\nuid_to_idx = {uid: idx for idx, uid in enumerate(uids)}\nn_users = len(uids)\n\ncatalog_df = pd.read_parquet(DATA / \"candidate_catalog.parquet\")\nitem_ids = catalog_df.item_id.values\nitem_to_idx = {item: idx for idx, item in enumerate(item_ids)}\nn_items = len(item_ids)\n\neligibility = CandidateSet(DATA / \"validation_eligible_candidates.npz\")\ngraded = validation_truth(eligibility, set(uids))\n\n# 1. Build matrix\nlistens_table = pq.read_table(DATA / \"train_listens.parquet\", columns=[\"uid\", \"item_id\", \"timestamp\", \"is_organic\", \"played_ratio_pct\"])\nl_uid = listens_table[\"uid\"].to_numpy()\nl_item = listens_table[\"item_id\"].to_numpy()\nl_ts = listens_table[\"timestamp\"].to_numpy()\nl_org = listens_table[\"is_organic\"].to_numpy()\nl_ratio = listens_table[\"played_ratio_pct\"].to_numpy()\n\nmask = np.isin(l_uid, uids) & np.isin(l_item, item_ids)\nu_idx = np.array([uid_to_idx[u] for u in l_uid[mask]], dtype=np.int32)\ni_idx = np.array([item_to_idx[i] for i in l_item[mask]], dtype=np.int32)\n\nt_max = 24786800.0\ndays_ago = (t_max - l_ts[mask]) / 86400.0\ntime_weight = np.exp(-days_ago / 60.0)\nlisten_weight = np.where(l_ratio[mask] >= 80, 2.0, np.where(l_ratio[mask] >= 50, 1.0, 0.2))\norg_weight = np.where(l_org[mask] == 1, 1.5, 1.0)\nweights = listen_weight * org_weight * time_weight\n\nX = sp.csr_matrix((weights, (u_idx, i_idx)), shape=(n_users, n_items), dtype=np.float32)\n\nlikes_df = pd.read_parquet(DATA / \"train_likes.parquet\")\nlikes_df = likes_df[likes_df.uid.isin(uids) & likes_df.item_id.isin(item_ids)]\nl_u = [uid_to_idx[u] for u in likes_df.uid]\nl_i = [item_to_idx[i] for i in likes_df.item_id]\nlikes_days = (t_max - likes_df.timestamp.values) / 86400.0\nl_w = 5.0 * np.exp(-likes_days / 60.0)\nX = X + sp.csr_matrix((l_w, (l_u, l_i)), shape=(n_users, n_items), dtype=np.float32)\n\npref_df = pd.read_parquet(DATA / \"validation_preference_state.parquet\")\npref_likes = pref_df[(pref_df.state == \"liked\") & pref_df.uid.isin(uids) & pref_df.item_id.isin(item_ids)]\np_u = [uid_to_idx[u] for u in pref_likes.uid]\np_i = [item_to_idx[i] for i in pref_likes.item_id]\nX = X + sp.csr_matrix((np.full(len(p_u), 5.0, dtype=np.float32), (p_u, p_i)), shape=(n_users, n_items), dtype=np.float32)\n\ndf = np.bincount(X.nonzero()[1], minlength=n_items)\nidf = np.log1p(n_users / (1.0 + df)).astype(np.float32)\nX_idf = X.multiply(idf).tocsr()\n\n# Column normalize X_idf (items)\n# norm of each column\nitem_norms = sp.linalg.norm(X_idf, axis=0)\nitem_norms[item_norms == 0] = 1.0\nY = X_idf.multiply(1.0 / item_norms).tocsr() # (8456, 30000)\n\nval_elig = np.load(DATA / \"validation_eligible_candidates.npz\")\npacked = val_elig[\"packed_eligible\"]\n\n# Let us normalize user rows of X_idf\nuser_norms = sp.linalg.norm(X_idf, axis=1)\nuser_norms[user_norms == 0] = 1.0\nX_unorm = sp.diags(1.0 / user_norms.ravel()) @ X_idf\n\nt0 = time.time()\nprint(\"Computing E = X_unorm @ Y.T...\")\nE = (X_unorm @ Y.T).toarray() # (8456, 8456)\nprint(f\"E computed in {time.time()-t0:.2f}s\")\n\n# Now score in batches of users:\nrows = []\nbatch_size = 1000\nfor b_start in range(0, n_users, batch_size):\n b_end = min(b_start + batch_size, n_users)\n b_len = b_end - b_start\n # scores: E[b_start:b_end] @ Y -> shape (b_len, 30000)\n scores_batch = E[b_start:b_end] @ Y # dense @ sparse is supported or convert Y block\n for i in range(b_len):\n u_curr = b_start + i\n uid = uids[u_curr]\n mask = np.unpackbits(packed[u_curr], count=n_items, bitorder=\"little\")\n u_scores = scores_batch[i]\n u_scores[mask == 0] = -np.inf\n top100 = np.argpartition(u_scores, -100)[-100:]\n top100 = top100[np.argsort(-u_scores[top100])]\n top_items = item_ids[top100]\n for rank_idx, item in enumerate(top_items, 1):\n rows.append((uid, item, rank_idx))\n\ndf_sub = pd.DataFrame(rows, columns=[\"uid\", \"item_id\", \"rank\"])\nranked = ranked_items(df_sub)\nscores = score_rankings(ranked, graded)\nscore_val = scores[\"quality_ndcg@10\"]\nprint(f\"Item-Item Cosine CF -> NDCG@10: {score_val} in {time.time()-t0:.2f}s\")\n'",
"description": "Test Item-Item Cosine Collaborative Filtering"
}<untrusted_context>
Output: Computing E = X_unorm @ Y.T...
E computed in 11.40s
Item-Item Cosine CF -> NDCG@10: 0.017004 in 31.74s
Process Group PGID: 2354
</untrusted_context>Exploring Performance Differences
My current thoughts are focused on understanding the significant performance disparity observed. I'm analyzing why SVD and User-User k-NN are outperforming the full dense Item-Item approach, suspecting SVD's orthogonal dense components are key to capturing nuanced latent factors like genres and moods.
Optimizing SVD Parameters
I'm now delving into the finer points of SVD optimization. Specifically, I'm investigating the impact of various weighting schemes for interactions, including time decay half-lives, listen completion, organic vs. recommendation boosts, and differential weighting for likes and dislikes. I'm also considering how matrix preprocessing choices might influence these outcomes.
{
"command": "python3 -c '\nimport sys\nsys.path.append(\"/task\")\nimport time\nimport numpy as np\nimport pandas as pd\nimport scipy.sparse as sp\nimport pyarrow.parquet as pq\nfrom pathlib import Path\nfrom sklearn.decomposition import TruncatedSVD\nfrom evaluate_public import validation_truth\nfrom ranking_evaluation import CandidateSet, ranked_items, score_rankings\n\nDATA = Path(\"/task/data\")\ntargets_df = pd.read_parquet(DATA / \"target_users.parquet\")\nuids = targets_df.uid.values\nuid_to_idx = {uid: idx for idx, uid in enumerate(uids)}\nn_users = len(uids)\n\ncatalog_df = pd.read_parquet(DATA / \"candidate_catalog.parquet\")\nitem_ids = catalog_df.item_id.values\nitem_to_idx = {item: idx for idx, item in enumerate(item_ids)}\nn_items = len(item_ids)\n\neligibility = CandidateSet(DATA / \"validation_eligible_candidates.npz\")\ngraded = validation_truth(eligibility, set(uids))\n\nval_elig = np.load(DATA / \"validation_eligible_candidates.npz\")\npacked = val_elig[\"packed_eligible\"]\n\n# Load tables once\nlistens_table = pq.read_table(DATA / \"train_listens.parquet\", columns=[\"uid\", \"item_id\", \"timestamp\", \"is_organic\", \"played_ratio_pct\"])\nl_uid = listens_table[\"uid\"].to_numpy()\nl_item = listens_table[\"item_id\"].to_numpy()\nl_ts = listens_table[\"timestamp\"].to_numpy()\nl_org = listens_table[\"is_organic\"].to_numpy()\nl_ratio = listens_table[\"played_ratio_pct\"].to_numpy()\n\nmask = np.isin(l_uid, uids) & np.isin(l_item, item_ids)\nu_idx = np.array([uid_to_idx[u] for u in l_uid[mask]], dtype=np.int32)\ni_idx = np.array([item_to_idx[i] for i in l_item[mask]], dtype=np.int32)\nl_ts_sub = l_ts[mask]\nl_org_sub = l_org[mask]\nl_ratio_sub = l_ratio[mask]\n\nlikes_df = pd.read_parquet(DATA / \"train_likes.parquet\")\nlikes_df = likes_df[likes_df.uid.isin(uids) & likes_df.item_id.isin(item_ids)]\nl_u = np.array([uid_to_idx[u] for u in likes_df.uid], dtype=np.int32)\nl_i = np.array([item_to_idx[i] for i in likes_df.item_id], dtype=np.int32)\nlikes_ts = likes_df.timestamp.values\n\npref_df = pd.read_parquet(DATA / \"validation_preference_state.parquet\")\npref_likes = pref_df[(pref_df.state == \"liked\") & pref_df.uid.isin(uids) & pref_df.item_id.isin(item_ids)]\np_u = np.array([uid_to_idx[u] for u in pref_likes.uid], dtype=np.int32)\np_i = np.array([item_to_idx[i] for i in pref_likes.item_id], dtype=np.int32)\n\nt_max = 24786800.0\n\ndef evaluate_config(decay_days=60.0, tf_mode=\"log\", dim=128, organic_boost=2.0, like_weight=5.0):\n days_ago = (t_max - l_ts_sub) / 86400.0\n if decay_days is not None:\n time_weight = np.exp(-days_ago / decay_days)\n else:\n time_weight = 1.0\n\n listen_weight = np.where(l_ratio_sub >= 80, 2.5, np.where(l_ratio_sub >= 50, 1.0, 0.2))\n org_weight = np.where(l_org_sub == 1, organic_boost, 1.0)\n w_listens = listen_weight * org_weight * time_weight\n\n X = sp.csr_matrix((w_listens, (u_idx, i_idx)), shape=(n_users, n_items), dtype=np.float32)\n\n # Likes\n if decay_days is not None:\n likes_time = np.exp(-((t_max - likes_ts) / 86400.0) / decay_days)\n else:\n likes_time = 1.0\n w_likes = like_weight * likes_time\n X = X + sp.csr_matrix((w_likes, (l_u, l_i)), shape=(n_users, n_items), dtype=np.float32)\n X = X + sp.csr_matrix((np.full(len(p_u), like_weight, dtype=np.float32), (p_u, p_i)), shape=(n_users, n_items), dtype=np.float32)\n\n # TF transform on X.data\n if tf_mode == \"log\":\n X.data = np.log1p(X.data)\n elif tf_mode == \"sqrt\":\n X.data = np.sqrt(X.data)\n\n # IDF\n df = np.bincount(X.nonzero()[1], minlength=n_items)\n idf = np.log1p(n_users / (1.0 + df)).astype(np.float32)\n X_idf = X.multiply(idf).tocsr()\n\n # Normalize rows\n user_norms = sp.linalg.norm(X_idf, axis=1)\n user_norms[user_norms == 0] = 1.0\n X_norm = sp.diags(1.0 / user_norms.ravel()) @ X_idf\n\n svd = TruncatedSVD(n_components=dim, random_state=42)\n U = svd.fit_transform(X_norm)\n V = svd.components_\n\n rows = []\n batch_size = 1000\n for b_start in range(0, n_users, batch_size):\n b_end = min(b_start + batch_size, n_users)\n scores = U[b_start:b_end] @ V\n for i in range(b_end - b_start):\n u_curr = b_start + i\n uid = uids[u_curr]\n mask = np.unpackbits(packed[u_curr], count=n_items, bitorder=\"little\")\n u_scores = scores[i]\n u_scores[mask == 0] = -np.inf\n top100 = np.argpartition(u_scores, -100)[-100:]\n top100 = top100[np.argsort(-u_scores[top100])]\n top_items = item_ids[top100]\n for rank_idx, item in enumerate(top_items, 1):\n rows.append((uid, item, rank_idx))\n\n df_sub = pd.DataFrame(rows, columns=[\"uid\", \"item_id\", \"rank\"])\n ranked = ranked_items(df_sub)\n scores = score_rankings(ranked, graded)\n return scores[\"quality_ndcg@10\"]\n\nprint(\"decay=60, log, dim=128:\", evaluate_config(decay_days=60, tf_mode=\"log\", dim=128))\nprint(\"decay=60, sqrt, dim=128:\", evaluate_config(decay_days=60, tf_mode=\"sqrt\", dim=128))\nprint(\"decay=60, raw, dim=128:\", evaluate_config(decay_days=60, tf_mode=\"raw\", dim=128))\nprint(\"decay=30, log, dim=128:\", evaluate_config(decay_days=30, tf_mode=\"log\", dim=128))\nprint(\"decay=90, log, dim=128:\", evaluate_config(decay_days=90, tf_mode=\"log\", dim=128))\nprint(\"decay=60, log, dim=160:\", evaluate_config(decay_days=60, tf_mode=\"log\", dim=160))\n'",
"description": "Systematic parameter search for SVD collaborative filtering"
}<untrusted_context>
Output: decay=60, log, dim=128: 0.030127
decay=60, sqrt, dim=128: 0.030251
decay=60, raw, dim=128: 0.030643
decay=30, log, dim=128: 0.033705
decay=90, log, dim=128: 0.026958
decay=60, log, dim=160: 0.030901
Process Group PGID: 2383
</untrusted_context>Exploring Decay Parameter
I'm investigating the impact of the decay parameter on performance. Initial tests with decay=30 yield a value of 0.033705. I observed a trend where higher decay values seem to correlate with better results, with decay=90 achieving 0.026958. My next step is to explore decay values between 15 and 30 to refine this understanding, as recent listening trends strongly influence music discovery.
{
"description": "Fine-grained search on decay days",
"command": "python3 -c '\nimport sys\nsys.path.append(\"/task\")\nimport time\nimport numpy as np\nimport pandas as pd\nimport scipy.sparse as sp\nimport pyarrow.parquet as pq\nfrom pathlib import Path\nfrom sklearn.decomposition import TruncatedSVD\nfrom evaluate_public import validation_truth\nfrom ranking_evaluation import CandidateSet, ranked_items, score_rankings\n\nDATA = Path(\"/task/data\")\ntargets_df = pd.read_parquet(DATA / \"target_users.parquet\")\nuids = targets_df.uid.values\nuid_to_idx = {uid: idx for idx, uid in enumerate(uids)}\nn_users = len(uids)\n\ncatalog_df = pd.read_parquet(DATA / \"candidate_catalog.parquet\")\nitem_ids = catalog_df.item_id.values\nitem_to_idx = {item: idx for idx, item in enumerate(item_ids)}\nn_items = len(item_ids)\n\neligibility = CandidateSet(DATA / \"validation_eligible_candidates.npz\")\ngraded = validation_truth(eligibility, set(uids))\n\nval_elig = np.load(DATA / \"validation_eligible_candidates.npz\")\npacked = val_elig[\"packed_eligible\"]\n\n# Load tables once\nlistens_table = pq.read_table(DATA / \"train_listens.parquet\", columns=[\"uid\", \"item_id\", \"timestamp\", \"is_organic\", \"played_ratio_pct\"])\nl_uid = listens_table[\"uid\"].to_numpy()\nl_item = listens_table[\"item_id\"].to_numpy()\nl_ts = listens_table[\"timestamp\"].to_numpy()\nl_org = listens_table[\"is_organic\"].to_numpy()\nl_ratio = listens_table[\"played_ratio_pct\"].to_numpy()\n\nmask = np.isin(l_uid, uids) & np.isin(l_item, item_ids)\nu_idx = np.array([uid_to_idx[u] for u in l_uid[mask]], dtype=np.int32)\ni_idx = np.array([item_to_idx[i] for i in l_item[mask]], dtype=np.int32)\nl_ts_sub = l_ts[mask]\nl_org_sub = l_org[mask]\nl_ratio_sub = l_ratio[mask]\n\nlikes_df = pd.read_parquet(DATA / \"train_likes.parquet\")\nlikes_df = likes_df[likes_df.uid.isin(uids) & likes_df.item_id.isin(item_ids)]\nl_u = np.array([uid_to_idx[u] for u in likes_df.uid], dtype=np.int32)\nl_i = np.array([item_to_idx[i] for i in likes_df.item_id], dtype=np.int32)\nlikes_ts = likes_df.timestamp.values\n\npref_df = pd.read_parquet(DATA / \"validation_preference_state.parquet\")\npref_likes = pref_df[(pref_df.state == \"liked\") & pref_df.uid.isin(uids) & pref_df.item_id.isin(item_ids)]\np_u = np.array([uid_to_idx[u] for u in pref_likes.uid], dtype=np.int32)\np_i = np.array([item_to_idx[i] for i in pref_likes.item_id], dtype=np.int32)\n\nt_max = 24786800.0\n\ndef evaluate_config(decay_days=30.0, tf_mode=\"log\", dim=128, organic_boost=2.0, like_weight=5.0):\n days_ago = (t_max - l_ts_sub) / 86400.0\n time_weight = np.exp(-days_ago / decay_days)\n\n listen_weight = np.where(l_ratio_sub >= 80, 2.5, np.where(l_ratio_sub >= 50, 1.0, 0.2))\n org_weight = np.where(l_org_sub == 1, organic_boost, 1.0)\n w_listens = listen_weight * org_weight * time_weight\n\n X = sp.csr_matrix((w_listens, (u_idx, i_idx)), shape=(n_users, n_items), dtype=np.float32)\n\n likes_time = np.exp(-((t_max - likes_ts) / 86400.0) / decay_days)\n w_likes = like_weight * likes_time\n X = X + sp.csr_matrix((w_likes, (l_u, l_i)), shape=(n_users, n_items), dtype=np.float32)\n X = X + sp.csr_matrix((np.full(len(p_u), like_weight, dtype=np.float32), (p_u, p_i)), shape=(n_users, n_items), dtype=np.float32)\n\n if tf_mode == \"log\":\n X.data = np.log1p(X.data)\n\n df = np.bincount(X.nonzero()[1], minlength=n_items)\n idf = np.log1p(n_users / (1.0 + df)).astype(np.float32)\n X_idf = X.multiply(idf).tocsr()\n\n user_norms = sp.linalg.norm(X_idf, axis=1)\n user_norms[user_norms == 0] = 1.0\n X_norm = sp.diags(1.0 / user_norms.ravel()) @ X_idf\n\n svd = TruncatedSVD(n_components=dim, random_state=42)\n U = svd.fit_transform(X_norm)\n V = svd.components_\n\n rows = []\n batch_size = 1000\n for b_start in range(0, n_users, batch_size):\n b_end = min(b_start + batch_size, n_users)\n scores = U[b_start:b_end] @ V\n for i in range(b_end - b_start):\n u_curr = b_start + i\n uid = uids[u_curr]\n mask = np.unpackbits(packed[u_curr], count=n_items, bitorder=\"little\")\n u_scores = scores[i]\n u_scores[mask == 0] = -np.inf\n top100 = np.argpartition(u_scores, -100)[-100:]\n top100 = top100[np.argsort(-u_scores[top100])]\n top_items = item_ids[top100]\n for rank_idx, item in enumerate(top_items, 1):\n rows.append((uid, item, rank_idx))\n\n df_sub = pd.DataFrame(rows, columns=[\"uid\", \"item_id\", \"rank\"])\n ranked = ranked_items(df_sub)\n scores = score_rankings(ranked, graded)\n return scores[\"quality_ndcg@10\"]\n\nfor d in [10, 15, 20, 25, 30, 40]:\n print(f\"decay={d}: {evaluate_config(decay_days=d, dim=128)}\")\n'"
}<untrusted_context>
Output: decay=10: 0.038638
decay=15: 0.038085
decay=20: 0.036539
decay=25: 0.035227
decay=30: 0.033705
decay=40: 0.032595
Process Group PGID: 2412
</untrusted_context>{
"command": "python3 -c '\nimport sys\nsys.path.append(\"/task\")\nimport time\nimport numpy as np\nimport pandas as pd\nimport scipy.sparse as sp\nimport pyarrow.parquet as pq\nfrom pathlib import Path\nfrom sklearn.decomposition import TruncatedSVD\nfrom evaluate_public import validation_truth\nfrom ranking_evaluation import CandidateSet, ranked_items, score_rankings\n\nDATA = Path(\"/task/data\")\ntargets_df = pd.read_parquet(DATA / \"target_users.parquet\")\nuids = targets_df.uid.values\nuid_to_idx = {uid: idx for idx, uid in enumerate(uids)}\nn_users = len(uids)\n\ncatalog_df = pd.read_parquet(DATA / \"candidate_catalog.parquet\")\nitem_ids = catalog_df.item_id.values\nitem_to_idx = {item: idx for idx, item in enumerate(item_ids)}\nn_items = len(item_ids)\n\neligibility = CandidateSet(DATA / \"validation_eligible_candidates.npz\")\ngraded = validation_truth(eligibility, set(uids))\n\nval_elig = np.load(DATA / \"validation_eligible_candidates.npz\")\npacked = val_elig[\"packed_eligible\"]\n\n# Load tables once\nlistens_table = pq.read_table(DATA / \"train_listens.parquet\", columns=[\"uid\", \"item_id\", \"timestamp\", \"is_organic\", \"played_ratio_pct\"])\nl_uid = listens_table[\"uid\"].to_numpy()\nl_item = listens_table[\"item_id\"].to_numpy()\nl_ts = listens_table[\"timestamp\"].to_numpy()\nl_org = listens_table[\"is_organic\"].to_numpy()\nl_ratio = listens_table[\"played_ratio_pct\"].to_numpy()\n\nmask = np.isin(l_uid, uids) & np.isin(l_item, item_ids)\nu_idx = np.array([uid_to_idx[u] for u in l_uid[mask]], dtype=np.int32)\ni_idx = np.array([item_to_idx[i] for i in l_item[mask]], dtype=np.int32)\nl_ts_sub = l_ts[mask]\nl_org_sub = l_org[mask]\nl_ratio_sub = l_ratio[mask]\n\nlikes_df = pd.read_parquet(DATA / \"train_likes.parquet\")\nlikes_df = likes_df[likes_df.uid.isin(uids) & likes_df.item_id.isin(item_ids)]\nl_u = np.array([uid_to_idx[u] for u in likes_df.uid], dtype=np.int32)\nl_i = np.array([item_to_idx[i] for i in likes_df.item_id], dtype=np.int32)\nlikes_ts = likes_df.timestamp.values\n\npref_df = pd.read_parquet(DATA / \"validation_preference_state.parquet\")\npref_likes = pref_df[(pref_df.state == \"liked\") & pref_df.uid.isin(uids) & pref_df.item_id.isin(item_ids)]\np_u = np.array([uid_to_idx[u] for u in pref_likes.uid], dtype=np.int32)\np_i = np.array([item_to_idx[i] for i in pref_likes.item_id], dtype=np.int32)\n\nt_max = 24786800.0\n\ndef evaluate_config(decay_days=10.0, tf_mode=\"log\", dim=128, organic_boost=2.0, like_weight=5.0):\n days_ago = (t_max - l_ts_sub) / 86400.0\n time_weight = np.exp(-days_ago / decay_days)\n\n listen_weight = np.where(l_ratio_sub >= 80, 2.5, np.where(l_ratio_sub >= 50, 1.0, 0.2))\n org_weight = np.where(l_org_sub == 1, organic_boost, 1.0)\n w_listens = listen_weight * org_weight * time_weight\n\n X = sp.csr_matrix((w_listens, (u_idx, i_idx)), shape=(n_users, n_items), dtype=np.float32)\n\n likes_time = np.exp(-((t_max - likes_ts) / 86400.0) / decay_days)\n w_likes = like_weight * likes_time\n X = X + sp.csr_matrix((w_likes, (l_u, l_i)), shape=(n_users, n_items), dtype=np.float32)\n X = X + sp.csr_matrix((np.full(len(p_u), like_weight, dtype=np.float32), (p_u, p_i)), shape=(n_users, n_items), dtype=np.float32)\n\n if tf_mode == \"log\":\n X.data = np.log1p(X.data)\n\n df = np.bincount(X.nonzero()[1], minlength=n_items)\n idf = np.log1p(n_users / (1.0 + df)).astype(np.float32)\n X_idf = X.multiply(idf).tocsr()\n\n user_norms = sp.linalg.norm(X_idf, axis=1)\n user_norms[user_norms == 0] = 1.0\n X_norm = sp.diags(1.0 / user_norms.ravel()) @ X_idf\n\n svd = TruncatedSVD(n_components=dim, random_state=42)\n U = svd.fit_transform(X_norm)\n V = svd.components_\n\n rows = []\n batch_size = 1000\n for b_start in range(0, n_users, batch_size):\n b_end = min(b_start + batch_size, n_users)\n scores = U[b_start:b_end] @ V\n for i in range(b_end - b_start):\n u_curr = b_start + i\n uid = uids[u_curr]\n mask = np.unpackbits(packed[u_curr], count=n_items, bitorder=\"little\")\n u_scores = scores[i]\n u_scores[mask == 0] = -np.inf\n top100 = np.argpartition(u_scores, -100)[-100:]\n top100 = top100[np.argsort(-u_scores[top100])]\n top_items = item_ids[top100]\n for rank_idx, item in enumerate(top_items, 1):\n rows.append((uid, item, rank_idx))\n\n df_sub = pd.DataFrame(rows, columns=[\"uid\", \"item_id\", \"rank\"])\n ranked = ranked_items(df_sub)\n scores = score_rankings(ranked, graded)\n return scores[\"quality_ndcg@10\"]\n\nfor d in [3, 5, 7, 10, 12]:\n print(f\"decay={d}: {evaluate_config(decay_days=d, dim=128)}\")\n'",
"description": "Test very short decay days"
}<untrusted_context>
Output: decay=3: 0.035435
decay=5: 0.038131
decay=7: 0.038788
decay=10: 0.038638
decay=12: 0.038616
Process Group PGID: 2441
</untrusted_context>Refining Decay Parameters
I've pinpointed a decay rate of 7, corresponding to a 1-week half-life. This precisely aligns with the validation window duration. My current focus is on leveraging recent listening activity, specifically within the 7-14 day window, as it seems to offer the most potent indicator of a user's imminent discovery. I'm now proceeding to test this refined approach.
{
"description": "Test long-term base weight mixed with exponential decay",
"command": "python3 -c '\nimport sys\nsys.path.append(\"/task\")\nimport time\nimport numpy as np\nimport pandas as pd\nimport scipy.sparse as sp\nimport pyarrow.parquet as pq\nfrom pathlib import Path\nfrom sklearn.decomposition import TruncatedSVD\nfrom evaluate_public import validation_truth\nfrom ranking_evaluation import CandidateSet, ranked_items, score_rankings\n\nDATA = Path(\"/task/data\")\ntargets_df = pd.read_parquet(DATA / \"target_users.parquet\")\nuids = targets_df.uid.values\nuid_to_idx = {uid: idx for idx, uid in enumerate(uids)}\nn_users = len(uids)\n\ncatalog_df = pd.read_parquet(DATA / \"candidate_catalog.parquet\")\nitem_ids = catalog_df.item_id.values\nitem_to_idx = {item: idx for idx, item in enumerate(item_ids)}\nn_items = len(item_ids)\n\neligibility = CandidateSet(DATA / \"validation_eligible_candidates.npz\")\ngraded = validation_truth(eligibility, set(uids))\n\nval_elig = np.load(DATA / \"validation_eligible_candidates.npz\")\npacked = val_elig[\"packed_eligible\"]\n\nlistens_table = pq.read_table(DATA / \"train_listens.parquet\", columns=[\"uid\", \"item_id\", \"timestamp\", \"is_organic\", \"played_ratio_pct\"])\nl_uid = listens_table[\"uid\"].to_numpy()\nl_item = listens_table[\"item_id\"].to_numpy()\nl_ts = listens_table[\"timestamp\"].to_numpy()\nl_org = listens_table[\"is_organic\"].to_numpy()\nl_ratio = listens_table[\"played_ratio_pct\"].to_numpy()\n\nmask = np.isin(l_uid, uids) & np.isin(l_item, item_ids)\nu_idx = np.array([uid_to_idx[u] for u in l_uid[mask]], dtype=np.int32)\ni_idx = np.array([item_to_idx[i] for i in l_item[mask]], dtype=np.int32)\nl_ts_sub = l_ts[mask]\nl_org_sub = l_org[mask]\nl_ratio_sub = l_ratio[mask]\n\nlikes_df = pd.read_parquet(DATA / \"train_likes.parquet\")\nlikes_df = likes_df[likes_df.uid.isin(uids) & likes_df.item_id.isin(item_ids)]\nl_u = np.array([uid_to_idx[u] for u in likes_df.uid], dtype=np.int32)\nl_i = np.array([item_to_idx[i] for i in likes_df.item_id], dtype=np.int32)\nlikes_ts = likes_df.timestamp.values\n\npref_df = pd.read_parquet(DATA / \"validation_preference_state.parquet\")\npref_likes = pref_df[(pref_df.state == \"liked\") & pref_df.uid.isin(uids) & pref_df.item_id.isin(item_ids)]\np_u = np.array([uid_to_idx[u] for u in pref_likes.uid], dtype=np.int32)\np_i = np.array([item_to_idx[i] for i in pref_likes.item_id], dtype=np.int32)\n\nt_max = 24786800.0\n\ndef test_time_mix(base_weight=0.1, decay_days=7.0, dim=128):\n days_ago = (t_max - l_ts_sub) / 86400.0\n time_weight = base_weight + (1.0 - base_weight) * np.exp(-days_ago / decay_days)\n\n listen_weight = np.where(l_ratio_sub >= 80, 2.5, np.where(l_ratio_sub >= 50, 1.0, 0.2))\n org_weight = np.where(l_org_sub == 1, 2.0, 1.0)\n w_listens = listen_weight * org_weight * time_weight\n\n X = sp.csr_matrix((w_listens, (u_idx, i_idx)), shape=(n_users, n_items), dtype=np.float32)\n\n likes_time = base_weight + (1.0 - base_weight) * np.exp(-((t_max - likes_ts) / 86400.0) / decay_days)\n w_likes = 5.0 * likes_time\n X = X + sp.csr_matrix((w_likes, (l_u, l_i)), shape=(n_users, n_items), dtype=np.float32)\n X = X + sp.csr_matrix((np.full(len(p_u), 5.0, dtype=np.float32), (p_u, p_i)), shape=(n_users, n_items), dtype=np.float32)\n\n X.data = np.log1p(X.data)\n\n df = np.bincount(X.nonzero()[1], minlength=n_items)\n idf = np.log1p(n_users / (1.0 + df)).astype(np.float32)\n X_idf = X.multiply(idf).tocsr()\n\n user_norms = sp.linalg.norm(X_idf, axis=1)\n user_norms[user_norms == 0] = 1.0\n X_norm = sp.diags(1.0 / user_norms.ravel()) @ X_idf\n\n svd = TruncatedSVD(n_components=dim, random_state=42)\n U = svd.fit_transform(X_norm)\n V = svd.components_\n\n rows = []\n batch_size = 1000\n for b_start in range(0, n_users, batch_size):\n b_end = min(b_start + batch_size, n_users)\n scores = U[b_start:b_end] @ V\n for i in range(b_end - b_start):\n u_curr = b_start + i\n uid = uids[u_curr]\n mask = np.unpackbits(packed[u_curr], count=n_items, bitorder=\"little\")\n u_scores = scores[i]\n u_scores[mask == 0] = -np.inf\n top100 = np.argpartition(u_scores, -100)[-100:]\n top100 = top100[np.argsort(-u_scores[top100])]\n top_items = item_ids[top100]\n for rank_idx, item in enumerate(top_items, 1):\n rows.append((uid, item, rank_idx))\n\n df_sub = pd.DataFrame(rows, columns=[\"uid\", \"item_id\", \"rank\"])\n ranked = ranked_items(df_sub)\n scores = score_rankings(ranked, graded)\n return scores[\"quality_ndcg@10\"]\n\nfor base in [0.0, 0.05, 0.1, 0.2, 0.3]:\n print(f\"base={base}: {test_time_mix(base_weight=base, decay_days=7.0)}\")\n'"
}<untrusted_context>
Output: base=0.0: 0.038788
base=0.05: 0.032644
base=0.1: 0.028814
base=0.2: 0.025656
base=0.3: 0.024137
Process Group PGID: 2470
</untrusted_context>Analyzing Decay Factor
My current focus is on understanding the impact of base=0.0, which signifies pure exponential decay with a 7-day window. It appears significantly more effective than retaining older interaction data. This is because stale, long-past interactions from over 100 days ago seem to dilute the listener's current musical engagement, especially when they're actively exploring new content.
{
"command": "python3 -c '\nimport sys\nsys.path.append(\"/task\")\nimport time\nimport numpy as np\nimport pandas as pd\nimport scipy.sparse as sp\nimport pyarrow.parquet as pq\nfrom pathlib import Path\nfrom sklearn.decomposition import TruncatedSVD\nfrom evaluate_public import validation_truth\nfrom ranking_evaluation import CandidateSet, ranked_items, score_rankings\n\nDATA = Path(\"/task/data\")\ntargets_df = pd.read_parquet(DATA / \"target_users.parquet\")\nuids = targets_df.uid.values\nuid_to_idx = {uid: idx for idx, uid in enumerate(uids)}\nn_users = len(uids)\n\ncatalog_df = pd.read_parquet(DATA / \"candidate_catalog.parquet\")\nitem_ids = catalog_df.item_id.values\nitem_to_idx = {item: idx for idx, item in enumerate(item_ids)}\nn_items = len(item_ids)\n\neligibility = CandidateSet(DATA / \"validation_eligible_candidates.npz\")\ngraded = validation_truth(eligibility, set(uids))\n\nval_elig = np.load(DATA / \"validation_eligible_candidates.npz\")\npacked = val_elig[\"packed_eligible\"]\n\nlistens_table = pq.read_table(DATA / \"train_listens.parquet\", columns=[\"uid\", \"item_id\", \"timestamp\", \"is_organic\", \"played_ratio_pct\"])\nl_uid = listens_table[\"uid\"].to_numpy()\nl_item = listens_table[\"item_id\"].to_numpy()\nl_ts = listens_table[\"timestamp\"].to_numpy()\nl_org = listens_table[\"is_organic\"].to_numpy()\nl_ratio = listens_table[\"played_ratio_pct\"].to_numpy()\n\nmask = np.isin(l_uid, uids) & np.isin(l_item, item_ids)\nu_idx = np.array([uid_to_idx[u] for u in l_uid[mask]], dtype=np.int32)\ni_idx = np.array([item_to_idx[i] for i in l_item[mask]], dtype=np.int32)\nl_ts_sub = l_ts[mask]\nl_org_sub = l_org[mask]\nl_ratio_sub = l_ratio[mask]\n\nlikes_df = pd.read_parquet(DATA / \"train_likes.parquet\")\nlikes_df = likes_df[likes_df.uid.isin(uids) & likes_df.item_id.isin(item_ids)]\nl_u = np.array([uid_to_idx[u] for u in likes_df.uid], dtype=np.int32)\nl_i = np.array([item_to_idx[i] for i in likes_df.item_id], dtype=np.int32)\nlikes_ts = likes_df.timestamp.values\n\npref_df = pd.read_parquet(DATA / \"validation_preference_state.parquet\")\npref_likes = pref_df[(pref_df.state == \"liked\") & pref_df.uid.isin(uids) & pref_df.item_id.isin(item_ids)]\np_u = np.array([uid_to_idx[u] for u in pref_likes.uid], dtype=np.int32)\np_i = np.array([item_to_idx[i] for i in pref_likes.item_id], dtype=np.int32)\n\nt_max = 24786800.0\ndecay_days = 7.0\n\ndays_ago = (t_max - l_ts_sub) / 86400.0\ntime_weight = np.exp(-days_ago / decay_days)\nlisten_weight = np.where(l_ratio_sub >= 80, 2.5, np.where(l_ratio_sub >= 50, 1.0, 0.2))\norg_weight = np.where(l_org_sub == 1, 2.0, 1.0)\nw_listens = listen_weight * org_weight * time_weight\n\nX = sp.csr_matrix((w_listens, (u_idx, i_idx)), shape=(n_users, n_items), dtype=np.float32)\n\nlikes_time = np.exp(-((t_max - likes_ts) / 86400.0) / decay_days)\nw_likes = 5.0 * likes_time\nX = X + sp.csr_matrix((w_likes, (l_u, l_i)), shape=(n_users, n_items), dtype=np.float32)\nX = X + sp.csr_matrix((np.full(len(p_u), 5.0, dtype=np.float32), (p_u, p_i)), shape=(n_users, n_items), dtype=np.float32)\n\nX.data = np.log1p(X.data)\n\ndf = np.bincount(X.nonzero()[1], minlength=n_items)\nidf = np.log1p(n_users / (1.0 + df)).astype(np.float32)\nX_idf = X.multiply(idf).tocsr()\n\nuser_norms = sp.linalg.norm(X_idf, axis=1)\nuser_norms[user_norms == 0] = 1.0\nX_norm = sp.diags(1.0 / user_norms.ravel()) @ X_idf\n\ndim = 128\nsvd = TruncatedSVD(n_components=dim, random_state=42)\nU = svd.fit_transform(X_norm)\nV = svd.components_\n\nK = 500\nfor w_knn in [0.0, 0.1, 0.2, 0.3, 0.4]:\n rows = []\n batch_size = 500\n for b_start in range(0, n_users, batch_size):\n b_end = min(b_start + batch_size, n_users)\n b_len = b_end - b_start\n\n s_svd = U[b_start:b_end] @ V\n s_svd_min = s_svd.min(axis=1, keepdims=True)\n s_svd_max = s_svd.max(axis=1, keepdims=True)\n s_svd = (s_svd - s_svd_min) / (s_svd_max - s_svd_min + 1e-6)\n\n if w_knn > 0:\n sim_batch = (X_norm[b_start:b_end] @ X_norm.T).toarray()\n for i in range(b_len):\n sim_batch[i, b_start + i] = 0.0\n topk_idx = np.argpartition(sim_batch, -K, axis=1)[:, -K:]\n row_idx = np.repeat(np.arange(b_len), K)\n col_idx = topk_idx.ravel()\n w_k = sim_batch[row_idx, col_idx]\n w_k[w_k < 0] = 0\n W_batch = sp.csr_matrix((w_k, (row_idx, col_idx)), shape=(b_len, n_users), dtype=np.float32)\n s_knn = (W_batch @ X_idf).toarray()\n s_knn_min = s_knn.min(axis=1, keepdims=True)\n s_knn_max = s_knn.max(axis=1, keepdims=True)\n s_knn = (s_knn - s_knn_min) / (s_knn_max - s_knn_min + 1e-6)\n s_final = (1.0 - w_knn) * s_svd + w_knn * s_knn\n else:\n s_final = s_svd\n\n for i in range(b_len):\n u_curr = b_start + i\n uid = uids[u_curr]\n mask = np.unpackbits(packed[u_curr], count=n_items, bitorder=\"little\")\n u_scores = s_final[i]\n u_scores[mask == 0] = -np.inf\n top100 = np.argpartition(u_scores, -100)[-100:]\n top100 = top100[np.argsort(-u_scores[top100])]\n top_items = item_ids[top100]\n for rank_idx, item in enumerate(top_items, 1):\n rows.append((uid, item, rank_idx))\n\n df_sub = pd.DataFrame(rows, columns=[\"uid\", \"item_id\", \"rank\"])\n ranked = ranked_items(df_sub)\n scores = score_rankings(ranked, graded)\n score_val = scores[\"quality_ndcg@10\"]\n print(f\"w_knn={w_knn} -> NDCG@10: {score_val}\")\n'",
"description": "Test SVD + User-User kNN with 7-day decay"
}<untrusted_context>
Output: w_knn=0.0 -> NDCG@10: 0.038788
w_knn=0.1 -> NDCG@10: 0.041966
w_knn=0.2 -> NDCG@10: 0.04431
w_knn=0.3 -> NDCG@10: 0.047341
w_knn=0.4 -> NDCG@10: 0.04895
Process Group PGID: 2499
</untrusted_context>Exploring w_knn weights
I've been investigating the impact of varying w_knn weights. With a value of 0.4, I've achieved an NDCG@10 of 0.048950, which is a significant improvement, nearly five times better than the baseline. I'm now curious to explore weights of 0.5, 0.6, and beyond.
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]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.
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.