Basic recommender gemini-3-8-flash-high-07
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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.Analyzing the Challenge
I'm currently dissecting the prompt, aiming to grasp every nuance of the requirements, constraints, and the technological landscape. My focus is on fully understanding the environment and available tools to ensure a robust approach to building the "Made for You" shelf ranking model.
Refining the Output
I'm focusing on the precise output format required: 100-row submission files (parquet or csv) per user, with ranks 1-100, ensuring all listed items are within their eligible candidates and that no ties exist. I'll also ensure the best valid submission is saved throughout the process.
Exploring and Establishing Baseline
I'm initiating the process by exploring the /task/ directory, investigating available hardware resources, and reviewing the evaluation scripts. My immediate goal is to understand the data structure, system capabilities, and the fundamental requirements for generating valid submission files.
{
"strategic_intent": "Explore environment, inspect hardware and dataset files, and inspect evaluation scripts.",
"title": "Exploration and Baseline Setup",
"summary": "Beginning exploration and baseline setup. We will inspect hardware resources (CPU, GPU, RAM), examine the files under /task/, inspect evaluate_public.py and ranking_evaluation.py, and examine data shapes to guide model design."
}Current topic: "Exploration and Baseline Setup"
Topic summary: Beginning exploration and baseline setup. We will inspect hardware resources (CPU, GPU, RAM), examine the files under /task/, inspect evaluate_public.py and ranking_evaluation.py, and examine data shapes to guide model design.
Strategic Intent: Explore environment, inspect hardware and dataset files, and inspect evaluation scripts.{
"description": "Check system hardware resources and list task files",
"command": "lscpu | grep \"Model name\\|CPU(s):\"; free -h; nvidia-smi || true; ls -la /task; ls -la /task/data"
}<untrusted_context>
Output: CPU(s): 8
Model name: AMD EPYC 7R13 Processor
NUMA node0 CPU(s): 0-7
/usr/bin/bash: line 4: free: command not found
/usr/bin/bash: line 4: nvidia-smi: command not found
total 36
dr-xr-xr-x 1 root root 4096 Sep 29 01:21 .
drwxr-xr-x 1 root root 4096 Sep 29 01:23 ..
dr-xr-xr-x 1 root root 4096 Sep 29 01:18 data
-r--r--r-- 1 root root 5774 Sep 29 01:17 evaluate_public.py
-r--r--r-- 1 root root 8265 Sep 29 01:17 ranking_evaluation.py
-r--r--r-- 1 root root 1278 Sep 29 01:17 requirements.txt
total 347392
dr-xr-xr-x 1 root root 4096 Sep 29 01:18 .
dr-xr-xr-x 1 root root 4096 Sep 29 01:21 ..
-r--r--r-- 1 root root 349352 Sep 29 01:17 candidate_catalog.parquet
-r--r--r-- 1 root root 84127 Sep 29 01:17 target_users.parquet
-r--r--r-- 1 root root 7736341 Sep 29 01:17 test_eligible_candidates.npz
-r--r--r-- 1 root root 1092329 Sep 29 01:18 test_preference_state.parquet
-r--r--r-- 1 root root 857213 Sep 29 01:17 train_dislikes.parquet
-r--r--r-- 1 root root 5374652 Sep 29 01:17 train_likes.parquet
-r--r--r-- 1 root root 319183255 Sep 29 01:17 train_listens.parquet
-r--r--r-- 1 root root 155988 Sep 29 01:17 train_undislikes.parquet
-r--r--r-- 1 root root 1855885 Sep 29 01:17 train_unlikes.parquet
-r--r--r-- 1 root root 36571 Sep 29 01:17 val_dislikes.parquet
-r--r--r-- 1 root root 298484 Sep 29 01:17 val_likes.parquet
-r--r--r-- 1 root root 9947609 Sep 29 01:17 val_listens.parquet
-r--r--r-- 1 root root 7845 Sep 29 01:17 val_undislikes.parquet
-r--r--r-- 1 root root 72536 Sep 29 01:17 val_unlikes.parquet
-r--r--r-- 1 root root 7573821 Sep 29 01:17 validation_eligible_candidates.npz
-r--r--r-- 1 root root 1061761 Sep 29 01:18 validation_preference_state.parquet
Process Group PGID: 1612
</untrusted_context>{
"file_path": "/task/evaluate_public.py"
}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{
"file_path": "/task/ranking_evaluation.py"
}Path not in workspace: Attempted path "/task/ranking_evaluation.py" resolves outside the allowed workspace directories: /app or the project temp directory: /home/agent/.gemini/tmp/app{
"command": "grep MemTotal /proc/meminfo; cat /task/requirements.txt",
"description": "Check total memory and inspect installed Python packages"
}<untrusted_context>
Output: MemTotal: 15988964 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": "cat /task/evaluate_public.py",
"description": "Read 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: 1623
</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: 1627
</untrusted_context>Analyzing System Configuration
My current focus is on understanding the system's resources. I've noted the presence of 8 CPU cores and approximately 16GB of RAM. Notably, no GPU is detected. Key Python packages like numpy, pandas, scipy, scikit-learn, and pyarrow are installed and readily available for use.
Investigating Library Dependencies
I'm checking for advanced gradient boosting libraries like LightGBM, XGBoost, and CatBoost. If they're not present, I'll proceed with utilizing scikit-learn, NumPy, and SciPy for my modeling tasks. I'll also start examining the provided dataset files and their structures.
{
"command": "python3 -c \"\nfor mod in ['lightgbm', 'xgboost', 'catboost', 'torch', 'implicit', 'sklearn', 'scipy', 'numpy', 'pandas', 'pyarrow']:\n try:\n __import__(mod)\n print(mod, 'available')\n except ImportError:\n print(mod, 'NOT available')\n\"",
"description": "Check ML library availability"
}<untrusted_context>
Output: lightgbm NOT available
xgboost NOT available
catboost NOT available
torch NOT available
implicit NOT available
sklearn available
scipy available
numpy available
pandas available
pyarrow available
Process Group PGID: 1630
</untrusted_context>{
"command": "python3 -c \"\nimport pandas as pd\nimport numpy as np\n\ntargets = pd.read_parquet('/task/data/target_users.parquet')\nprint('Targets shape:', targets.shape)\nprint('Targets head:\\n', targets.head())\n\ncatalog = pd.read_parquet('/task/data/candidate_catalog.parquet')\nprint('Catalog shape:', catalog.shape)\nprint('Catalog head:\\n', catalog.head())\n\nval_pref = pd.read_parquet('/task/data/validation_preference_state.parquet')\nprint('Val pref shape:', val_pref.shape)\nprint('Val pref head:\\n', val_pref.head())\n\ntest_pref = pd.read_parquet('/task/data/test_preference_state.parquet')\nprint('Test pref shape:', test_pref.shape)\nprint('Test pref head:\\n', test_pref.head())\n\nval_npz = np.load('/task/data/validation_eligible_candidates.npz')\nprint('val_npz keys:', val_npz.files)\nprint('uids len:', len(val_npz['uids']), 'item_ids len:', len(val_npz['item_ids']), 'packed shape:', val_npz['packed_eligible'].shape)\n\ntest_npz = np.load('/task/data/test_eligible_candidates.npz')\nprint('test_npz keys:', test_npz.files)\nprint('test uids len:', len(test_npz['uids']), 'test item_ids len:', len(test_npz['item_ids']), 'test packed shape:', test_npz['packed_eligible'].shape)\n\"",
"description": "Inspect dataset schemas and shapes"
}<untrusted_context>
Output: Targets shape: (8456, 3)
Targets head:
uid recent_positive_listens history_positive_listens
0 100 844.0 2604
1 200 50.0 286
2 300 168.0 359
3 600 1985.0 2423
4 700 1776.0 6114
Catalog shape: (30000, 4)
Catalog head:
item_id positive_listens organic_positive_listens popularity_rank
0 5862961 26615 20620 1
1 6901374 25536 17347 2
2 5635052 24691 17194 3
3 3542184 24075 17699 4
4 9378983 24036 18195 5
Val pref shape: (469124, 3)
Val pref head:
uid item_id state
0 100 410868 liked
1 100 732449 liked
2 100 982903 liked
3 100 1730273 liked
4 100 2621343 liked
Test pref shape: (484776, 3)
Test pref head:
uid item_id state
0 100 410868 liked
1 100 732449 liked
2 100 982903 liked
3 100 1730273 liked
4 100 2621343 liked
val_npz keys: ['uids', 'item_ids', 'packed_eligible']
uids len: 8456 item_ids len: 30000 packed shape: (8456, 3750)
test_npz keys: ['uids', 'item_ids', 'packed_eligible']
test uids len: 8456 test item_ids len: 30000 test packed shape: (8456, 3750)
Process Group PGID: 1633
</untrusted_context>Analyzing Data Structure
My current focus is on understanding the dimensions and nature of the provided datasets. I've noted 8456 target users and 30,000 candidate items, with the item IDs in val_npz and test_npz aligning with the candidate catalog. The packed_eligible array's shape (8456, 3750) bytes is also under review.
{
"description": "Inspect alignment of candidates and count eligible items",
"command": "python3 -c \"\nimport numpy as np\nimport pandas as pd\n\nval_npz = np.load('/task/data/validation_eligible_candidates.npz')\ntest_npz = np.load('/task/data/test_eligible_candidates.npz')\n\n# Check if item_ids and uids match catalog and targets\ntargets = pd.read_parquet('/task/data/target_users.parquet')\ncatalog = pd.read_parquet('/task/data/candidate_catalog.parquet')\n\nprint('val uids == targets uid:', np.array_equal(val_npz['uids'], targets['uid'].values))\nprint('test uids == targets uid:', np.array_equal(test_npz['uids'], targets['uid'].values))\nprint('val items == catalog item_id:', np.array_equal(val_npz['item_ids'], catalog['item_id'].values))\nprint('test items == catalog item_id:', np.array_equal(test_npz['item_ids'], catalog['item_id'].values))\n\n# Count eligible items per user\nval_unpacked = np.unpackbits(val_npz['packed_eligible'], axis=1, count=30000, bitorder='little')\ntest_unpacked = np.unpackbits(test_npz['packed_eligible'], axis=1, count=30000, bitorder='little')\n\nprint('val eligible per user: min, median, max, mean:', \n val_unpacked.sum(axis=1).min(), np.median(val_unpacked.sum(axis=1)), val_unpacked.sum(axis=1).max(), val_unpacked.sum(axis=1).mean())\nprint('test eligible per user: min, median, max, mean:', \n test_unpacked.sum(axis=1).min(), np.median(test_unpacked.sum(axis=1)), test_unpacked.sum(axis=1).max(), test_unpacked.sum(axis=1).mean())\n\""
}<untrusted_context>
Output: val uids == targets uid: True
test uids == targets uid: True
val items == catalog item_id: True
test items == catalog item_id: True
val eligible per user: min, median, max, mean: 24657 29242.0 30000 29067.306291390727
test eligible per user: min, median, max, mean: 24552 29220.0 30000 29041.3178807947
Process Group PGID: 1665
</untrusted_context>Confirming Data Alignment
My latest checks indicate a strong alignment between user IDs and target IDs, as well as item IDs and catalog IDs. The volume of eligible items per user is substantial, roughly 29,000.
{
"description": "Inspect validation ground truth stats and timestamp ranges",
"command": "python3 -c \"\nimport pandas as pd\nfrom ranking_evaluation import CandidateSet\nimport evaluate_public\n\ntargets_frame = pd.read_parquet('/task/data/target_users.parquet')\ntargets = set(targets_frame.uid.astype(int))\neligibility = CandidateSet('/task/data/validation_eligible_candidates.npz')\ngraded = evaluate_public.validation_truth(eligibility, targets)\nprint('Number of users with validation labels:', len(graded))\ntotal_labels = sum(len(v) for v in graded.values())\ngrade_counts = {}\nfor v in graded.values():\n for g in v.values():\n grade_counts[g] = grade_counts.get(g, 0) + 1\nprint('Total graded user-item pairs:', total_labels)\nprint('Grade counts:', grade_counts)\n\n# Let's check timestamps of train and val\nval_listens = pd.read_parquet('/task/data/val_listens.parquet')\nprint('Val listens timestamps: min', val_listens.timestamp.min(), 'max', val_listens.timestamp.max())\ntrain_likes = pd.read_parquet('/task/data/train_likes.parquet')\nprint('Train likes timestamps: min', train_likes.timestamp.min(), 'max', train_likes.timestamp.max())\n\""
}<untrusted_context>
Output: Traceback (most recent call last):
File "<string>", line 3, in <module>
ModuleNotFoundError: No module named 'ranking_evaluation'
Exit Code: 1
Process Group PGID: 1682
</untrusted_context>{
"description": "Inspect validation ground truth stats with PYTHONPATH",
"command": "python3 -c \"\nimport sys\nsys.path.append('/task')\nimport pandas as pd\nfrom ranking_evaluation import CandidateSet\nimport evaluate_public\n\ntargets_frame = pd.read_parquet('/task/data/target_users.parquet')\ntargets = set(targets_frame.uid.astype(int))\neligibility = CandidateSet('/task/data/validation_eligible_candidates.npz')\ngraded = evaluate_public.validation_truth(eligibility, targets)\nprint('Number of users with validation labels:', len(graded))\ntotal_labels = sum(len(v) for v in graded.values())\ngrade_counts = {}\nfor v in graded.values():\n for g in v.values():\n grade_counts[g] = grade_counts.get(g, 0) + 1\nprint('Total graded user-item pairs:', total_labels)\nprint('Grade counts:', grade_counts)\n\nval_listens = pd.read_parquet('/task/data/val_listens.parquet')\nprint('Val listens timestamps: min', val_listens.timestamp.min(), 'max', val_listens.timestamp.max(), 'count:', len(val_listens))\ntrain_likes = pd.read_parquet('/task/data/train_likes.parquet')\nprint('Train likes timestamps: min', train_likes.timestamp.min(), 'max', train_likes.timestamp.max(), 'count:', len(train_likes))\n\""
}<untrusted_context>
Output: Number of users with validation labels: 4263
Total graded user-item pairs: 37769
Grade counts: {1: 33352, 3: 4417}
Val listens timestamps: min 24788600 max 25393395 count: 1501697
Train likes timestamps: min 80 max 24786790 count: 814831
Process Group PGID: 1693
</untrusted_context>Analyzing Data Gaps
I'm currently examining the training data endpoint and noting a significant gap before validation begins. I'm also tracking the duration of the validation period and the proportion of target users included.
{
"command": "python3 -c \"\nimport pyarrow.parquet as pq\nfor name in ['train_listens', 'train_likes', 'train_dislikes', 'train_unlikes', 'train_undislikes', \n 'val_listens', 'val_likes', 'val_dislikes', 'val_unlikes', 'val_undislikes']:\n meta = pq.read_metadata(f'/task/data/{name}.parquet')\n print(f'{name}: {meta.num_rows} rows, {meta.num_columns} cols')\n\"",
"description": "Check row counts across all parquet files"
}<untrusted_context>
Output: train_listens: 43484159 rows, 6 cols
train_likes: 814831 rows, 4 cols
train_dislikes: 101075 rows, 4 cols
train_unlikes: 292665 rows, 4 cols
train_undislikes: 19960 rows, 4 cols
val_listens: 1501697 rows, 6 cols
val_likes: 37795 rows, 4 cols
val_dislikes: 3563 rows, 4 cols
val_unlikes: 9346 rows, 4 cols
val_undislikes: 606 rows, 4 cols
Process Group PGID: 1713
</untrusted_context>Analyzing User Data
My current focus is on understanding the user distribution within the training data. I'm investigating the total number of unique users present in train_listens to see if the 8,456 target users represent the entirety of the user base or just a subset.
{
"command": "python3 -c \"\nimport pyarrow.dataset as ds\nimport pandas as pd\n\ndataset = ds.dataset('/task/data/train_listens.parquet')\nprint('Schema:', dataset.schema)\n\n# Sample read some batches or columns\ntargets = pd.read_parquet('/task/data/target_users.parquet')\ntarget_uids = set(targets.uid.astype(int))\n\n# Check in train_likes how many uids are in targets\ntrain_likes = pd.read_parquet('/task/data/train_likes.parquet')\nprint('train_likes total unique uids:', train_likes.uid.nunique())\nprint('train_likes unique uids in targets:', train_likes[train_likes.uid.isin(target_uids)].uid.nunique())\n\n# Let's inspect timestamp range of train_listens\n# Using pyarrow scanner to read min/max timestamp or sample\ntable_sample = dataset.scanner(columns=['uid', 'timestamp', 'item_id', 'is_organic', 'played_ratio_pct'], \n batch_size=1000000).to_batches()\nfirst_batch = next(table_sample)\nprint('First batch shape:', first_batch.to_pandas().shape)\nprint('First batch head:\\n', first_batch.to_pandas().head())\n\"",
"description": "Inspect train_listens structure and user overlap"
}<untrusted_context>
Output: Schema: uid: uint32
timestamp: uint32
item_id: uint32
is_organic: uint8
played_ratio_pct: uint16
track_length_seconds: uint32
-- schema metadata --
pandas: '{"index_columns": [], "column_indexes": [], "columns": [{"name":' + 797
train_likes total unique uids: 8144
train_likes unique uids in targets: 7071
First batch shape: (1000000, 5)
First batch head:
uid timestamp item_id is_organic played_ratio_pct
0 100 39420 8326270 0 100
1 100 39420 1441281 0 100
2 100 39625 286361 0 100
3 100 40110 732449 0 100
4 100 40360 3397170 0 46
Process Group PGID: 1724
</untrusted_context>{
"description": "Inspect target users summary stats and total rows in train_listens",
"command": "python3 -c \"\nimport pyarrow.dataset as ds\nimport pandas as pd\n\ntargets = pd.read_parquet('/task/data/target_users.parquet')\nprint('Targets total history positive listens sum:', targets.history_positive_listens.sum())\nprint('Targets recent positive listens sum:', targets.recent_positive_listens.sum())\n\n# Let's see how fast pyarrow scanner is over train_listens filtering on target uids or inspecting\ndataset = ds.dataset('/task/data/train_listens.parquet')\nprint('Total rows in dataset:', dataset.count_rows())\n\""
}<untrusted_context>
Output: Targets total history positive listens sum: 27367343
Targets recent positive listens sum: 10579105.0
Total rows in dataset: 43484159
Process Group PGID: 1751
</untrusted_context>Analyzing Target Data
My focus has shifted to understanding the user listening patterns within the train_listens dataset. I've observed that approximately 27.3 million out of 43.4 million total rows represent positive listening history for the targets, which is a crucial piece of information for building the ranking model.
Refining Ranking Logic
I'm now focusing on the specific grading system for candidate relevance. The system prioritizes user likes (grade 3) over significant listens (grade 1) and otherwise (grade 0). This graded approach will be crucial for calculating the DCG and ultimately the NDCG@10 for measuring ranking quality.
Testing Popularity Baseline
I'm evaluating a popularity-based baseline using organic_positive_listens from the candidate_catalog.parquet. This seems like a quick and robust way to test the evaluation pipeline and establish an initial performance metric.
{
"description": "Build and evaluate simple popularity baseline on validation set",
"command": "python3 -c \"\nimport numpy as np\nimport pandas as pd\nimport sys\nsys.path.append('/task')\nfrom ranking_evaluation import CandidateSet, validate_ranking, ranked_items, score_rankings\nimport evaluate_public\n\ncatalog = pd.read_parquet('/task/data/candidate_catalog.parquet')\nval_npz = np.load('/task/data/validation_eligible_candidates.npz')\nuids = val_npz['uids']\nitem_ids = val_npz['item_ids']\npacked = val_npz['packed_eligible']\n\n# Let's unpack bits for all users\nunpacked = np.unpackbits(packed, axis=1, count=len(item_ids), bitorder='little')\n\n# For each user, pick top 100 eligible items by popularity rank (items are already in order 0..29999 in catalog)\n# In catalog, item_ids matches val_npz['item_ids'] exactly!\n# Let's verify:\nprint('catalog item_id matches item_ids:', np.array_equal(catalog['item_id'].values, item_ids))\n\n# Let's test standard popularity:\nrows = []\nfor r, uid in enumerate(uids):\n # argwhere of unpacked[r] gives the indices of eligible items in catalog order!\n eligible_indices = np.where(unpacked[r])[0][:100]\n chosen_items = item_ids[eligible_indices]\n for rank, item in enumerate(chosen_items, start=1):\n rows.append((uid, item, rank))\n\nval_sub = pd.DataFrame(rows, columns=['uid', 'item_id', 'rank'])\nval_sub.to_parquet('/app/solution/val_baseline_pop.parquet')\nprint('Val baseline saved, row count:', len(val_sub))\n\" && python3 /task/evaluate_public.py /app/solution/val_baseline_pop.parquet"
}<untrusted_context>
Output: catalog item_id matches item_ids: True
Val baseline saved, row count: 845600
{
"model": "/app/solution/val_baseline_pop.parquet",
"scores": {
"quality_ndcg@10": 0.01071,
"quality_users": 4263
}
}
Process Group PGID: 1767
</untrusted_context>Testing Ranking Strategies
My initial baseline using basic popularity has yielded quality_ndcg@10 of 0.010710 in under 2 seconds. Now, I'm exploring alternative ranking approaches, specifically considering organic_positive_listens and recency of positive listens to identify potential improvements.
{
"description": "Generate and validate initial submission.parquet on test set",
"command": "python3 -c \"\nimport numpy as np\nimport pandas as pd\nimport sys\nsys.path.append('/task')\nfrom ranking_evaluation import CandidateSet, validate_ranking\n\ncatalog = pd.read_parquet('/task/data/candidate_catalog.parquet')\ntest_npz = np.load('/task/data/test_eligible_candidates.npz')\ntargets_frame = pd.read_parquet('/task/data/target_users.parquet')\ntargets = set(targets_frame.uid.astype(int))\n\nuids = test_npz['uids']\nitem_ids = test_npz['item_ids']\npacked = test_npz['packed_eligible']\nunpacked = np.unpackbits(packed, axis=1, count=len(item_ids), bitorder='little')\n\nrows = []\nfor r, uid in enumerate(uids):\n eligible_indices = np.where(unpacked[r])[0][:100]\n chosen_items = item_ids[eligible_indices]\n for rank, item in enumerate(chosen_items, start=1):\n rows.append((uid, item, rank))\n\ntest_sub = pd.DataFrame(rows, columns=['uid', 'item_id', 'rank'])\ntest_sub.to_parquet('/app/solution/submission.parquet')\nprint('Initial submission saved, row count:', len(test_sub))\n\n# Validate submission using validate_ranking\nvalidated = validate_ranking(test_sub, targets)\neligibility = CandidateSet('/task/data/test_eligible_candidates.npz')\nfor uid, item in validated[['uid', 'item_id']].itertuples(index=False, name=None):\n if not eligibility.is_eligible(uid, item):\n raise ValueError(f'Ineligible item: {uid}, {item}')\nprint('Submission validation PASSED! All invariants satisfied.')\n\""
}<untrusted_context>
Output: Initial submission saved, row count: 845600
Submission validation PASSED! All invariants satisfied.
Process Group PGID: 1802
</untrusted_context>Confirming Valid Fallback
I've successfully generated a robust fallback submission file, /app/solution/submission.parquet. It meticulously adheres to all specified constraints, ensuring 8456 target users, exactly 100 unique items ranked 1-100 per user, and confirmed item eligibility. The next step involves refining the current focus.
{
"strategic_intent": "Analyze user interaction history, candidate relationships, and discover ranking patterns.",
"summary": "Initial baseline submission is created and verified. Now analyzing the user-item interaction signals (likes, dislikes, organic listens, completion ratios, recency), exploring collaborative filtering, matrix factorization, and item-item similarity approaches suited to the discovery objective and 16GB memory / 8-core CPU environment.",
"title": "Interaction Analysis and Modeling Strategy"
}Current topic: "Interaction Analysis and Modeling Strategy"
Topic summary: Initial baseline submission is created and verified. Now analyzing the user-item interaction signals (likes, dislikes, organic listens, completion ratios, recency), exploring collaborative filtering, matrix factorization, and item-item similarity approaches suited to the discovery objective and 16GB memory / 8-core CPU environment.
Strategic Intent: Analyze user interaction history, candidate relationships, and discover ranking patterns.Refining Problem Understanding
I'm currently dissecting the core objective: constructing the most robust ranking model possible for a music streaming service's "Made for You" shelf. My focus is on per-user candidate eligibility and cutoff-specific explicit data.
Analyzing Candidate Data
My current focus is on understanding the candidate catalog data. I'm examining positive_listens, organic_positive_listens, and popularity_rank to identify key features for ranking.
Defining Ranking Metrics
I am now delving into how to evaluate the ranking performance. The primary metric appears to be NDCG@10, using a specific gain and discount calculation, averaged per user.
Exploring User Data
My attention is now directed towards the user data. I am analyzing recent_positive_listens and history_positive_listens to capture user engagement patterns.
Evaluating Explicit Feedback
I'm currently investigating the explicit feedback data, specifically validation_preference_state and test_preference_state. This will help me understand user liked and disliked signals.
{
"command": "python3 -c \"\nimport pandas as pd\nimport numpy as np\n\nval_pref = pd.read_parquet('/task/data/validation_preference_state.parquet')\nprint('Val pref state counts:\\n', val_pref['state'].value_counts())\nprint('Val pref unique users:', val_pref['uid'].nunique(), 'unique items:', val_pref['item_id'].nunique())\n\nval_npz = np.load('/task/data/validation_eligible_candidates.npz')\nuids = val_npz['uids']\nitem_ids = val_npz['item_ids']\npacked = val_npz['packed_eligible']\nunpacked = np.unpackbits(packed, axis=1, count=len(item_ids), bitorder='little')\n\nuid_to_row = {uid: r for r, uid in enumerate(uids)}\nitem_to_col = {item: c for c, item in enumerate(item_ids)}\n\n# Check whether items in val_pref are eligible\neligible_likes = 0\ntotal_likes = 0\nfor uid, item, state in val_pref.itertuples(index=False):\n r = uid_to_row.get(uid)\n c = item_to_col.get(item)\n if r is not None and c is not None:\n if state == 'liked':\n total_likes += 1\n if unpacked[r, c]:\n eligible_likes += 1\n\nprint(f'Total liked in val_pref: {total_likes}, eligible liked: {eligible_likes}')\n\n# Check validation ground truth items: are they in val_pref?\nsys_path = '/task'\nimport sys; sys.path.append(sys_path)\nimport evaluate_public\nfrom ranking_evaluation import CandidateSet\neligibility = CandidateSet('/task/data/validation_eligible_candidates.npz')\ntargets_frame = pd.read_parquet('/task/data/target_users.parquet')\ntargets = set(targets_frame.uid.astype(int))\ngraded = evaluate_public.validation_truth(eligibility, targets)\n\n# How many ground truth relevant items are already liked in val_pref?\nalready_liked_gt = 0\ntotal_gt = 0\npref_liked_pairs = set(val_pref[val_pref.state == 'liked'][['uid', 'item_id']].itertuples(index=False, name=None))\nfor uid, items in graded.items():\n for item, grade in items.items():\n total_gt += 1\n if (uid, item) in pref_liked_pairs:\n already_liked_gt += 1\nprint(f'Total GT relevant items: {total_gt}, of which already in pref_liked_pairs: {already_liked_gt}')\n\"",
"description": "Check if liked items are eligible and if ground truth items were previously liked"
}<untrusted_context>
Output: Val pref state counts:
state
liked 418435
disliked 50689
Name: count, dtype: int64
Val pref unique users: 7038 unique items: 28814
Total liked in val_pref: 418435, eligible liked: 0
Total GT relevant items: 37769, of which already in pref_liked_pairs: 0
Process Group PGID: 1819
</untrusted_context>Analyzing Discovery Anomalies
I'm noticing a curious pattern where no "liked" items in val_pref are deemed eligible, nor are any GT relevant items appearing in pref_liked_pairs. This suggests a potential filtering issue, as items already in the user's preference list are being excluded, which seems to be the intended behavior but the zero counts are stark.
{
"description": "Check if previously listened items can be eligible candidates",
"command": "python3 -c \"\nimport pyarrow.dataset as ds\nimport pandas as pd\nimport numpy as np\n\nval_npz = np.load('/task/data/validation_eligible_candidates.npz')\nuids = set(val_npz['uids'])\nitem_ids = set(val_npz['item_ids'])\n\n# Read a sample of train_listens for target users\nlistens = pd.read_parquet('/task/data/val_listens.parquet')\nprint('val_listens columns:', listens.columns)\n\n# Check if items in val_listens (ground truth) were previously listened to in train_listens\n# Let's check for 100 random target users\ntargets = list(uids)[:100]\ndataset = ds.dataset('/task/data/train_listens.parquet')\nsample_train = dataset.to_table(filter=ds.field('uid').isin(targets)).to_pandas()\nprint('Sample train listens for 100 users:', len(sample_train))\n\nsys_path = '/task'\nimport sys; sys.path.append(sys_path)\nimport evaluate_public\nfrom ranking_evaluation import CandidateSet\neligibility = CandidateSet('/task/data/validation_eligible_candidates.npz')\ntargets_frame = pd.read_parquet('/task/data/target_users.parquet')\ngraded = evaluate_public.validation_truth(eligibility, set(targets_frame.uid.astype(int)))\n\n# Check for those 100 users, were any of their graded items listened to in train?\ngraded_items_sample = set()\nfor u in targets:\n if u in graded:\n for it in graded[u]:\n graded_items_sample.add((u, it))\n\ntrain_pairs_sample = set(sample_train[['uid', 'item_id']].itertuples(index=False, name=None))\noverlap = graded_items_sample.intersection(train_pairs_sample)\nprint(f'For 100 target users: graded pairs = {len(graded_items_sample)}, previously listened in train = {len(overlap)}')\n\n# What about eligibility for previously listened items in sample_train?\neligible_previously_listened = sum(eligibility.is_eligible(u, it) for u, it in train_pairs_sample)\nprint(f'For 100 target users: train listened pairs = {len(train_pairs_sample)}, of which eligible = {eligible_previously_listened}')\n\""
}<untrusted_context>
Output: val_listens columns: Index(['uid', 'timestamp', 'item_id', 'is_organic', 'played_ratio_pct',
'track_length_seconds'],
dtype='object')
Sample train listens for 100 users: 451914
For 100 target users: graded pairs = 448, previously listened in train = 0
For 100 target users: train listened pairs = 126266, of which eligible = 0
Process Group PGID: 1839
</untrusted_context>Exploring Candidate Pool
I'm currently assessing the candidate pool, noticing a peculiar outcome: zero eligible pairs from the listened set for training, yet a substantial number of graded pairs. This suggests a potential disconnect in how I'm defining or filtering for these sets.
Refining Item Similarity
My thoughts are now focused on refining how I define item similarity. I'm investigating the distinction between general "positive_listens" and "organic_positive_listens" within the candidate catalog to see if that clarifies why eligible pairs for training seem absent.
{
"command": "python3 -c \"\nimport numpy as np\nimport pandas as pd\nimport sys\nsys.path.append('/task')\n\ncatalog = pd.read_parquet('/task/data/candidate_catalog.parquet')\nval_npz = np.load('/task/data/validation_eligible_candidates.npz')\nuids = val_npz['uids']\nitem_ids = val_npz['item_ids']\npacked = val_npz['packed_eligible']\nunpacked = np.unpackbits(packed, axis=1, count=len(item_ids), bitorder='little')\n\n# Sort catalog by organic_positive_listens descending\ncatalog['orig_idx'] = np.arange(len(catalog))\nsorted_catalog = catalog.sort_values('organic_positive_listens', ascending=False)\nsorted_indices = sorted_catalog['orig_idx'].values\n\n# Let's see: for each user, pick top 100 eligible items in sorted_indices order\nrows = []\nfor r, uid in enumerate(uids):\n user_eligible = unpacked[r]\n # filter sorted_indices by user_eligible\n chosen = [idx for idx in sorted_indices if user_eligible[idx]][:100]\n chosen_items = item_ids[chosen]\n for rank, item in enumerate(chosen_items, start=1):\n rows.append((uid, item, rank))\n\nval_sub = pd.DataFrame(rows, columns=['uid', 'item_id', 'rank'])\nval_sub.to_parquet('/app/solution/val_baseline_organic.parquet')\n\" && python3 /task/evaluate_public.py /app/solution/val_baseline_organic.parquet",
"description": "Evaluate organic positive listens baseline on validation set"
}<untrusted_context>
Output: {
"model": "/app/solution/val_baseline_organic.parquet",
"scores": {
"quality_ndcg@10": 0.010578,
"quality_users": 4263
}
}
Process Group PGID: 1860
</untrusted_context>Exploring Personalization Strategies
My current focus is on understanding the impact of collaborative filtering on recommendation quality. I'm investigating how personalized user tastes can be leveraged to move beyond the baseline NDCG@10 of 0.0107 achieved with global popularity alone, recognizing the strong genre-specific nature of music preferences.
Analyzing User Interactions
I'm now delving into the specifics of user-item interactions to inform candidate selection. My investigation into the scale of user history and candidate items confirms we're working within a defined 30,000-track catalog. I'm preparing to analyze the train_likes dataset to understand the density of interactions among our target users and identify potential features for personalization.
{
"description": "Check target users interactions and overlap with candidate catalog",
"command": "python3 -c \"\nimport pandas as pd\nimport numpy as np\n\ntargets = pd.read_parquet('/task/data/target_users.parquet')\ntarget_uids = set(targets.uid.astype(int))\n\nval_pref = pd.read_parquet('/task/data/validation_preference_state.parquet')\nprint('Val pref: target users covered:', val_pref[val_pref.uid.isin(target_uids)].uid.nunique(), 'out of', len(targets))\nprint('Likes in val pref for target users:', (val_pref.uid.isin(target_uids) & (val_pref.state == 'liked')).sum())\nprint('Dislikes in val pref for target users:', (val_pref.uid.isin(target_uids) & (val_pref.state == 'disliked')).sum())\n\ntrain_likes = pd.read_parquet('/task/data/train_likes.parquet')\ntarget_likes = train_likes[train_likes.uid.isin(target_uids)]\nprint('Train likes for target users: count =', len(target_likes), 'unique users =', target_likes.uid.nunique())\n\n# Check overlap of items in target_likes with candidate_catalog\ncatalog = pd.read_parquet('/task/data/candidate_catalog.parquet')\ncatalog_items = set(catalog.item_id)\nprint('Target likes items in catalog:', target_likes.item_id.isin(catalog_items).mean())\n\""
}<untrusted_context>
Output: Val pref: target users covered: 7038 out of 8456
Likes in val pref for target users: 418435
Dislikes in val pref for target users: 50689
Train likes for target users: count = 690968 unique users = 7071
Target likes items in catalog: 0.6843862523300644
Process Group PGID: 1895
</untrusted_context>Analyzing User Behavior
I'm currently examining the listening habits of target users, specifically focusing on the 1,418 individuals not accounted for by current validation preferences. I'm cross-referencing their recent and historical positive listens against the validation_preference_state data.
{
"description": "Benchmark reading train_listens with pyarrow",
"command": "python3 -c \"\nimport pyarrow.parquet as pq\nimport pyarrow.compute as pc\nimport time\n\nt0 = time.time()\n# Read columns uid, item_id, played_ratio_pct, is_organic, timestamp\ntable = pq.read_table('/task/data/train_listens.parquet', columns=['uid', 'item_id', 'played_ratio_pct', 'is_organic', 'timestamp'])\nprint(f'Read parquet table in {time.time() - t0:.2f} s, rows: {len(table)}')\n\nt1 = time.time()\n# Filter on played_ratio_pct >= 50 (positive listens)\nmask = pc.greater_equal(table['played_ratio_pct'], 50)\nfiltered = table.filter(mask)\nprint(f'Filtered to positive listens in {time.time() - t1:.2f} s, rows: {len(filtered)}')\n\""
}<untrusted_context>
Output: Read parquet table in 0.65 s, rows: 43484159
Filtered to positive listens in 0.57 s, rows: 27500755
Process Group PGID: 1915
</untrusted_context>Achieving PyArrow Efficiency
I'm astounded by PyArrow's in-memory speed; loading and filtering a massive 43.4 million row table took mere fractions of a second. The memory footprint is incredibly light, around 200-300MB, easily accommodated by my current setup.
{
"command": "python3 -c \"\nimport pyarrow.parquet as pq\nimport pyarrow.compute as pc\nimport numpy as np\nimport pandas as pd\n\ntable = pq.read_table('/task/data/train_listens.parquet', columns=['uid', 'item_id', 'played_ratio_pct', 'is_organic', 'timestamp'])\ncatalog = pd.read_parquet('/task/data/candidate_catalog.parquet')\ncatalog_items = set(catalog.item_id)\n\ntargets = pd.read_parquet('/task/data/target_users.parquet')\ntarget_uids = set(targets.uid)\n\nprint('Target uids count:', len(target_uids))\nprint('Catalog items count:', len(catalog_items))\n\n# Let's filter table to target uids and catalog items\nmask = pc.and_([\n pc.is_in(table['uid'], pa.array(list(target_uids))),\n pc.is_in(table['item_id'], pa.array(list(catalog_items)))\n])\n\"",
"description": "Check pyarrow array filtering"
}<untrusted_context>
Output: Traceback (most recent call last):
File "<string>", line 19, in <module>
NameError: name 'pa' is not defined. Did you mean: 'pq'?
Target uids count: 8456
Catalog items count: 30000
Exit Code: 1
Process Group PGID: 1937
</untrusted_context>{
"command": "python3 -c \"\nimport pyarrow as pa\nimport pyarrow.parquet as pq\nimport pyarrow.compute as pc\nimport numpy as np\nimport pandas as pd\n\ntable = pq.read_table('/task/data/train_listens.parquet', columns=['uid', 'item_id', 'played_ratio_pct', 'is_organic', 'timestamp'])\ncatalog = pd.read_parquet('/task/data/candidate_catalog.parquet')\ncatalog_items = pa.array(catalog.item_id.values, type=pa.uint32())\n\ntargets = pd.read_parquet('/task/data/target_users.parquet')\ntarget_uids = pa.array(targets.uid.values, type=pa.uint32())\n\nmask = pc.is_in(table['uid'], target_uids)\ntarget_listens = table.filter(mask)\nprint('Target users listens total rows:', len(target_listens))\n\nmask_catalog = pc.is_in(target_listens['item_id'], catalog_items)\ncatalog_target_listens = target_listens.filter(mask_catalog)\nprint('Target users listens on catalog items:', len(catalog_target_listens))\n\n# Convert to pandas\ndf = catalog_target_listens.to_pandas()\nprint('Number of unique target users in df:', df.uid.nunique())\nprint('Number of unique catalog items in df:', df.item_id.nunique())\n\"",
"description": "Inspect target user listens on catalog items"
}<untrusted_context>
Output: Target users listens total rows: 43222297
Target users listens on catalog items: 31062540
Number of unique target users in df: 8452
Number of unique catalog items in df: 30000
Process Group PGID: 1959
</untrusted_context>Verifying User and Catalog Data
I've confirmed that nearly all target users and catalog items are accounted for in the target listens. The next step involves investigating the interaction data across the catalog items.
Analyzing User Preferences
I've confirmed that the target users constitute an overwhelming majority of the listens in the dataset. My focus has now shifted to defining user taste through interactions like likes, strong listens, and recency, and exploring item similarity via co-occurrence patterns.
{
"command": "python3 -c \"\nimport pandas as pd\nimport numpy as np\n\n# Let's count unique (user, item) pairs in train_likes and train_listens\nval_pref = pd.read_parquet('/task/data/validation_preference_state.parquet')\nprint('Val pref pairs:', len(val_pref))\n\ntrain_likes = pd.read_parquet('/task/data/train_likes.parquet')\nprint('Train likes pairs:', len(train_likes.drop_duplicates(['uid', 'item_id'])))\n\"",
"description": "Check unique user-item pairs in likes and preference state"
}<untrusted_context>
Output: Val pref pairs: 469124
Train likes pairs: 785069
Process Group PGID: 1981
</untrusted_context>{
"description": "Count unique positive listen user-item pairs",
"command": "python3 -c \"\nimport pyarrow.parquet as pq\nimport pyarrow.compute as pc\nimport numpy as np\nimport pandas as pd\n\ntable = pq.read_table('/task/data/train_listens.parquet', columns=['uid', 'item_id', 'played_ratio_pct'])\npos_mask = pc.greater_equal(table['played_ratio_pct'], 50)\npos_table = table.filter(pos_mask)\ndf_pairs = pos_table.to_pandas()[['uid', 'item_id']].drop_duplicates()\nprint('Unique (user, item) positive listen pairs:', len(df_pairs))\n\""
}<untrusted_context>
Output: Unique (user, item) positive listen pairs: 8471966
Process Group PGID: 1998
</untrusted_context>Exploring Recommendation Strategies
I'm currently diving into how recommendation algorithms tackle the vast number of unique pairs generated. My focus is on analyzing common methods like Matrix Factorization techniques, including Truncated SVD, SVD, and ALS variants.
{
"description": "Benchmark matrix multiplication and top100 extraction",
"command": "python3 -c \"\nimport numpy as np\nimport time\n\nU = np.random.randn(8456, 128).astype(np.float32)\nV = np.random.randn(30000, 128).astype(np.float32)\n\nt0 = time.time()\nscores = U @ V.T\nt1 = time.time()\nprint(f'Full matrix multiplication 8456x128 @ 128x30000 took {t1-t0:.3f} s, shape {scores.shape}, memory {scores.nbytes / 1e6:.1f} MB')\n\n# How fast is masked top 100 retrieval?\nval_npz = np.load('/task/data/validation_eligible_candidates.npz')\nunpacked = np.unpackbits(val_npz['packed_eligible'], axis=1, count=30000, bitorder='little')\n\nt2 = time.time()\n# Set ineligible to -inf\nscores[~unpacked.astype(bool)] = -np.inf\n# Partition top 100 per user\ntop100_idx = np.argpartition(-scores, 100, axis=1)[:, :100]\nt3 = time.time()\nprint(f'Masking and top100 argpartition for 8456 users took {t3-t2:.3f} s')\n\""
}<untrusted_context>
Output: Full matrix multiplication 8456x128 @ 128x30000 took 0.358 s, shape (8456, 30000), memory 1014.7 MB
Masking and top100 argpartition for 8456 users took 1.144 s
Process Group PGID: 2024
</untrusted_context>Exploring Collaborative Filtering
I've just confirmed that our full inference process for all users and candidates completes in a swift 1.5 seconds. My current focus is on identifying the optimal collaborative filtering and ranking model. I'm diving into established implicit recommendation models, specifically starting with Implicit Alternating Least Squares, to see how well they perform for music discovery.
Testing EASE Matrix Inversion
I'm evaluating the feasibility of inverting a 30,000x30,000 matrix for the EASE model, which is estimated to require 3.6 GB of memory. My next step is to explore how to efficiently solve this linear system to implement EASE.
Evaluating EASE Model Scaling
I've shifted focus to User-User EASE after realizing the Item-Item EASE matrix inversion is computationally prohibitive on current hardware. An 8456x8456 matrix is manageable, requiring ~286 MB and completing inversion in ~3 seconds. I'm now exploring combining this with other approaches like SVD, popularity, and explicit preferences.
{
"description": "Inspect listen timestamp distribution over time windows",
"command": "python3 -c \"\nimport pyarrow.parquet as pq\nimport pyarrow.compute as pc\nimport numpy as np\nimport pandas as pd\n\ntable = pq.read_table('/task/data/train_listens.parquet', columns=['timestamp', 'is_organic', 'played_ratio_pct'])\ntimestamps = table['timestamp'].to_numpy()\nprint('Train listens timestamps: min', timestamps.min(), 'max', timestamps.max())\ncutoff = 24786800\nday = 86400\nfor days in [7, 14, 30, 90, 180, 365]:\n cnt = np.sum(timestamps >= (cutoff - days * day))\n print(f'Past {days:3d} days: {cnt:10d} listens ({cnt/len(timestamps)*100:.1f}%)')\n\""
}<untrusted_context>
Output: Train listens timestamps: min 0 max 24786795
Past 7 days: 1491699 listens (3.4%)
Past 14 days: 2920227 listens (6.7%)
Past 30 days: 6126103 listens (14.1%)
Past 90 days: 16769274 listens (38.6%)
Past 180 days: 30187038 listens (69.4%)
Past 365 days: 43484159 listens (100.0%)
Process Group PGID: 2034
</untrusted_context>Analyzing Listen Counts
My current focus is on understanding listen volume trends across different timeframes. I've noted that the past 7 days and the validation window show remarkably similar listen counts, suggesting a stable recent period. The 30-day and 90-day figures provide broader context for this recent stability.
{
"command": "python3 -c \"\nimport sys\nsys.path.append('/task')\nimport pandas as pd\nimport numpy as np\nfrom ranking_evaluation import CandidateSet\nimport evaluate_public\n\neligibility = CandidateSet('/task/data/validation_eligible_candidates.npz')\ntargets_frame = pd.read_parquet('/task/data/target_users.parquet')\ntargets = set(targets_frame.uid.astype(int))\ngraded = evaluate_public.validation_truth(eligibility, targets)\n\ncatalog = pd.read_parquet('/task/data/candidate_catalog.parquet').set_index('item_id')\n\nall_gt_items = []\nfor u, items in graded.items():\n for it, g in items.items():\n all_gt_items.append((u, it, g, catalog.loc[it, 'popularity_rank'] if it in catalog.index else -1))\n\ndf_gt = pd.DataFrame(all_gt_items, columns=['uid', 'item_id', 'grade', 'pop_rank'])\nprint('GT popularity rank summary:')\nprint(df_gt['pop_rank'].describe(percentiles=[0.1, 0.25, 0.5, 0.75, 0.9]))\n\nprint('\\nBreakdown by grade:')\nprint(df_gt.groupby('grade')['pop_rank'].describe(percentiles=[0.25, 0.5, 0.75]))\n\"",
"description": "Analyze popularity rank of validation ground truth items"
}<untrusted_context>
Output: GT popularity rank summary:
count 37769.000000
mean 8463.087744
std 8117.953262
min 1.000000
10% 428.000000
25% 1733.000000
50% 5622.000000
75% 13374.000000
90% 21680.200000
max 30000.000000
Name: pop_rank, dtype: float64
Breakdown by grade:
count mean std ... 50% 75% max
grade ...
1 33352.0 8406.287659 8124.885100 ... 5545.0 13274.0 30000.0
3 4417.0 8891.975323 8053.409263 ... 6371.0 14181.0 29989.0
[2 rows x 8 columns]
Process Group PGID: 2058
</untrusted_context>Analyzing Ranking Performance
I've been examining the distribution of ground truth items within the ranked list. The median is at 5,622, with 75% below 13,374 and 10% above 21,680. This clearly indicates that a simple popularity model, restricted to top 100 items, will significantly underperform due to the broad discovery patterns observed.
Exploring Interaction Data
I'm now considering how to construct a user-item interaction matrix and evaluate collaborative filtering. My focus is on leveraging validation_preference_state, train_likes, and train_listens, particularly distinguishing organic listens and applying time-weighting to account for recency.
{
"description": "Check indexing and build preference matrix prototype",
"command": "python3 -c \"\nimport sys\nsys.path.append('/task')\nimport time\nimport numpy as np\nimport pandas as pd\nfrom scipy import sparse\nfrom sklearn.decomposition import TruncatedSVD\n\nprint('Starting SVD prototype...')\nt0 = time.time()\n\n# 1. Load target users and catalog items\ntargets = pd.read_parquet('/task/data/target_users.parquet')\ncatalog = pd.read_parquet('/task/data/candidate_catalog.parquet')\n\nuid_to_idx = {uid: i for i, uid in enumerate(targets.uid.values)}\nitem_to_idx = {item: i for i, item in enumerate(catalog.item_id.values)}\n\nn_users = len(targets)\nn_items = len(catalog)\nprint(f'n_users: {n_users}, n_items: {n_items}')\n\n# 2. Load validation preference state (explicit likes)\nval_pref = pd.read_parquet('/task/data/validation_preference_state.parquet')\nval_likes = val_pref[val_pref.state == 'liked']\nval_dislikes = val_pref[val_pref.state == 'disliked']\n\npref_u = [uid_to_idx[u] for u in val_likes.uid if u in uid_to_idx and val_likes.item_id.iloc[0] in item_to_idx] # wait, fix indexing\npref_rows = []\npref_cols = []\npref_vals = []\nfor u, it in zip(val_likes.uid, val_likes.item_id):\n if u in uid_to_idx and it in item_to_idx:\n pref_rows.append(uid_to_idx[u])\n pref_cols.append(item_to_idx[it])\n pref_vals.append(5.0)\n\nfor u, it in zip(val_dislikes.uid, val_dislikes.item_id):\n if u in uid_to_idx and it in item_to_idx:\n pref_rows.append(uid_to_idx[u])\n pref_cols.append(item_to_idx[it])\n pref_vals.append(-3.0)\n\nprint(f'Preference entries: {len(pref_rows)} in {time.time() - t0:.2f} s')\n\""
}<untrusted_context>
Output: Starting SVD prototype...
n_users: 8456, n_items: 30000
Preference entries: 469124 in 3.30 s
Process Group PGID: 2078
</untrusted_context>Analyzing Train Listens
I'm examining the train_listens dataset, noting its substantial 43.4 million rows and columns like uid, item_id, played_ratio_pct, is_organic, and timestamp. My next step is to determine the optimal processing approach for this data.
{
"description": "Test filtering and loading train_listens into pandas",
"command": "python3 -c \"\nimport pyarrow.parquet as pq\nimport pyarrow.compute as pc\nimport pyarrow as pa\nimport numpy as np\nimport pandas as pd\nimport time\n\nt0 = time.time()\ntargets = pd.read_parquet('/task/data/target_users.parquet')\ntarget_uids = pa.array(targets.uid.values, type=pa.uint32())\ncatalog = pd.read_parquet('/task/data/candidate_catalog.parquet')\ncatalog_items = pa.array(catalog.item_id.values, type=pa.uint32())\n\n# Read only columns needed\ntable = pq.read_table('/task/data/train_listens.parquet', columns=['uid', 'item_id', 'timestamp', 'played_ratio_pct', 'is_organic'])\nprint(f'Read table in {time.time() - t0:.2f} s')\n\nt1 = time.time()\n# Filter to target users and catalog items and positive listens (>= 50%)\n# Or what about listens in the past 120 days?\ncutoff = 24786800\nmask = pc.and_([\n pc.is_in(table['uid'], target_uids),\n pc.is_in(table['item_id'], catalog_items),\n pc.greater_equal(table['played_ratio_pct'], 50)\n])\nfiltered = table.filter(mask)\nprint(f'Filtered to positive target listens in {time.time() - t1:.2f} s, count: {len(filtered)}')\n\ndf = filtered.to_pandas()\nprint(f'Converted to pandas in {time.time() - t1:.2f} s')\nprint(df.head())\n\""
}<untrusted_context>
Output: Traceback (most recent call last):
File "<string>", line 23, in <module>
File "/opt/task-venv/lib/python3.12/site-packages/pyarrow/compute.py", line 241, in wrapper
raise TypeError(
TypeError: and_ takes 2 positional argument(s), but 1 were given
Read table in 0.41 s
Exit Code: 1
Process Group PGID: 2102
</untrusted_context>Refining Boolean Expressions
I'm currently optimizing how I construct complex boolean masks in PyArrow compute. Instead of chaining multiple pc.and_ calls, I'm exploring using a more concise syntax, like the & operator directly with pc.is_in and pc.greater_equal for efficiency.
{
"description": "Filter positive target listens with pyarrow compute",
"command": "python3 -c \"\nimport pyarrow.parquet as pq\nimport pyarrow.compute as pc\nimport pyarrow as pa\nimport numpy as np\nimport pandas as pd\nimport time\n\nt0 = time.time()\ntargets = pd.read_parquet('/task/data/target_users.parquet')\ntarget_uids = pa.array(targets.uid.values, type=pa.uint32())\ncatalog = pd.read_parquet('/task/data/candidate_catalog.parquet')\ncatalog_items = pa.array(catalog.item_id.values, type=pa.uint32())\n\ntable = pq.read_table('/task/data/train_listens.parquet', columns=['uid', 'item_id', 'timestamp', 'played_ratio_pct', 'is_organic'])\nprint(f'Read table in {time.time() - t0:.2f} s')\n\nt1 = time.time()\nmask = pc.is_in(table['uid'], target_uids)\nmask = pc.and_(mask, pc.is_in(table['item_id'], catalog_items))\nmask = pc.and_(mask, pc.greater_equal(table['played_ratio_pct'], 50))\nfiltered = table.filter(mask)\nprint(f'Filtered to positive target listens in {time.time() - t1:.2f} s, count: {len(filtered)}')\n\ndf = filtered.to_pandas()\nprint(f'Converted to pandas in {time.time() - t1:.2f} s, memory: {df.memory_usage().sum() / 1e6:.1f} MB')\nprint(df.head())\n\""
}<untrusted_context>
Output: Read table in 0.40 s
Filtered to positive target listens in 1.30 s, count: 19959853
Converted to pandas in 1.34 s, memory: 299.4 MB
uid item_id timestamp played_ratio_pct is_organic
0 100 8326270 39420 100 0
1 100 286361 39625 100 0
2 100 732449 40110 100 0
3 100 7849270 40380 100 0
4 100 1449307 40640 100 0
Process Group PGID: 2124
</untrusted_context>Analyzing Listener Behaviors
I'm currently delving into listener interaction data, specifically investigating which signals best predict future organic discovery. My immediate focus is on analyzing the validation window metrics like val_listens and val_likes to uncover these predictive patterns.
Testing Collaborative Filtering
My current focus is on evaluating collaborative filtering for predicting organic discovery. I'm constructing a user-item interaction matrix using a weighted approach that incorporates likes, high-percentage organic listens, and other listen data, while also considering dislikes as negative signals. This will allow me to test direct collaborative filtering principles.
Exploring Collaborative Filtering Duality
I've confirmed the mathematical equivalence between user-user and item-item linear collaborative filtering. This realization streamlines our approach, allowing us to leverage the associative property for efficient computation and explore non-linear transformations on user embeddings.
Implementing Collaborative Filtering
I'm now implementing and testing various collaborative filtering techniques, including cosine similarity, EASE, and KNN. My current focus is on measuring their performance using a dedicated evaluation script, as the EASE model inversion appears to be computationally feasible.
{
"description": "Build and evaluate User-User Collaborative Filtering prototype",
"command": "python3 -c \"\nimport sys\nsys.path.append('/task')\nimport time\nimport numpy as np\nimport pandas as pd\nfrom scipy import sparse\nimport pyarrow.parquet as pq\nimport pyarrow.compute as pc\nimport pyarrow as pa\nfrom ranking_evaluation import CandidateSet, validate_ranking\n\nt0 = time.time()\nprint('Loading targets, catalog, preferences...')\ntargets = pd.read_parquet('/task/data/target_users.parquet')\ncatalog = pd.read_parquet('/task/data/candidate_catalog.parquet')\nn_users = len(targets)\nn_items = len(catalog)\n\nuid_to_idx = {uid: i for i, uid in enumerate(targets.uid.values)}\nitem_to_idx = {item: i for i, item in enumerate(catalog.item_id.values)}\n\n# Load validation preferences (likes)\nval_pref = pd.read_parquet('/task/data/validation_preference_state.parquet')\nlikes = val_pref[val_pref.state == 'liked']\n\n# Load train listens (positive listens, played_ratio >= 50)\ntable = pq.read_table('/task/data/train_listens.parquet', columns=['uid', 'item_id', 'timestamp', 'played_ratio_pct', 'is_organic'])\ntarget_uids = pa.array(targets.uid.values, type=pa.uint32())\ncatalog_items = pa.array(catalog.item_id.values, type=pa.uint32())\n\nmask = pc.is_in(table['uid'], target_uids)\nmask = pc.and_(mask, pc.is_in(table['item_id'], catalog_items))\nmask = pc.and_(mask, pc.greater_equal(table['played_ratio_pct'], 50))\nfiltered = table.filter(mask).to_pandas()\nprint(f'Data loaded in {time.time() - t0:.2f} s. Positive listens: {len(filtered)}')\n\n# Build user-item weights\n# Group listens by (uid, item_id)\n# Recency weighting: cutoff = 24786800, half-life = 90 days = 7776000 sec\ncutoff = 24786800\nhalf_life = 90 * 86400\nfiltered['weight'] = np.exp(-(cutoff - filtered['timestamp']) / half_life)\n# Boost organic listens\nfiltered['weight'] *= np.where(filtered['is_organic'] == 1, 2.0, 1.0)\n# Boost completion >= 80%\nfiltered['weight'] *= np.where(filtered['played_ratio_pct'] >= 80, 1.5, 1.0)\n\n# Aggregate listens per (uid, item_id)\nagg_listens = filtered.groupby(['uid', 'item_id'])['weight'].sum().reset_index()\n# Cap weight per (uid, item_id) to avoid heavy repeat skew (e.g. log1p)\nagg_listens['weight'] = np.log1p(agg_listens['weight'])\n\n# Combine with explicit likes: likes get an additional boost\nagg_likes = likes[['uid', 'item_id']].copy()\nagg_likes['weight'] = 3.0 # strong positive\n\ncombined = pd.concat([agg_listens, agg_likes]).groupby(['uid', 'item_id'])['weight'].sum().reset_index()\nprint(f'Combined interaction entries: {len(combined)}')\n\n# Build sparse matrix R\nrows = combined['uid'].map(uid_to_idx).values\ncols = combined['item_id'].map(item_to_idx).values\ndata = combined['weight'].values.astype(np.float32)\n\nR = sparse.csr_matrix((data, (rows, cols)), shape=(n_users, n_items))\nprint(f'Sparse matrix R built: shape {R.shape}, nnz {R.nnz}')\n\n# Normalize R (L2 row normalization for cosine similarity)\nrow_norms = sparse.linalg.norm(R, axis=1)\nrow_norms[row_norms == 0] = 1.0\nR_norm = R.multiply(1.0 / row_norms[:, np.newaxis]).tocsr()\n\n# Compute User-User Cosine Similarity: S = R_norm @ R_norm.T (8456 x 8456)\nt1 = time.time()\nS = (R_norm @ R_norm.T).toarray()\nprint(f'User-user similarity computed in {time.time() - t1:.2f} s, shape {S.shape}')\n\n# Zero out diagonal\nnp.fill_diagonal(S, 0)\n# Sharpen similarities (e.g. S**2 or S**3 to emphasize top taste neighbors)\nS = np.maximum(S, 0)\nS = np.power(S, 2.0)\n\n# Predict scores: Pred = S @ R_norm or S @ R\nt2 = time.time()\nscores = S @ R.toarray()\nprint(f'Prediction scores computed in {time.time() - t2:.2f} s, shape {scores.shape}')\n\n# Mask ineligible candidates using validation_eligible_candidates.npz\nval_npz = np.load('/task/data/validation_eligible_candidates.npz')\nunpacked = np.unpackbits(val_npz['packed_eligible'], axis=1, count=n_items, bitorder='little')\nscores[~unpacked.astype(bool)] = -np.inf\n\n# Get top 100 per user\nt3 = time.time()\nitem_ids = val_npz['item_ids']\nuids = val_npz['uids']\nrows_sub = []\nfor r in range(n_users):\n user_scores = scores[r]\n # top 100 indices\n top100_idx = np.argpartition(-user_scores, 100)[:100]\n # sort top 100\n sorted_top100 = top100_idx[np.argsort(-user_scores[top100_idx])]\n uid = uids[r]\n for rank, idx in enumerate(sorted_top100, start=1):\n rows_sub.append((uid, item_ids[idx], rank))\n\nsub = pd.DataFrame(rows_sub, columns=['uid', 'item_id', 'rank'])\nsub.to_parquet('/app/solution/val_test_cf.parquet')\nprint(f'Sub built in {time.time() - t3:.2f} s. Ready for evaluation.')\n\" && python3 /task/evaluate_public.py /app/solution/val_test_cf.parquet"
}<untrusted_context>
Output: Loading targets, catalog, preferences...
Data loaded in 1.84 s. Positive listens: 19959853
Combined interaction entries: 5558048
Sparse matrix R built: shape (8456, 30000), nnz 5558048
User-user similarity computed in 5.89 s, shape (8456, 8456)
Prediction scores computed in 19.82 s, shape (8456, 30000)
Sub built in 1.89 s. Ready for evaluation.
{
"model": "/app/solution/val_test_cf.parquet",
"scores": {
"quality_ndcg@10": 0.014732,
"quality_users": 4263
}
}
Process Group PGID: 2146
</untrusted_context>Achieving Early Wins
My initial exploration into User-User CF has yielded promising results. The prototype immediately boosted popularity baseline by a remarkable 37.5%, demonstrating significant potential with just a 30-second pipeline execution.
Refining Predictive Power
I'm now focusing on the underlying mechanics of the User-User CF. It seems the current prediction $\hat{R} = S R$ overemphasizes globally popular items. I'm exploring normalization strategies, like IDF or BM25 weighting, and considering a shift to Item-Item CF, to enhance predictive accuracy and avoid generic recommendations.
{
"description": "Benchmark sparse matrix multiplication for item-item similarity",
"command": "python3 -c \"\nimport numpy as np\nimport scipy.sparse as sp\nimport time\n\n# Suppose R is 8456 x 30000 with 5.5M non-zeros\n# Let's test R.T @ R or chunked item-item\nR = sp.random(8456, 30000, density=5.5e6/(8456*30000), format='csr', dtype=np.float32)\nR_csc = R.tocsc()\n\nt0 = time.time()\n# If we chunk items in blocks of 2000\nscores_sample = R_csc[:, :2000].T @ R_csc\nprint(f'2000 items x 30000 items multiplication took {time.time()-t0:.2f} s')\n\""
}<untrusted_context>
Output: 2000 items x 30000 items multiplication took 1.68 s
Process Group PGID: 2193
</untrusted_context>Optimizing Matrix Multiplication
My focus is now on accelerating matrix multiplication. I've observed impressive speedups, reducing operations from what initially seemed prohibitive to manageable computations. I'm exploring SVD decomposition as a more efficient alternative to direct $R^T R$ calculation, specifically investigating Truncated SVD for its potential in recommender systems.
{
"command": "python3 -c \"\nimport sys\nsys.path.append('/task')\nimport time\nimport numpy as np\nimport pandas as pd\nfrom scipy import sparse\nfrom sklearn.decomposition import TruncatedSVD\nimport pyarrow.parquet as pq\nimport pyarrow.compute as pc\nimport pyarrow as pa\n\nt0 = time.time()\ntargets = pd.read_parquet('/task/data/target_users.parquet')\ncatalog = pd.read_parquet('/task/data/candidate_catalog.parquet')\nn_users = len(targets)\nn_items = len(catalog)\n\nuid_to_idx = {uid: i for i, uid in enumerate(targets.uid.values)}\nitem_to_idx = {item: i for i, item in enumerate(catalog.item_id.values)}\n\nval_pref = pd.read_parquet('/task/data/validation_preference_state.parquet')\nlikes = val_pref[val_pref.state == 'liked']\n\ntable = pq.read_table('/task/data/train_listens.parquet', columns=['uid', 'item_id', 'timestamp', 'played_ratio_pct', 'is_organic'])\ntarget_uids = pa.array(targets.uid.values, type=pa.uint32())\ncatalog_items = pa.array(catalog.item_id.values, type=pa.uint32())\n\nmask = pc.is_in(table['uid'], target_uids)\nmask = pc.and_(mask, pc.is_in(table['item_id'], catalog_items))\nmask = pc.and_(mask, pc.greater_equal(table['played_ratio_pct'], 50))\nfiltered = table.filter(mask).to_pandas()\n\ncutoff = 24786800\nhalf_life = 90 * 86400\nfiltered['weight'] = np.exp(-(cutoff - filtered['timestamp']) / half_life)\nfiltered['weight'] *= np.where(filtered['is_organic'] == 1, 2.0, 1.0)\nfiltered['weight'] *= np.where(filtered['played_ratio_pct'] >= 80, 1.5, 1.0)\n\nagg_listens = filtered.groupby(['uid', 'item_id'])['weight'].sum().reset_index()\nagg_listens['weight'] = np.log1p(agg_listens['weight'])\n\nagg_likes = likes[['uid', 'item_id']].copy()\nagg_likes['weight'] = 3.0\n\ncombined = pd.concat([agg_listens, agg_likes]).groupby(['uid', 'item_id'])['weight'].sum().reset_index()\n\nrows = combined['uid'].map(uid_to_idx).values\ncols = combined['item_id'].map(item_to_idx).values\ndata = combined['weight'].values.astype(np.float32)\n\nR = sparse.csr_matrix((data, (rows, cols)), shape=(n_users, n_items))\n\n# Try BM25 or TF-IDF weighting on R before SVD!\n# In recommender systems, BM25 / TF-IDF transformation of user-item matrix yields massive gains!\n# Let's test BM25 on R:\n# Item frequency: number of users who interacted with item\ndf_item = np.diff(R.tocsc().indptr)\nN = n_users\nidf = np.log((N - df_item + 0.5) / (df_item + 0.5) + 1.0)\n\n# Apply TF-IDF or BM25:\nR_tfidf = R.multiply(idf)\n\nprint('Running TruncatedSVD(n_components=128)...')\nt1 = time.time()\nsvd = TruncatedSVD(n_components=128, random_state=42)\nU_sigma = svd.fit_transform(R_tfidf) # shape: (8456, 128)\nV = svd.components_ # shape: (128, 30000)\nprint(f'SVD fitted in {time.time() - t1:.2f} s, explained variance: {svd.explained_variance_ratio_.sum():.4f}')\n\n# Predict scores\nscores = U_sigma @ V\nprint('Scores shape:', scores.shape)\n\n# Mask ineligible\nval_npz = np.load('/task/data/validation_eligible_candidates.npz')\nunpacked = np.unpackbits(val_npz['packed_eligible'], axis=1, count=n_items, bitorder='little')\nscores[~unpacked.astype(bool)] = -np.inf\n\nitem_ids = val_npz['item_ids']\nuids = val_npz['uids']\nrows_sub = []\nfor r in range(n_users):\n user_scores = scores[r]\n top100_idx = np.argpartition(-user_scores, 100)[:100]\n sorted_top100 = top100_idx[np.argsort(-user_scores[top100_idx])]\n uid = uids[r]\n for rank, idx in enumerate(sorted_top100, start=1):\n rows_sub.append((uid, item_ids[idx], rank))\n\nsub = pd.DataFrame(rows_sub, columns=['uid', 'item_id', 'rank'])\nsub.to_parquet('/app/solution/val_test_svd.parquet')\nprint('SVD sub ready for eval.')\n\" && python3 /task/evaluate_public.py /app/solution/val_test_svd.parquet",
"description": "Test BM25 + TruncatedSVD on validation set"
}<untrusted_context>
Output: Running TruncatedSVD(n_components=128)...
SVD fitted in 5.81 s, explained variance: 0.2978
Scores shape: (8456, 30000)
SVD sub ready for eval.
{
"model": "/app/solution/val_test_svd.parquet",
"scores": {
"quality_ndcg@10": 0.017015,
"quality_users": 4263
}
}
Process Group PGID: 2210
</untrusted_context>Improving Recommendation Scores
I've just achieved a score of 0.017015 with BM25 combined with TruncatedSVD(128), a noticeable improvement over previous methods like popularity and User-User CF, and it processed remarkably fast at just 5.8 seconds. I'm now considering further avenues for enhancement.
Experimenting with SVD Components
I'm actively investigating the impact of varying SVD components, testing values like 64, 128, 192, 256, and 320 on the validation set to optimize performance before refitting on the full training data.
{
"command": "python3 -c \"\nimport sys\nsys.path.append('/task')\nimport time\nimport numpy as np\nimport pandas as pd\nfrom scipy import sparse\nfrom sklearn.decomposition import TruncatedSVD\nimport pyarrow.parquet as pq\nimport pyarrow.compute as pc\nimport pyarrow as pa\nfrom ranking_evaluation import CandidateSet\nimport evaluate_public\n\ntargets = pd.read_parquet('/task/data/target_users.parquet')\ncatalog = pd.read_parquet('/task/data/candidate_catalog.parquet')\nn_users = len(targets)\nn_items = len(catalog)\n\nuid_to_idx = {uid: i for i, uid in enumerate(targets.uid.values)}\nitem_to_idx = {item: i for i, item in enumerate(catalog.item_id.values)}\n\nval_pref = pd.read_parquet('/task/data/validation_preference_state.parquet')\nlikes = val_pref[val_pref.state == 'liked']\n\ntable = pq.read_table('/task/data/train_listens.parquet', columns=['uid', 'item_id', 'timestamp', 'played_ratio_pct', 'is_organic'])\ntarget_uids = pa.array(targets.uid.values, type=pa.uint32())\ncatalog_items = pa.array(catalog.item_id.values, type=pa.uint32())\n\nmask = pc.is_in(table['uid'], target_uids)\nmask = pc.and_(mask, pc.is_in(table['item_id'], catalog_items))\nmask = pc.and_(mask, pc.greater_equal(table['played_ratio_pct'], 50))\nfiltered = table.filter(mask).to_pandas()\n\ncutoff = 24786800\nhalf_life = 90 * 86400\nfiltered['weight'] = np.exp(-(cutoff - filtered['timestamp']) / half_life)\nfiltered['weight'] *= np.where(filtered['is_organic'] == 1, 2.0, 1.0)\nfiltered['weight'] *= np.where(filtered['played_ratio_pct'] >= 80, 1.5, 1.0)\n\nagg_listens = filtered.groupby(['uid', 'item_id'])['weight'].sum().reset_index()\nagg_listens['weight'] = np.log1p(agg_listens['weight'])\n\nagg_likes = likes[['uid', 'item_id']].copy()\nagg_likes['weight'] = 3.0\n\ncombined = pd.concat([agg_listens, agg_likes]).groupby(['uid', 'item_id'])['weight'].sum().reset_index()\n\nrows = combined['uid'].map(uid_to_idx).values\ncols = combined['item_id'].map(item_to_idx).values\ndata = combined['weight'].values.astype(np.float32)\n\nR = sparse.csr_matrix((data, (rows, cols)), shape=(n_users, n_items))\n\ndf_item = np.diff(R.tocsc().indptr)\nN = n_users\nidf = np.log((N - df_item + 0.5) / (df_item + 0.5) + 1.0)\nR_tfidf = R.multiply(idf).tocsr()\n\nval_npz = np.load('/task/data/validation_eligible_candidates.npz')\nunpacked = np.unpackbits(val_npz['packed_eligible'], axis=1, count=n_items, bitorder='little')\neligibility = CandidateSet('/task/data/validation_eligible_candidates.npz')\ntargets_set = set(targets.uid.astype(int))\ngraded = evaluate_public.validation_truth(eligibility, targets_set)\n\nitem_ids = val_npz['item_ids']\nuids = val_npz['uids']\n\ndef eval_scores(raw_scores):\n s = raw_scores.copy()\n s[~unpacked.astype(bool)] = -np.inf\n ranked = {}\n for r in range(n_users):\n u_sc = s[r]\n top100 = np.argpartition(-u_sc, 100)[:100]\n top100_sorted = top100[np.argsort(-u_sc[top100])]\n ranked[int(uids[r])] = list(map(int, item_ids[top100_sorted]))\n return evaluate_public.score(ranked, graded)['quality_ndcg@10']\n\nfor k in [64, 128, 192, 256, 320]:\n t = time.time()\n svd = TruncatedSVD(n_components=k, random_state=42)\n U_s = svd.fit_transform(R_tfidf)\n V = svd.components_\n pred = U_s @ V\n ndcg = eval_scores(pred)\n print(f'k={k:3d}: NDCG@10 = {ndcg:.6f} (time: {time.time()-t:.1f}s)')\n\"",
"description": "Evaluate TruncatedSVD with varying n_components"
}<untrusted_context>
Output: k= 64: NDCG@10 = 0.014744 (time: 6.1s)
k=128: NDCG@10 = 0.017015 (time: 8.1s)
k=192: NDCG@10 = 0.017975 (time: 12.5s)
k=256: NDCG@10 = 0.017798 (time: 15.6s)
k=320: NDCG@10 = 0.018002 (time: 20.2s)
Process Group PGID: 2257
</untrusted_context>Exploring Dislike Influence
I'm now investigating how incorporating train_dislikes might refine our results. Specifically, I'm exploring strategies like assigning negative weights to disliked items or modifying their neighborhood influence to potentially improve performance beyond the current k=192 and k=320 outcomes.
Testing EASE Variants
I'm now exploring the impact of different half_life durations on our models, alongside investigating the effectiveness of user row normalization with TF-IDF/LSI. Additionally, I plan to evaluate User-User EASE with various lambda values and consider incorporating non-target users into item embeddings.
{
"command": "python3 -c \"\nimport sys\nsys.path.append('/task')\nimport time\nimport numpy as np\nimport pandas as pd\nfrom scipy import sparse\nimport pyarrow.parquet as pq\nimport pyarrow.compute as pc\nimport pyarrow as pa\nfrom ranking_evaluation import CandidateSet\nimport evaluate_public\n\ntargets = pd.read_parquet('/task/data/target_users.parquet')\ncatalog = pd.read_parquet('/task/data/candidate_catalog.parquet')\nn_users = len(targets)\nn_items = len(catalog)\n\nuid_to_idx = {uid: i for i, uid in enumerate(targets.uid.values)}\nitem_to_idx = {item: i for i, item in enumerate(catalog.item_id.values)}\n\nval_pref = pd.read_parquet('/task/data/validation_preference_state.parquet')\nlikes = val_pref[val_pref.state == 'liked']\n\ntable = pq.read_table('/task/data/train_listens.parquet', columns=['uid', 'item_id', 'timestamp', 'played_ratio_pct', 'is_organic'])\ntarget_uids = pa.array(targets.uid.values, type=pa.uint32())\ncatalog_items = pa.array(catalog.item_id.values, type=pa.uint32())\n\nmask = pc.is_in(table['uid'], target_uids)\nmask = pc.and_(mask, pc.is_in(table['item_id'], catalog_items))\nmask = pc.and_(mask, pc.greater_equal(table['played_ratio_pct'], 50))\nfiltered = table.filter(mask).to_pandas()\n\ncutoff = 24786800\nhalf_life = 90 * 86400\nfiltered['weight'] = np.exp(-(cutoff - filtered['timestamp']) / half_life)\nfiltered['weight'] *= np.where(filtered['is_organic'] == 1, 2.0, 1.0)\nfiltered['weight'] *= np.where(filtered['played_ratio_pct'] >= 80, 1.5, 1.0)\n\nagg_listens = filtered.groupby(['uid', 'item_id'])['weight'].sum().reset_index()\nagg_listens['weight'] = np.log1p(agg_listens['weight'])\n\nagg_likes = likes[['uid', 'item_id']].copy()\nagg_likes['weight'] = 3.0\n\ncombined = pd.concat([agg_listens, agg_likes]).groupby(['uid', 'item_id'])['weight'].sum().reset_index()\n\nrows = combined['uid'].map(uid_to_idx).values\ncols = combined['item_id'].map(item_to_idx).values\ndata = combined['weight'].values.astype(np.float32)\n\nR = sparse.csr_matrix((data, (rows, cols)), shape=(n_users, n_items))\n\ndf_item = np.diff(R.tocsc().indptr)\nN = n_users\nidf = np.log((N - df_item + 0.5) / (df_item + 0.5) + 1.0)\nR_tfidf = R.multiply(idf).tocsr()\n\nval_npz = np.load('/task/data/validation_eligible_candidates.npz')\nunpacked = np.unpackbits(val_npz['packed_eligible'], axis=1, count=n_items, bitorder='little')\neligibility = CandidateSet('/task/data/validation_eligible_candidates.npz')\ntargets_set = set(targets.uid.astype(int))\ngraded = evaluate_public.validation_truth(eligibility, targets_set)\n\nitem_ids = val_npz['item_ids']\nuids = val_npz['uids']\n\ndef eval_scores(raw_scores):\n s = raw_scores.copy()\n s[~unpacked.astype(bool)] = -np.inf\n ranked = {}\n for r in range(n_users):\n u_sc = s[r]\n top100 = np.argpartition(-u_sc, 100)[:100]\n top100_sorted = top100[np.argsort(-u_sc[top100])]\n ranked[int(uids[r])] = list(map(int, item_ids[top100_sorted]))\n return evaluate_public.score(ranked, graded)['quality_ndcg@10']\n\nprint('Computing G = R_tfidf @ R_tfidf.T...')\nt0 = time.time()\n# L2-normalize rows of R_tfidf\nfrom sklearn.preprocessing import normalize\nR_norm = normalize(R_tfidf, norm='l2', axis=1)\nG = (R_norm @ R_norm.T).toarray()\nprint(f'Gram matrix G computed in {time.time() - t0:.2f} s')\n\nfor reg in [10.0, 50.0, 100.0, 500.0, 1000.0]:\n t1 = time.time()\n G_reg = G.copy()\n G_reg[np.diag_indices_from(G_reg)] += reg\n P = np.linalg.inv(G_reg)\n B = -P / np.diag(P)[:, None]\n np.fill_diagonal(B, 0)\n pred = B @ R_tfidf.toarray()\n ndcg = eval_scores(pred)\n print(f'EASE reg={reg:6.1f}: NDCG@10 = {ndcg:.6f} (time: {time.time()-t1:.1f}s)')\n\"",
"description": "Test User-User EASE with various regularization parameters"
}<untrusted_context>
Output: /usr/bin/bash: line 101: 2288 Killed python3 -c "
import sys
sys.path.append('/task')
import time
import numpy as np
import pandas as pd
from scipy import sparse
import pyarrow.parquet as pq
import pyarrow.compute as pc
import pyarrow as pa
from ranking_evaluation import CandidateSet
import evaluate_public
targets = pd.read_parquet('/task/data/target_users.parquet')
catalog = pd.read_parquet('/task/data/candidate_catalog.parquet')
n_users = len(targets)
n_items = len(catalog)
uid_to_idx = {uid: i for i, uid in enumerate(targets.uid.values)}
item_to_idx = {item: i for i, item in enumerate(catalog.item_id.values)}
val_pref = pd.read_parquet('/task/data/validation_preference_state.parquet')
likes = val_pref[val_pref.state == 'liked']
table = pq.read_table('/task/data/train_listens.parquet', columns=['uid', 'item_id', 'timestamp', 'played_ratio_pct', 'is_organic'])
target_uids = pa.array(targets.uid.values, type=pa.uint32())
catalog_items = pa.array(catalog.item_id.values, type=pa.uint32())
mask = pc.is_in(table['uid'], target_uids)
mask = pc.and_(mask, pc.is_in(table['item_id'], catalog_items))
mask = pc.and_(mask, pc.greater_equal(table['played_ratio_pct'], 50))
filtered = table.filter(mask).to_pandas()
cutoff = 24786800
half_life = 90 * 86400
filtered['weight'] = np.exp(-(cutoff - filtered['timestamp']) / half_life)
filtered['weight'] *= np.where(filtered['is_organic'] == 1, 2.0, 1.0)
filtered['weight'] *= np.where(filtered['played_ratio_pct'] >= 80, 1.5, 1.0)
agg_listens = filtered.groupby(['uid', 'item_id'])['weight'].sum().reset_index()
agg_listens['weight'] = np.log1p(agg_listens['weight'])
agg_likes = likes[['uid', 'item_id']].copy()
agg_likes['weight'] = 3.0
combined = pd.concat([agg_listens, agg_likes]).groupby(['uid', 'item_id'])['weight'].sum().reset_index()
rows = combined['uid'].map(uid_to_idx).values
cols = combined['item_id'].map(item_to_idx).values
data = combined['weight'].values.astype(np.float32)
R = sparse.csr_matrix((data, (rows, cols)), shape=(n_users, n_items))
df_item = np.diff(R.tocsc().indptr)
N = n_users
idf = np.log((N - df_item + 0.5) / (df_item + 0.5) + 1.0)
R_tfidf = R.multiply(idf).tocsr()
val_npz = np.load('/task/data/validation_eligible_candidates.npz')
unpacked = np.unpackbits(val_npz['packed_eligible'], axis=1, count=n_items, bitorder='little')
eligibility = CandidateSet('/task/data/validation_eligible_candidates.npz')
targets_set = set(targets.uid.astype(int))
graded = evaluate_public.validation_truth(eligibility, targets_set)
item_ids = val_npz['item_ids']
uids = val_npz['uids']
def eval_scores(raw_scores):
s = raw_scores.copy()
s[~unpacked.astype(bool)] = -np.inf
ranked = {}
for r in range(n_users):
u_sc = s[r]
top100 = np.argpartition(-u_sc, 100)[:100]
top100_sorted = top100[np.argsort(-u_sc[top100])]
ranked[int(uids[r])] = list(map(int, item_ids[top100_sorted]))
return evaluate_public.score(ranked, graded)['quality_ndcg@10']
print('Computing G = R_tfidf @ R_tfidf.T...')
t0 = time.time()
# L2-normalize rows of R_tfidf
from sklearn.preprocessing import normalize
R_norm = normalize(R_tfidf, norm='l2', axis=1)
G = (R_norm @ R_norm.T).toarray()
print(f'Gram matrix G computed in {time.time() - t0:.2f} s')
for reg in [10.0, 50.0, 100.0, 500.0, 1000.0]:
t1 = time.time()
G_reg = G.copy()
G_reg[np.diag_indices_from(G_reg)] += reg
P = np.linalg.inv(G_reg)
B = -P / np.diag(P)[:, None]
np.fill_diagonal(B, 0)
pred = B @ R_tfidf.toarray()
ndcg = eval_scores(pred)
print(f'EASE reg={reg:6.1f}: NDCG@10 = {ndcg:.6f} (time: {time.time()-t1:.1f}s)')
"
Exit Code: 137
Process Group PGID: 2286
</untrusted_context>Investigating Resource Issues
I'm currently trying to understand why a process was terminated. It seems the memory demands of converting TF-IDF matrices to dense arrays and subsequent matrix multiplications are leading to out-of-memory errors, specifically exit code 137. I'm exploring optimizations for these steps.
{
"description": "Check psutil availability",
"command": "python3 -c \"\nimport gc, os, psutil\nimport pandas as pd\nimport numpy as np\nimport pyarrow.parquet as pq\n\np = psutil.Process()\nprint('Initial RSS:', p.memory_info().rss / 1e6, 'MB')\n\""
}<untrusted_context>
Output: Traceback (most recent call last):
File "<string>", line 2, in <module>
ModuleNotFoundError: No module named 'psutil'
Exit Code: 1
Process Group PGID: 2315
</untrusted_context>Refining Memory Metrics
I'm currently exploring methods for memory measurement, considering alternatives like /proc/self/status and /proc/meminfo since psutil isn't available. My immediate focus is on extracting VmRSS for more precise data.
Evaluating Event Weights
I am now investigating how to best weigh various user interaction events to predict future organic discovery. My current focus is on analyzing the correlation of explicit likes, dislikes, different levels of organic listens, non-organic listens, and repeat listens against discovery. I'm also exploring the impact of time decay on these interactions.
Tuning Ranking Functions
I'm now deeply engaged in optimizing the IDF component of our ranking function, specifically experimenting with different exponent values ($\gamma$). The aim is to fine-tune the penalty for extremely popular items. I'm also evaluating user vector normalization strategies before and after SVD to mitigate popularity bias in retrieval.
{
"description": "Test recency half-life on validation set",
"command": "python3 -c \"\nimport sys\nsys.path.append('/task')\nimport time\nimport gc\nimport numpy as np\nimport pandas as pd\nfrom scipy import sparse\nfrom sklearn.decomposition import TruncatedSVD\nimport pyarrow.parquet as pq\nimport pyarrow.compute as pc\nimport pyarrow as pa\nfrom ranking_evaluation import CandidateSet\nimport evaluate_public\n\nprint('Loading base data...')\ntargets = pd.read_parquet('/task/data/target_users.parquet')\ncatalog = pd.read_parquet('/task/data/candidate_catalog.parquet')\nn_users = len(targets)\nn_items = len(catalog)\n\nuid_to_idx = {uid: i for i, uid in enumerate(targets.uid.values)}\nitem_to_idx = {item: i for i, item in enumerate(catalog.item_id.values)}\n\nval_pref = pd.read_parquet('/task/data/validation_preference_state.parquet')\nlikes = val_pref[val_pref.state == 'liked']\ndislikes = val_pref[val_pref.state == 'disliked']\n\ntable = pq.read_table('/task/data/train_listens.parquet', columns=['uid', 'item_id', 'timestamp', 'played_ratio_pct', 'is_organic'])\ntarget_uids = pa.array(targets.uid.values, type=pa.uint32())\ncatalog_items = pa.array(catalog.item_id.values, type=pa.uint32())\n\nmask = pc.is_in(table['uid'], target_uids)\nmask = pc.and_(mask, pc.is_in(table['item_id'], catalog_items))\nmask = pc.and_(mask, pc.greater_equal(table['played_ratio_pct'], 50))\nfiltered = table.filter(mask).to_pandas()\ndel table; gc.collect()\n\nval_npz = np.load('/task/data/validation_eligible_candidates.npz')\nunpacked = np.unpackbits(val_npz['packed_eligible'], axis=1, count=n_items, bitorder='little').astype(bool)\neligibility = CandidateSet('/task/data/validation_eligible_candidates.npz')\ntargets_set = set(targets.uid.astype(int))\ngraded = evaluate_public.validation_truth(eligibility, targets_set)\n\nitem_ids = val_npz['item_ids']\nuids = val_npz['uids']\n\ndef run_experiment(half_life_days=90, organic_boost=2.0, strong_boost=1.5, idf_gamma=1.0, k=256, alpha_norm=0.0):\n cutoff = 24786800\n if half_life_days is not None:\n hl = half_life_days * 86400\n w = np.exp(-(cutoff - filtered['timestamp'].values) / hl)\n else:\n w = np.ones(len(filtered), dtype=np.float32)\n \n w *= np.where(filtered['is_organic'].values == 1, organic_boost, 1.0)\n w *= np.where(filtered['played_ratio_pct'].values >= 80, strong_boost, 1.0)\n \n df_w = pd.DataFrame({'uid': filtered['uid'], 'item_id': filtered['item_id'], 'w': w})\n agg_listens = df_w.groupby(['uid', 'item_id'])['w'].sum().reset_index()\n agg_listens['w'] = np.log1p(agg_listens['w'])\n \n agg_likes = likes[['uid', 'item_id']].copy()\n agg_likes['w'] = 3.0\n \n combined = pd.concat([agg_listens, agg_likes]).groupby(['uid', 'item_id'])['w'].sum().reset_index()\n rows = combined['uid'].map(uid_to_idx).values\n cols = combined['item_id'].map(item_to_idx).values\n data = combined['w'].values.astype(np.float32)\n \n R = sparse.csr_matrix((data, (rows, cols)), shape=(n_users, n_items))\n \n if idf_gamma > 0:\n df_item = np.diff(R.tocsc().indptr)\n N = n_users\n idf = np.log((N - df_item + 0.5) / (df_item + 0.5) + 1.0) ** idf_gamma\n R_mat = R.multiply(idf).tocsr()\n else:\n R_mat = R\n \n svd = TruncatedSVD(n_components=k, random_state=42)\n U_s = svd.fit_transform(R_mat).astype(np.float32)\n V = svd.components_.astype(np.float32) # (k, 30000)\n \n if alpha_norm > 0:\n item_norms = np.linalg.norm(V, axis=0) + 1e-8\n V = V / (item_norms ** alpha_norm)\n \n pred = U_s @ V\n pred[~unpacked] = -np.inf\n \n ranked = {}\n for r in range(n_users):\n u_sc = pred[r]\n top100 = np.argpartition(-u_sc, 100)[:100]\n top100_sorted = top100[np.argsort(-u_sc[top100])]\n ranked[int(uids[r])] = list(map(int, item_ids[top100_sorted]))\n \n score = evaluate_public.score(ranked, graded)['quality_ndcg@10']\n return score\n\nprint('Testing half_life_days...')\nfor hl in [30, 60, 90, 180, None]:\n sc = run_experiment(half_life_days=hl, idf_gamma=1.0, k=192)\n print(f'half_life={hl}: NDCG = {sc:.6f}')\n\""
}<untrusted_context>
Output: <string>:52: RuntimeWarning: overflow encountered in exp
Traceback (most recent call last):
File "<string>", line 104, in <module>
File "<string>", line 82, in run_experiment
File "/opt/task-venv/lib/python3.12/site-packages/sklearn/utils/_set_output.py", line 316, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/opt/task-venv/lib/python3.12/site-packages/sklearn/base.py", line 1365, in wrapper
return fit_method(estimator, *args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/opt/task-venv/lib/python3.12/site-packages/sklearn/decomposition/_truncated_svd.py", line 226, in fit_transform
X = validate_data(self, X, accept_sparse=["csr", "csc"], ensure_min_features=2)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/opt/task-venv/lib/python3.12/site-packages/sklearn/utils/validation.py", line 2954, in validate_data
out = check_array(X, input_name="X", **check_params)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/opt/task-venv/lib/python3.12/site-packages/sklearn/utils/validation.py", line 1012, in check_array
array = _ensure_sparse_format(
^^^^^^^^^^^^^^^^^^^^^^
File "/opt/task-venv/lib/python3.12/site-packages/sklearn/utils/validation.py", line 647, in _ensure_sparse_format
_assert_all_finite(
File "/opt/task-venv/lib/python3.12/site-packages/sklearn/utils/validation.py", line 120, in _assert_all_finite
_assert_all_finite_element_wise(
File "/opt/task-venv/lib/python3.12/site-packages/sklearn/utils/validation.py", line 169, in _assert_all_finite_element_wise
raise ValueError(msg_err)
ValueError: Input X contains infinity or a value too large for dtype('float64').
Loading base data...
Testing half_life_days...
Exit Code: 1
Process Group PGID: 2318
</untrusted_context>Investigating Overflow Error
I'm tracking down an overflow encountered in exp error. It seems to stem from the calculation np.exp(-(cutoff - timestamp) / hl). The (cutoff - timestamp) difference is proving quite large, potentially leading to an excessively large negative exponent when divided by hl.
{
"command": "python3 -c \"\nimport sys\nsys.path.append('/task')\nimport time\nimport gc\nimport numpy as np\nimport pandas as pd\nfrom scipy import sparse\nfrom sklearn.decomposition import TruncatedSVD\nimport pyarrow.parquet as pq\nimport pyarrow.compute as pc\nimport pyarrow as pa\nfrom ranking_evaluation import CandidateSet\nimport evaluate_public\n\nprint('Loading base data...')\ntargets = pd.read_parquet('/task/data/target_users.parquet')\ncatalog = pd.read_parquet('/task/data/candidate_catalog.parquet')\nn_users = len(targets)\nn_items = len(catalog)\n\nuid_to_idx = {uid: i for i, uid in enumerate(targets.uid.values)}\nitem_to_idx = {item: i for i, item in enumerate(catalog.item_id.values)}\n\nval_pref = pd.read_parquet('/task/data/validation_preference_state.parquet')\nlikes = val_pref[val_pref.state == 'liked']\ndislikes = val_pref[val_pref.state == 'disliked']\n\ntable = pq.read_table('/task/data/train_listens.parquet', columns=['uid', 'item_id', 'timestamp', 'played_ratio_pct', 'is_organic'])\ntarget_uids = pa.array(targets.uid.values, type=pa.uint32())\ncatalog_items = pa.array(catalog.item_id.values, type=pa.uint32())\n\nmask = pc.is_in(table['uid'], target_uids)\nmask = pc.and_(mask, pc.is_in(table['item_id'], catalog_items))\nmask = pc.and_(mask, pc.greater_equal(table['played_ratio_pct'], 50))\nfiltered = table.filter(mask).to_pandas()\ndel table; gc.collect()\n\nval_npz = np.load('/task/data/validation_eligible_candidates.npz')\nunpacked = np.unpackbits(val_npz['packed_eligible'], axis=1, count=n_items, bitorder='little').astype(bool)\neligibility = CandidateSet('/task/data/validation_eligible_candidates.npz')\ntargets_set = set(targets.uid.astype(int))\ngraded = evaluate_public.validation_truth(eligibility, targets_set)\n\nitem_ids = val_npz['item_ids']\nuids = val_npz['uids']\n\nts_float = filtered['timestamp'].astype(np.float64).values\n\ndef run_experiment(half_life_days=90, organic_boost=2.0, strong_boost=1.5, idf_gamma=1.0, k=192, alpha_norm=0.0):\n cutoff = 24786800.0\n if half_life_days is not None:\n hl = half_life_days * 86400.0\n w = np.exp(-(cutoff - ts_float) / hl).astype(np.float32)\n else:\n w = np.ones(len(filtered), dtype=np.float32)\n \n w *= np.where(filtered['is_organic'].values == 1, organic_boost, 1.0).astype(np.float32)\n w *= np.where(filtered['played_ratio_pct'].values >= 80, strong_boost, 1.0).astype(np.float32)\n \n df_w = pd.DataFrame({'uid': filtered['uid'], 'item_id': filtered['item_id'], 'w': w})\n agg_listens = df_w.groupby(['uid', 'item_id'])['w'].sum().reset_index()\n agg_listens['w'] = np.log1p(agg_listens['w'])\n \n agg_likes = likes[['uid', 'item_id']].copy()\n agg_likes['w'] = 3.0\n \n combined = pd.concat([agg_listens, agg_likes]).groupby(['uid', 'item_id'])['w'].sum().reset_index()\n rows = combined['uid'].map(uid_to_idx).values\n cols = combined['item_id'].map(item_to_idx).values\n data = combined['w'].values.astype(np.float32)\n \n R = sparse.csr_matrix((data, (rows, cols)), shape=(n_users, n_items))\n \n if idf_gamma > 0:\n df_item = np.diff(R.tocsc().indptr)\n N = n_users\n idf = np.log((N - df_item + 0.5) / (df_item + 0.5) + 1.0) ** idf_gamma\n R_mat = R.multiply(idf).tocsr()\n else:\n R_mat = R\n \n svd = TruncatedSVD(n_components=k, random_state=42)\n U_s = svd.fit_transform(R_mat).astype(np.float32)\n V = svd.components_.astype(np.float32)\n \n if alpha_norm > 0:\n item_norms = np.linalg.norm(V, axis=0) + 1e-8\n V = V / (item_norms ** alpha_norm)\n \n pred = U_s @ V\n pred[~unpacked] = -np.inf\n \n ranked = {}\n for r in range(n_users):\n u_sc = pred[r]\n top100 = np.argpartition(-u_sc, 100)[:100]\n top100_sorted = top100[np.argsort(-u_sc[top100])]\n ranked[int(uids[r])] = list(map(int, item_ids[top100_sorted]))\n \n score = evaluate_public.score(ranked, graded)['quality_ndcg@10']\n return score\n\nprint('Testing half_life_days...')\nfor hl in [30, 60, 90, 180, None]:\n t = time.time()\n sc = run_experiment(half_life_days=hl, idf_gamma=1.0, k=192)\n print(f'half_life={hl}: NDCG = {sc:.6f} ({time.time()-t:.1f}s)')\n\"",
"description": "Test half life with correct float casting"
}<untrusted_context>
Output: Loading base data...
Testing half_life_days...
half_life=30: NDCG = 0.030037 (14.7s)
half_life=60: NDCG = 0.026516 (14.6s)
half_life=90: NDCG = 0.025394 (14.6s)
half_life=180: NDCG = 0.022800 (14.6s)
half_life=None: NDCG = 0.020161 (14.8s)
Process Group PGID: 2347
</untrusted_context>Discovering Temporal Relevance
I'm seeing a significant improvement in NDCG with a half-life of 30 days, reaching 0.030037. This indicates music taste exhibits strong temporal locality, with recent listening habits proving far more predictive than older ones or no temporal weighting at all.
{
"command": "python3 -c \"\nimport sys\nsys.path.append('/task')\nimport time\nimport gc\nimport numpy as np\nimport pandas as pd\nfrom scipy import sparse\nfrom sklearn.decomposition import TruncatedSVD\nimport pyarrow.parquet as pq\nimport pyarrow.compute as pc\nimport pyarrow as pa\nfrom ranking_evaluation import CandidateSet\nimport evaluate_public\n\ntargets = pd.read_parquet('/task/data/target_users.parquet')\ncatalog = pd.read_parquet('/task/data/candidate_catalog.parquet')\nn_users = len(targets)\nn_items = len(catalog)\n\nuid_to_idx = {uid: i for i, uid in enumerate(targets.uid.values)}\nitem_to_idx = {item: i for i, item in enumerate(catalog.item_id.values)}\n\nval_pref = pd.read_parquet('/task/data/validation_preference_state.parquet')\nlikes = val_pref[val_pref.state == 'liked']\ndislikes = val_pref[val_pref.state == 'disliked']\n\ntable = pq.read_table('/task/data/train_listens.parquet', columns=['uid', 'item_id', 'timestamp', 'played_ratio_pct', 'is_organic'])\ntarget_uids = pa.array(targets.uid.values, type=pa.uint32())\ncatalog_items = pa.array(catalog.item_id.values, type=pa.uint32())\n\nmask = pc.is_in(table['uid'], target_uids)\nmask = pc.and_(mask, pc.is_in(table['item_id'], catalog_items))\nmask = pc.and_(mask, pc.greater_equal(table['played_ratio_pct'], 50))\nfiltered = table.filter(mask).to_pandas()\ndel table; gc.collect()\n\nval_npz = np.load('/task/data/validation_eligible_candidates.npz')\nunpacked = np.unpackbits(val_npz['packed_eligible'], axis=1, count=n_items, bitorder='little').astype(bool)\neligibility = CandidateSet('/task/data/validation_eligible_candidates.npz')\ntargets_set = set(targets.uid.astype(int))\ngraded = evaluate_public.validation_truth(eligibility, targets_set)\n\nitem_ids = val_npz['item_ids']\nuids = val_npz['uids']\nts_float = filtered['timestamp'].astype(np.float64).values\n\ndef run_experiment(half_life_days=30, organic_boost=2.0, strong_boost=1.5, idf_gamma=1.0, k=192, alpha_norm=0.0):\n cutoff = 24786800.0\n hl = half_life_days * 86400.0\n w = np.exp(-(cutoff - ts_float) / hl).astype(np.float32)\n \n w *= np.where(filtered['is_organic'].values == 1, organic_boost, 1.0).astype(np.float32)\n w *= np.where(filtered['played_ratio_pct'].values >= 80, strong_boost, 1.0).astype(np.float32)\n \n df_w = pd.DataFrame({'uid': filtered['uid'], 'item_id': filtered['item_id'], 'w': w})\n agg_listens = df_w.groupby(['uid', 'item_id'])['w'].sum().reset_index()\n agg_listens['w'] = np.log1p(agg_listens['w'])\n \n agg_likes = likes[['uid', 'item_id']].copy()\n agg_likes['w'] = 3.0\n \n combined = pd.concat([agg_listens, agg_likes]).groupby(['uid', 'item_id'])['w'].sum().reset_index()\n rows = combined['uid'].map(uid_to_idx).values\n cols = combined['item_id'].map(item_to_idx).values\n data = combined['w'].values.astype(np.float32)\n \n R = sparse.csr_matrix((data, (rows, cols)), shape=(n_users, n_items))\n \n if idf_gamma > 0:\n df_item = np.diff(R.tocsc().indptr)\n N = n_users\n idf = np.log((N - df_item + 0.5) / (df_item + 0.5) + 1.0) ** idf_gamma\n R_mat = R.multiply(idf).tocsr()\n else:\n R_mat = R\n \n svd = TruncatedSVD(n_components=k, random_state=42)\n U_s = svd.fit_transform(R_mat).astype(np.float32)\n V = svd.components_.astype(np.float32)\n \n if alpha_norm > 0:\n item_norms = np.linalg.norm(V, axis=0) + 1e-8\n V = V / (item_norms ** alpha_norm)\n \n pred = U_s @ V\n pred[~unpacked] = -np.inf\n \n ranked = {}\n for r in range(n_users):\n u_sc = pred[r]\n top100 = np.argpartition(-u_sc, 100)[:100]\n top100_sorted = top100[np.argsort(-u_sc[top100])]\n ranked[int(uids[r])] = list(map(int, item_ids[top100_sorted]))\n \n score = evaluate_public.score(ranked, graded)['quality_ndcg@10']\n return score\n\nfor hl in [10, 14, 20, 25, 30, 40]:\n t = time.time()\n sc = run_experiment(half_life_days=hl, idf_gamma=1.0, k=192)\n print(f'half_life={hl:2d}: NDCG = {sc:.6f} ({time.time()-t:.1f}s)')\n\"",
"description": "Fine-tune recency half-life around 10-40 days"
}<untrusted_context>
Output: half_life=10: NDCG = 0.032219 (15.2s)
half_life=14: NDCG = 0.032966 (15.2s)
half_life=20: NDCG = 0.031735 (15.3s)
half_life=25: NDCG = 0.030114 (15.2s)
half_life=30: NDCG = 0.030037 (15.3s)
half_life=40: NDCG = 0.029155 (15.2s)
Process Group PGID: 2376
</untrusted_context>Improving NDCG Scores
I'm thrilled to report a significant leap in NDCG, now sitting at 0.032966, a threefold increase from our baseline! My next steps involve systematically exploring different half-life values (7, 10, 12, 14, 16 days) and fine-tuning the organic_boost parameter, considering values like 3.0 and 4.0.
{
"command": "python3 -c \"\nimport sys\nsys.path.append('/task')\nimport time\nimport gc\nimport numpy as np\nimport pandas as pd\nfrom scipy import sparse\nfrom sklearn.decomposition import TruncatedSVD\nimport pyarrow.parquet as pq\nimport pyarrow.compute as pc\nimport pyarrow as pa\nfrom ranking_evaluation import CandidateSet\nimport evaluate_public\n\ntargets = pd.read_parquet('/task/data/target_users.parquet')\ncatalog = pd.read_parquet('/task/data/candidate_catalog.parquet')\nn_users = len(targets)\nn_items = len(catalog)\n\nuid_to_idx = {uid: i for i, uid in enumerate(targets.uid.values)}\nitem_to_idx = {item: i for i, item in enumerate(catalog.item_id.values)}\n\nval_pref = pd.read_parquet('/task/data/validation_preference_state.parquet')\nlikes = val_pref[val_pref.state == 'liked']\ndislikes = val_pref[val_pref.state == 'disliked']\n\ntable = pq.read_table('/task/data/train_listens.parquet', columns=['uid', 'item_id', 'timestamp', 'played_ratio_pct', 'is_organic'])\ntarget_uids = pa.array(targets.uid.values, type=pa.uint32())\ncatalog_items = pa.array(catalog.item_id.values, type=pa.uint32())\n\nmask = pc.is_in(table['uid'], target_uids)\nmask = pc.and_(mask, pc.is_in(table['item_id'], catalog_items))\nmask = pc.and_(mask, pc.greater_equal(table['played_ratio_pct'], 50))\nfiltered = table.filter(mask).to_pandas()\ndel table; gc.collect()\n\nval_npz = np.load('/task/data/validation_eligible_candidates.npz')\nunpacked = np.unpackbits(val_npz['packed_eligible'], axis=1, count=n_items, bitorder='little').astype(bool)\neligibility = CandidateSet('/task/data/validation_eligible_candidates.npz')\ntargets_set = set(targets.uid.astype(int))\ngraded = evaluate_public.validation_truth(eligibility, targets_set)\n\nitem_ids = val_npz['item_ids']\nuids = val_npz['uids']\nts_float = filtered['timestamp'].astype(np.float64).values\n\ndef run_experiment(half_life_days=14, organic_boost=2.0, strong_boost=1.5, like_weight=3.0, idf_gamma=1.0, k=256, alpha_norm=0.0):\n cutoff = 24786800.0\n hl = half_life_days * 86400.0\n w = np.exp(-(cutoff - ts_float) / hl).astype(np.float32)\n \n w *= np.where(filtered['is_organic'].values == 1, organic_boost, 1.0).astype(np.float32)\n w *= np.where(filtered['played_ratio_pct'].values >= 80, strong_boost, 1.0).astype(np.float32)\n \n df_w = pd.DataFrame({'uid': filtered['uid'], 'item_id': filtered['item_id'], 'w': w})\n agg_listens = df_w.groupby(['uid', 'item_id'])['w'].sum().reset_index()\n agg_listens['w'] = np.log1p(agg_listens['w'])\n \n agg_likes = likes[['uid', 'item_id']].copy()\n agg_likes['w'] = like_weight\n \n combined = pd.concat([agg_listens, agg_likes]).groupby(['uid', 'item_id'])['w'].sum().reset_index()\n rows = combined['uid'].map(uid_to_idx).values\n cols = combined['item_id'].map(item_to_idx).values\n data = combined['w'].values.astype(np.float32)\n \n R = sparse.csr_matrix((data, (rows, cols)), shape=(n_users, n_items))\n \n if idf_gamma > 0:\n df_item = np.diff(R.tocsc().indptr)\n N = n_users\n idf = np.log((N - df_item + 0.5) / (df_item + 0.5) + 1.0) ** idf_gamma\n R_mat = R.multiply(idf).tocsr()\n else:\n R_mat = R\n \n svd = TruncatedSVD(n_components=k, random_state=42)\n U_s = svd.fit_transform(R_mat).astype(np.float32)\n V = svd.components_.astype(np.float32)\n \n if alpha_norm > 0:\n item_norms = np.linalg.norm(V, axis=0) + 1e-8\n V = V / (item_norms ** alpha_norm)\n \n pred = U_s @ V\n pred[~unpacked] = -np.inf\n \n ranked = {}\n for r in range(n_users):\n u_sc = pred[r]\n top100 = np.argpartition(-u_sc, 100)[:100]\n top100_sorted = top100[np.argsort(-u_sc[top100])]\n ranked[int(uids[r])] = list(map(int, item_ids[top100_sorted]))\n \n score = evaluate_public.score(ranked, graded)['quality_ndcg@10']\n return score\n\nprint('Testing like_weight with hl=14...')\nfor lw in [1.0, 3.0, 5.0, 8.0, 12.0]:\n sc = run_experiment(half_life_days=14, like_weight=lw, k=192)\n print(f'like_weight={lw:4.1f}: NDCG = {sc:.6f}')\n\nprint('\\nTesting organic_boost with hl=14...')\nfor ob in [1.0, 2.0, 3.0, 4.0]:\n sc = run_experiment(half_life_days=14, organic_boost=ob, k=192)\n print(f'organic_boost={ob:4.1f}: NDCG = {sc:.6f}')\n\"",
"description": "Test like_weight and organic_boost effects"
}<untrusted_context>
Output: Testing like_weight with hl=14...
like_weight= 1.0: NDCG = 0.034554
like_weight= 3.0: NDCG = 0.032966
like_weight= 5.0: NDCG = 0.028768
like_weight= 8.0: NDCG = 0.024070
like_weight=12.0: NDCG = 0.020298
Testing organic_boost with hl=14...
organic_boost= 1.0: NDCG = 0.030516
organic_boost= 2.0: NDCG = 0.032966
organic_boost= 3.0: NDCG = 0.033981
organic_boost= 4.0: NDCG = 0.034452
Process Group PGID: 2405
</untrusted_context>Exploring Like Weight
My current focus is on understanding the impact of like_weight. A value of 1.0 yielded a significant jump in NDCG to 0.034554. I'm investigating this because validation_preference_state likes, lacking timestamps, are treated as all-time, contrasting with the recency of fresh listens. Overly high weights appear to overshadow recent interactions.
{
"description": "Inspect timestamps and organic flag in train_likes",
"command": "python3 -c \"\nimport pandas as pd\nimport numpy as np\n\ntrain_likes = pd.read_parquet('/task/data/train_likes.parquet')\nprint('Train likes timestamp summary:')\nprint(train_likes['timestamp'].describe())\nprint('Train likes is_organic value counts:')\nprint(train_likes['is_organic'].value_counts())\n\""
}<untrusted_context>
Output: Train likes timestamp summary:
count 8.148310e+05
mean 1.360703e+07
std 7.373538e+06
min 8.000000e+01
25% 7.173508e+06
50% 1.433050e+07
75% 2.034649e+07
max 2.478679e+07
Name: timestamp, dtype: float64
Train likes is_organic value counts:
is_organic
1 461839
0 352992
Name: count, dtype: int64
Process Group PGID: 2434
</untrusted_context>Analyzing Interaction Data
My recent focus has been on the temporal aspects of user interactions. I've confirmed that both 'likes' and 'unlikes' are timestamped, and I'm now investigating the implications of 'unlikes' effectively reversing a 'like'. The next step involves a similar temporal analysis for 'dislikes' and 'undislikes'.
{
"description": "Test timestamped decayed likes and organic multipliers",
"command": "python3 -c \"\nimport sys\nsys.path.append('/task')\nimport time\nimport gc\nimport numpy as np\nimport pandas as pd\nfrom scipy import sparse\nfrom sklearn.decomposition import TruncatedSVD\nimport pyarrow.parquet as pq\nimport pyarrow.compute as pc\nimport pyarrow as pa\nfrom ranking_evaluation import CandidateSet\nimport evaluate_public\n\ntargets = pd.read_parquet('/task/data/target_users.parquet')\ncatalog = pd.read_parquet('/task/data/candidate_catalog.parquet')\nn_users = len(targets)\nn_items = len(catalog)\n\nuid_to_idx = {uid: i for i, uid in enumerate(targets.uid.values)}\nitem_to_idx = {item: i for i, item in enumerate(catalog.item_id.values)}\n\n# Load train likes with timestamps\ntrain_likes = pd.read_parquet('/task/data/train_likes.parquet')\ntarget_uids_set = set(targets.uid)\ncatalog_items_set = set(catalog.item_id)\ntrain_likes = train_likes[train_likes.uid.isin(target_uids_set) & train_likes.item_id.isin(catalog_items_set)]\n\ntable = pq.read_table('/task/data/train_listens.parquet', columns=['uid', 'item_id', 'timestamp', 'played_ratio_pct', 'is_organic'])\ntarget_uids = pa.array(targets.uid.values, type=pa.uint32())\ncatalog_items = pa.array(catalog.item_id.values, type=pa.uint32())\n\nmask = pc.is_in(table['uid'], target_uids)\nmask = pc.and_(mask, pc.is_in(table['item_id'], catalog_items))\nmask = pc.and_(mask, pc.greater_equal(table['played_ratio_pct'], 50))\nfiltered = table.filter(mask).to_pandas()\ndel table; gc.collect()\n\nval_npz = np.load('/task/data/validation_eligible_candidates.npz')\nunpacked = np.unpackbits(val_npz['packed_eligible'], axis=1, count=n_items, bitorder='little').astype(bool)\neligibility = CandidateSet('/task/data/validation_eligible_candidates.npz')\ntargets_set = set(targets.uid.astype(int))\ngraded = evaluate_public.validation_truth(eligibility, targets_set)\n\nitem_ids = val_npz['item_ids']\nuids = val_npz['uids']\n\nts_listens = filtered['timestamp'].astype(np.float64).values\nts_likes = train_likes['timestamp'].astype(np.float64).values\n\ndef run_experiment(half_life_days=14, organic_listen_mult=3.0, strong_listen_mult=1.5, like_base_mult=4.0, organic_like_mult=2.0, idf_gamma=1.0, k=192):\n cutoff = 24786800.0\n hl = half_life_days * 86400.0\n \n # Listens weight\n w_listens = np.exp(-(cutoff - ts_listens) / hl).astype(np.float32)\n w_listens *= np.where(filtered['is_organic'].values == 1, organic_listen_mult, 1.0).astype(np.float32)\n w_listens *= np.where(filtered['played_ratio_pct'].values >= 80, strong_listen_mult, 1.0).astype(np.float32)\n \n # Likes weight with timestamp decay\n w_likes = np.exp(-(cutoff - ts_likes) / hl).astype(np.float32) * like_base_mult\n w_likes *= np.where(train_likes['is_organic'].values == 1, organic_like_mult, 1.0).astype(np.float32)\n \n df_l = pd.DataFrame({'uid': filtered['uid'], 'item_id': filtered['item_id'], 'w': w_listens})\n df_k = pd.DataFrame({'uid': train_likes['uid'], 'item_id': train_likes['item_id'], 'w': w_likes})\n \n combined = pd.concat([df_l, df_k]).groupby(['uid', 'item_id'])['w'].sum().reset_index()\n combined['w'] = np.log1p(combined['w'])\n \n rows = combined['uid'].map(uid_to_idx).values\n cols = combined['item_id'].map(item_to_idx).values\n data = combined['w'].values.astype(np.float32)\n \n R = sparse.csr_matrix((data, (rows, cols)), shape=(n_users, n_items))\n \n df_item = np.diff(R.tocsc().indptr)\n N = n_users\n idf = np.log((N - df_item + 0.5) / (df_item + 0.5) + 1.0) ** idf_gamma\n R_mat = R.multiply(idf).tocsr()\n \n svd = TruncatedSVD(n_components=k, random_state=42)\n U_s = svd.fit_transform(R_mat).astype(np.float32)\n V = svd.components_.astype(np.float32)\n \n pred = U_s @ V\n pred[~unpacked] = -np.inf\n \n ranked = {}\n for r in range(n_users):\n u_sc = pred[r]\n top100 = np.argpartition(-u_sc, 100)[:100]\n top100_sorted = top100[np.argsort(-u_sc[top100])]\n ranked[int(uids[r])] = list(map(int, item_ids[top100_sorted]))\n \n score = evaluate_public.score(ranked, graded)['quality_ndcg@10']\n return score\n\nfor hl in [7, 10, 14, 21]:\n for org_l in [2.0, 4.0, 6.0]:\n sc = run_experiment(half_life_days=hl, organic_listen_mult=org_l, k=192)\n print(f'hl={hl:2d}, org_l={org_l:3.1f}: NDCG = {sc:.6f}')\n\""
}<untrusted_context>
Output: hl= 7, org_l=2.0: NDCG = 0.035128
hl= 7, org_l=4.0: NDCG = 0.035530
hl= 7, org_l=6.0: NDCG = 0.035835
hl=10, org_l=2.0: NDCG = 0.036816
hl=10, org_l=4.0: NDCG = 0.037473
hl=10, org_l=6.0: NDCG = 0.037265
hl=14, org_l=2.0: NDCG = 0.037327
hl=14, org_l=4.0: NDCG = 0.037709
hl=14, org_l=6.0: NDCG = 0.037453
hl=21, org_l=2.0: NDCG = 0.036951
hl=21, org_l=4.0: NDCG = 0.037333
hl=21, org_l=6.0: NDCG = 0.036983
Process Group PGID: 2451
</untrusted_context>Boosting Organic Listens
I'm seeing promising gains, with NDCG climbing towards 0.038 from a previous 0.0107. My next step is to investigate further boosting strong organic listens, specifically those with a played ratio of 80% or higher, by adjusting the strong_listen_mult.
{
"description": "Compare train_likes and validation_preference_state",
"command": "python3 -c \"\nimport pandas as pd\n\ntrain_likes = pd.read_parquet('/task/data/train_likes.parquet')\ntrain_unlikes = pd.read_parquet('/task/data/train_unlikes.parquet')\nval_pref = pd.read_parquet('/task/data/validation_preference_state.parquet')\n\n# Number of likes in val_pref\nliked_pairs_val = set(val_pref[val_pref.state == 'liked'][['uid', 'item_id']].itertuples(index=False, name=None))\n# Number of likes in train_likes\nliked_pairs_train = set(train_likes[['uid', 'item_id']].itertuples(index=False, name=None))\nunliked_pairs_train = set(train_unlikes[['uid', 'item_id']].itertuples(index=False, name=None))\n\nprint('Val pref liked pairs:', len(liked_pairs_val))\nprint('Train likes pairs:', len(liked_pairs_train))\nprint('Train unlikes pairs:', len(unliked_pairs_train))\nprint('Train net liked pairs:', len(liked_pairs_train - unliked_pairs_train))\nprint('Overlap between Val pref liked and Train likes:', len(liked_pairs_val & liked_pairs_train))\n\""
}<untrusted_context>
Output: Val pref liked pairs: 418435
Train likes pairs: 785069
Train unlikes pairs: 271951
Train net liked pairs: 709193
Overlap between Val pref liked and Train likes: 418435
Process Group PGID: 2480
</untrusted_context>Confirming Train/Val Consistency
I've confirmed that every pair in val_pref is present in train_likes, and that val_pref accurately reflects the active snapshot at the validation cutoff. My next step is to investigate test_preference_state.parquet to understand its relation to the test cutoff.
{
"description": "Test idf_gamma and k values on validation set",
"command": "python3 -c \"\nimport sys\nsys.path.append('/task')\nimport time\nimport gc\nimport numpy as np\nimport pandas as pd\nfrom scipy import sparse\nfrom sklearn.decomposition import TruncatedSVD\nimport pyarrow.parquet as pq\nimport pyarrow.compute as pc\nimport pyarrow as pa\nfrom ranking_evaluation import CandidateSet\nimport evaluate_public\n\ntargets = pd.read_parquet('/task/data/target_users.parquet')\ncatalog = pd.read_parquet('/task/data/candidate_catalog.parquet')\nn_users = len(targets)\nn_items = len(catalog)\n\nuid_to_idx = {uid: i for i, uid in enumerate(targets.uid.values)}\nitem_to_idx = {item: i for i, item in enumerate(catalog.item_id.values)}\n\ntrain_likes = pd.read_parquet('/task/data/train_likes.parquet')\ntarget_uids_set = set(targets.uid)\ncatalog_items_set = set(catalog.item_id)\ntrain_likes = train_likes[train_likes.uid.isin(target_uids_set) & train_likes.item_id.isin(catalog_items_set)]\n\ntable = pq.read_table('/task/data/train_listens.parquet', columns=['uid', 'item_id', 'timestamp', 'played_ratio_pct', 'is_organic'])\ntarget_uids = pa.array(targets.uid.values, type=pa.uint32())\ncatalog_items = pa.array(catalog.item_id.values, type=pa.uint32())\n\nmask = pc.is_in(table['uid'], target_uids)\nmask = pc.and_(mask, pc.is_in(table['item_id'], catalog_items))\nmask = pc.and_(mask, pc.greater_equal(table['played_ratio_pct'], 50))\nfiltered = table.filter(mask).to_pandas()\ndel table; gc.collect()\n\nval_npz = np.load('/task/data/validation_eligible_candidates.npz')\nunpacked = np.unpackbits(val_npz['packed_eligible'], axis=1, count=n_items, bitorder='little').astype(bool)\neligibility = CandidateSet('/task/data/validation_eligible_candidates.npz')\ntargets_set = set(targets.uid.astype(int))\ngraded = evaluate_public.validation_truth(eligibility, targets_set)\n\nitem_ids = val_npz['item_ids']\nuids = val_npz['uids']\n\nts_listens = filtered['timestamp'].astype(np.float64).values\nts_likes = train_likes['timestamp'].astype(np.float64).values\n\ncutoff = 24786800.0\nhl = 14 * 86400.0\n\nw_listens = np.exp(-(cutoff - ts_listens) / hl).astype(np.float32)\nw_listens *= np.where(filtered['is_organic'].values == 1, 4.0, 1.0).astype(np.float32)\nw_listens *= np.where(filtered['played_ratio_pct'].values >= 80, 2.0, 1.0).astype(np.float32)\n\nw_likes = np.exp(-(cutoff - ts_likes) / hl).astype(np.float32) * 4.0\nw_likes *= np.where(train_likes['is_organic'].values == 1, 2.0, 1.0).astype(np.float32)\n\ndf_l = pd.DataFrame({'uid': filtered['uid'], 'item_id': filtered['item_id'], 'w': w_listens})\ndf_k = pd.DataFrame({'uid': train_likes['uid'], 'item_id': train_likes['item_id'], 'w': w_likes})\n\ncombined = pd.concat([df_l, df_k]).groupby(['uid', 'item_id'])['w'].sum().reset_index()\ncombined['w'] = np.log1p(combined['w'])\n\nrows = combined['uid'].map(uid_to_idx).values\ncols = combined['item_id'].map(item_to_idx).values\ndata = combined['w'].values.astype(np.float32)\n\nR = sparse.csr_matrix((data, (rows, cols)), shape=(n_users, n_items))\n\ndf_item = np.diff(R.tocsc().indptr)\nN = n_users\nidf_base = np.log((N - df_item + 0.5) / (df_item + 0.5) + 1.0)\n\ndef score_predictions(pred):\n p = pred.copy()\n p[~unpacked] = -np.inf\n ranked = {}\n for r in range(n_users):\n u_sc = p[r]\n top100 = np.argpartition(-u_sc, 100)[:100]\n top100_sorted = top100[np.argsort(-u_sc[top100])]\n ranked[int(uids[r])] = list(map(int, item_ids[top100_sorted]))\n return evaluate_public.score(ranked, graded)['quality_ndcg@10']\n\nprint('--- Testing idf_gamma ---')\nfor gamma in [0.5, 0.75, 1.0, 1.25]:\n R_mat = R.multiply(idf_base ** gamma).tocsr()\n svd = TruncatedSVD(n_components=192, random_state=42)\n U_s = svd.fit_transform(R_mat).astype(np.float32)\n V = svd.components_.astype(np.float32)\n pred = U_s @ V\n print(f'gamma={gamma:4.2f}: NDCG = {score_predictions(pred):.6f}')\n\nprint('\\n--- Testing k (n_components) with gamma=1.0 ---')\nR_mat = R.multiply(idf_base).tocsr()\nfor k in [128, 192, 256, 320, 384, 512]:\n t = time.time()\n svd = TruncatedSVD(n_components=k, random_state=42)\n U_s = svd.fit_transform(R_mat).astype(np.float32)\n V = svd.components_.astype(np.float32)\n pred = U_s @ V\n print(f'k={k:3d}: NDCG = {score_predictions(pred):.6f} ({time.time()-t:.1f}s)')\n\""
}<untrusted_context>
Output: --- Testing idf_gamma ---
gamma=0.50: NDCG = 0.040507
gamma=0.75: NDCG = 0.039709
gamma=1.00: NDCG = 0.038007
gamma=1.25: NDCG = 0.036008
--- Testing k (n_components) with gamma=1.0 ---
k=128: NDCG = 0.037997 (7.4s)
k=192: NDCG = 0.038007 (11.8s)
k=256: NDCG = 0.037613 (15.6s)
k=320: NDCG = 0.036111 (19.4s)
k=384: NDCG = 0.034909 (22.5s)
k=512: NDCG = 0.033434 (28.1s)
Process Group PGID: 2497
</untrusted_context>Improving NDCG Score
My NDCG has now surpassed 0.040, a significant leap from the baseline of 0.0107, representing nearly a fourfold increase. I've also identified that $k$ values between 128 and 192 are optimal, preventing overfitting. I will now focus on further refining the gamma parameter.
{
"command": "python3 -c \"\nimport sys\nsys.path.append('/task')\nimport time\nimport gc\nimport numpy as np\nimport pandas as pd\nfrom scipy import sparse\nfrom sklearn.decomposition import TruncatedSVD\nimport pyarrow.parquet as pq\nimport pyarrow.compute as pc\nimport pyarrow as pa\nfrom ranking_evaluation import CandidateSet\nimport evaluate_public\n\ntargets = pd.read_parquet('/task/data/target_users.parquet')\ncatalog = pd.read_parquet('/task/data/candidate_catalog.parquet')\nn_users = len(targets)\nn_items = len(catalog)\n\nuid_to_idx = {uid: i for i, uid in enumerate(targets.uid.values)}\nitem_to_idx = {item: i for i, item in enumerate(catalog.item_id.values)}\n\ntrain_likes = pd.read_parquet('/task/data/train_likes.parquet')\ntarget_uids_set = set(targets.uid)\ncatalog_items_set = set(catalog.item_id)\ntrain_likes = train_likes[train_likes.uid.isin(target_uids_set) & train_likes.item_id.isin(catalog_items_set)]\n\ntable = pq.read_table('/task/data/train_listens.parquet', columns=['uid', 'item_id', 'timestamp', 'played_ratio_pct', 'is_organic'])\ntarget_uids = pa.array(targets.uid.values, type=pa.uint32())\ncatalog_items = pa.array(catalog.item_id.values, type=pa.uint32())\n\nmask = pc.is_in(table['uid'], target_uids)\nmask = pc.and_(mask, pc.is_in(table['item_id'], catalog_items))\nmask = pc.and_(mask, pc.greater_equal(table['played_ratio_pct'], 50))\nfiltered = table.filter(mask).to_pandas()\ndel table; gc.collect()\n\nval_npz = np.load('/task/data/validation_eligible_candidates.npz')\nunpacked = np.unpackbits(val_npz['packed_eligible'], axis=1, count=n_items, bitorder='little').astype(bool)\neligibility = CandidateSet('/task/data/validation_eligible_candidates.npz')\ntargets_set = set(targets.uid.astype(int))\ngraded = evaluate_public.validation_truth(eligibility, targets_set)\n\nitem_ids = val_npz['item_ids']\nuids = val_npz['uids']\n\nts_listens = filtered['timestamp'].astype(np.float64).values\nts_likes = train_likes['timestamp'].astype(np.float64).values\n\ncutoff = 24786800.0\nhl = 14 * 86400.0\n\nw_listens = np.exp(-(cutoff - ts_listens) / hl).astype(np.float32)\nw_listens *= np.where(filtered['is_organic'].values == 1, 4.0, 1.0).astype(np.float32)\nw_listens *= np.where(filtered['played_ratio_pct'].values >= 80, 2.0, 1.0).astype(np.float32)\n\nw_likes = np.exp(-(cutoff - ts_likes) / hl).astype(np.float32) * 4.0\nw_likes *= np.where(train_likes['is_organic'].values == 1, 2.0, 1.0).astype(np.float32)\n\ndf_l = pd.DataFrame({'uid': filtered['uid'], 'item_id': filtered['item_id'], 'w': w_listens})\ndf_k = pd.DataFrame({'uid': train_likes['uid'], 'item_id': train_likes['item_id'], 'w': w_likes})\n\ncombined = pd.concat([df_l, df_k]).groupby(['uid', 'item_id'])['w'].sum().reset_index()\ncombined['w'] = np.log1p(combined['w'])\n\nrows = combined['uid'].map(uid_to_idx).values\ncols = combined['item_id'].map(item_to_idx).values\ndata = combined['w'].values.astype(np.float32)\n\nR = sparse.csr_matrix((data, (rows, cols)), shape=(n_users, n_items))\n\ndf_item = np.diff(R.tocsc().indptr)\nN = n_users\nidf_base = np.log((N - df_item + 0.5) / (df_item + 0.5) + 1.0)\n\ndef score_predictions(pred):\n p = pred.copy()\n p[~unpacked] = -np.inf\n ranked = {}\n for r in range(n_users):\n u_sc = p[r]\n top100 = np.argpartition(-u_sc, 100)[:100]\n top100_sorted = top100[np.argsort(-u_sc[top100])]\n ranked[int(uids[r])] = list(map(int, item_ids[top100_sorted]))\n return evaluate_public.score(ranked, graded)['quality_ndcg@10']\n\nprint('--- Fine-tuning gamma around 0.3 - 0.6 ---')\nfor gamma in [0.2, 0.3, 0.4, 0.5, 0.6]:\n R_mat = R.multiply(idf_base ** gamma).tocsr()\n svd = TruncatedSVD(n_components=160, random_state=42)\n U_s = svd.fit_transform(R_mat).astype(np.float32)\n V = svd.components_.astype(np.float32)\n pred = U_s @ V\n print(f'gamma={gamma:4.2f}: NDCG = {score_predictions(pred):.6f}')\n\nprint('\\n--- Testing item norm power alpha with gamma=0.4 ---')\nR_mat = R.multiply(idf_base ** 0.4).tocsr()\nsvd = TruncatedSVD(n_components=160, random_state=42)\nU_s = svd.fit_transform(R_mat).astype(np.float32)\nV_base = svd.components_.astype(np.float32)\nitem_norms = np.linalg.norm(V_base, axis=0) + 1e-8\n\nfor alpha in [0.0, 0.1, 0.2, 0.3, 0.4, 0.5]:\n V = V_base / (item_norms ** alpha)\n pred = U_s @ V\n print(f'alpha={alpha:4.2f}: NDCG = {score_predictions(pred):.6f}')\n\"",
"description": "Fine-tune gamma and test item norm power alpha"
}<untrusted_context>
Output: --- Fine-tuning gamma around 0.3 - 0.6 ---
gamma=0.20: NDCG = 0.039545
gamma=0.30: NDCG = 0.040144
gamma=0.40: NDCG = 0.041235
gamma=0.50: NDCG = 0.041078
gamma=0.60: NDCG = 0.040612
--- Testing item norm power alpha with gamma=0.4 ---
alpha=0.00: NDCG = 0.041235
alpha=0.10: NDCG = 0.039981
alpha=0.20: NDCG = 0.039069
alpha=0.30: NDCG = 0.038114
alpha=0.40: NDCG = 0.037080
alpha=0.50: NDCG = 0.035404
Process Group PGID: 2526
</untrusted_context>Exploring Item Discovery
I'm now investigating the impact of incorporating an item discovery prior. Initial findings suggest pure dot product outperforms normalized cosine. I'm about to dive into the candidate_catalog.parquet file to analyze the new results.
{
"description": "Test blending SVD scores with recent and catalog popularity priors",
"command": "python3 -c \"\nimport sys\nsys.path.append('/task')\nimport time\nimport gc\nimport numpy as np\nimport pandas as pd\nfrom scipy import sparse\nfrom sklearn.decomposition import TruncatedSVD\nimport pyarrow.parquet as pq\nimport pyarrow.compute as pc\nimport pyarrow as pa\nfrom ranking_evaluation import CandidateSet\nimport evaluate_public\n\ntargets = pd.read_parquet('/task/data/target_users.parquet')\ncatalog = pd.read_parquet('/task/data/candidate_catalog.parquet')\nn_users = len(targets)\nn_items = len(catalog)\n\nuid_to_idx = {uid: i for i, uid in enumerate(targets.uid.values)}\nitem_to_idx = {item: i for i, item in enumerate(catalog.item_id.values)}\n\ntrain_likes = pd.read_parquet('/task/data/train_likes.parquet')\ntarget_uids_set = set(targets.uid)\ncatalog_items_set = set(catalog.item_id)\ntrain_likes = train_likes[train_likes.uid.isin(target_uids_set) & train_likes.item_id.isin(catalog_items_set)]\n\ntable = pq.read_table('/task/data/train_listens.parquet', columns=['uid', 'item_id', 'timestamp', 'played_ratio_pct', 'is_organic'])\ntarget_uids = pa.array(targets.uid.values, type=pa.uint32())\ncatalog_items = pa.array(catalog.item_id.values, type=pa.uint32())\n\nmask = pc.is_in(table['uid'], target_uids)\nmask = pc.and_(mask, pc.is_in(table['item_id'], catalog_items))\nmask = pc.and_(mask, pc.greater_equal(table['played_ratio_pct'], 50))\nfiltered = table.filter(mask).to_pandas()\ndel table; gc.collect()\n\nval_npz = np.load('/task/data/validation_eligible_candidates.npz')\nunpacked = np.unpackbits(val_npz['packed_eligible'], axis=1, count=n_items, bitorder='little').astype(bool)\neligibility = CandidateSet('/task/data/validation_eligible_candidates.npz')\ntargets_set = set(targets.uid.astype(int))\ngraded = evaluate_public.validation_truth(eligibility, targets_set)\n\nitem_ids = val_npz['item_ids']\nuids = val_npz['uids']\n\nts_listens = filtered['timestamp'].astype(np.float64).values\nts_likes = train_likes['timestamp'].astype(np.float64).values\n\ncutoff = 24786800.0\nhl = 14 * 86400.0\n\nw_listens = np.exp(-(cutoff - ts_listens) / hl).astype(np.float32)\nw_listens *= np.where(filtered['is_organic'].values == 1, 4.0, 1.0).astype(np.float32)\nw_listens *= np.where(filtered['played_ratio_pct'].values >= 80, 2.0, 1.0).astype(np.float32)\n\nw_likes = np.exp(-(cutoff - ts_likes) / hl).astype(np.float32) * 4.0\nw_likes *= np.where(train_likes['is_organic'].values == 1, 2.0, 1.0).astype(np.float32)\n\ndf_l = pd.DataFrame({'uid': filtered['uid'], 'item_id': filtered['item_id'], 'w': w_listens})\ndf_k = pd.DataFrame({'uid': train_likes['uid'], 'item_id': train_likes['item_id'], 'w': w_likes})\n\ncombined = pd.concat([df_l, df_k]).groupby(['uid', 'item_id'])['w'].sum().reset_index()\ncombined['w'] = np.log1p(combined['w'])\n\nrows = combined['uid'].map(uid_to_idx).values\ncols = combined['item_id'].map(item_to_idx).values\ndata = combined['w'].values.astype(np.float32)\n\nR = sparse.csr_matrix((data, (rows, cols)), shape=(n_users, n_items))\n\ndf_item = np.diff(R.tocsc().indptr)\nN = n_users\nidf_base = np.log((N - df_item + 0.5) / (df_item + 0.5) + 1.0)\n\nR_mat = R.multiply(idf_base ** 0.4).tocsr()\nsvd = TruncatedSVD(n_components=160, random_state=42)\nU_s = svd.fit_transform(R_mat).astype(np.float32)\nV = svd.components_.astype(np.float32)\npred_base = U_s @ V\n\ndef score_predictions(pred):\n p = pred.copy()\n p[~unpacked] = -np.inf\n ranked = {}\n for r in range(n_users):\n u_sc = p[r]\n top100 = np.argpartition(-u_sc, 100)[:100]\n top100_sorted = top100[np.argsort(-u_sc[top100])]\n ranked[int(uids[r])] = list(map(int, item_ids[top100_sorted]))\n return evaluate_public.score(ranked, graded)['quality_ndcg@10']\n\n# Compute recent item organic popularity\nrecent_mask = (ts_listens >= (cutoff - 14 * 86400)) & (filtered['is_organic'].values == 1)\ndf_recent = pd.DataFrame({'item_id': filtered['item_id'].values[recent_mask]})\nrecent_pop = df_recent['item_id'].value_counts()\nrecent_vec = np.zeros(n_items, dtype=np.float32)\nfor item, cnt in recent_pop.items():\n if item in item_to_idx:\n recent_vec[item_to_idx[item]] = np.log1p(cnt)\n\n# Normalize prior vectors to std=1 for easy blending\nrecent_vec = (recent_vec - np.mean(recent_vec)) / (np.std(recent_vec) + 1e-8)\ncatalog_pop = np.log1p(catalog['organic_positive_listens'].values.astype(np.float32))\ncatalog_pop = (catalog_pop - np.mean(catalog_pop)) / (np.std(catalog_pop) + 1e-8)\n\npred_std = np.std(pred_base)\nprint(f'pred_std: {pred_std:.4f}')\n\nprint('--- Testing blend with recent organic popularity ---')\nfor beta in [0.0, 0.05, 0.1, 0.2, 0.3, 0.5]:\n pred = pred_base + (beta * pred_std) * recent_vec[None, :]\n print(f'beta_recent={beta:4.2f}: NDCG = {score_predictions(pred):.6f}')\n\nprint('\\n--- Testing blend with catalog organic popularity ---')\nfor beta in [0.0, 0.05, 0.1, 0.2, 0.3, 0.5]:\n pred = pred_base + (beta * pred_std) * catalog_pop[None, :]\n print(f'beta_catalog={beta:4.2f}: NDCG = {score_predictions(pred):.6f}')\n\""
}<untrusted_context>
Output: pred_std: 0.0884
--- Testing blend with recent organic popularity ---
beta_recent=0.00: NDCG = 0.041235
beta_recent=0.05: NDCG = 0.041274
beta_recent=0.10: NDCG = 0.041585
beta_recent=0.20: NDCG = 0.041733
beta_recent=0.30: NDCG = 0.041889
beta_recent=0.50: NDCG = 0.042227
--- Testing blend with catalog organic popularity ---
beta_catalog=0.00: NDCG = 0.041235
beta_catalog=0.05: NDCG = 0.040917
beta_catalog=0.10: NDCG = 0.040788
beta_catalog=0.20: NDCG = 0.041136
beta_catalog=0.30: NDCG = 0.040977
beta_catalog=0.50: NDCG = 0.039935
Process Group PGID: 2555
</untrusted_context>Observing Engagement Trends
My recent analysis indicates a strong positive correlation between recent organic popularity and NDCG scores. It seems static catalog popularity is less impactful, possibly due to the dynamic nature of music trends. I'm currently exploring how to best leverage these rapidly shifting user interests.
{
"description": "Fine-tune beta_recent popularity blend",
"command": "python3 -c \"\nimport sys\nsys.path.append('/task')\nimport numpy as np\nimport pandas as pd\nfrom scipy import sparse\nfrom sklearn.decomposition import TruncatedSVD\nimport pyarrow.parquet as pq\nimport pyarrow.compute as pc\nimport pyarrow as pa\nfrom ranking_evaluation import CandidateSet\nimport evaluate_public\n\ntargets = pd.read_parquet('/task/data/target_users.parquet')\ncatalog = pd.read_parquet('/task/data/candidate_catalog.parquet')\nn_users = len(targets)\nn_items = len(catalog)\n\nuid_to_idx = {uid: i for i, uid in enumerate(targets.uid.values)}\nitem_to_idx = {item: i for i, item in enumerate(catalog.item_id.values)}\n\ntrain_likes = pd.read_parquet('/task/data/train_likes.parquet')\ntarget_uids_set = set(targets.uid)\ncatalog_items_set = set(catalog.item_id)\ntrain_likes = train_likes[train_likes.uid.isin(target_uids_set) & train_likes.item_id.isin(catalog_items_set)]\n\ntable = pq.read_table('/task/data/train_listens.parquet', columns=['uid', 'item_id', 'timestamp', 'played_ratio_pct', 'is_organic'])\ntarget_uids = pa.array(targets.uid.values, type=pa.uint32())\ncatalog_items = pa.array(catalog.item_id.values, type=pa.uint32())\n\nmask = pc.is_in(table['uid'], target_uids)\nmask = pc.and_(mask, pc.is_in(table['item_id'], catalog_items))\nmask = pc.and_(mask, pc.greater_equal(table['played_ratio_pct'], 50))\nfiltered = table.filter(mask).to_pandas()\n\nval_npz = np.load('/task/data/validation_eligible_candidates.npz')\nunpacked = np.unpackbits(val_npz['packed_eligible'], axis=1, count=n_items, bitorder='little').astype(bool)\neligibility = CandidateSet('/task/data/validation_eligible_candidates.npz')\ntargets_set = set(targets.uid.astype(int))\ngraded = evaluate_public.validation_truth(eligibility, targets_set)\n\nitem_ids = val_npz['item_ids']\nuids = val_npz['uids']\n\nts_listens = filtered['timestamp'].astype(np.float64).values\nts_likes = train_likes['timestamp'].astype(np.float64).values\n\ncutoff = 24786800.0\nhl = 14 * 86400.0\n\nw_listens = np.exp(-(cutoff - ts_listens) / hl).astype(np.float32)\nw_listens *= np.where(filtered['is_organic'].values == 1, 4.0, 1.0).astype(np.float32)\nw_listens *= np.where(filtered['played_ratio_pct'].values >= 80, 2.0, 1.0).astype(np.float32)\n\nw_likes = np.exp(-(cutoff - ts_likes) / hl).astype(np.float32) * 4.0\nw_likes *= np.where(train_likes['is_organic'].values == 1, 2.0, 1.0).astype(np.float32)\n\ndf_l = pd.DataFrame({'uid': filtered['uid'], 'item_id': filtered['item_id'], 'w': w_listens})\ndf_k = pd.DataFrame({'uid': train_likes['uid'], 'item_id': train_likes['item_id'], 'w': w_likes})\n\ncombined = pd.concat([df_l, df_k]).groupby(['uid', 'item_id'])['w'].sum().reset_index()\ncombined['w'] = np.log1p(combined['w'])\n\nrows = combined['uid'].map(uid_to_idx).values\ncols = combined['item_id'].map(item_to_idx).values\ndata = combined['w'].values.astype(np.float32)\n\nR = sparse.csr_matrix((data, (rows, cols)), shape=(n_users, n_items))\n\ndf_item = np.diff(R.tocsc().indptr)\nN = n_users\nidf_base = np.log((N - df_item + 0.5) / (df_item + 0.5) + 1.0)\n\nR_mat = R.multiply(idf_base ** 0.4).tocsr()\nsvd = TruncatedSVD(n_components=160, random_state=42)\nU_s = svd.fit_transform(R_mat).astype(np.float32)\nV = svd.components_.astype(np.float32)\npred_base = U_s @ V\n\n# Recent organic listens\nrecent_mask = (ts_listens >= (cutoff - 14 * 86400)) & (filtered['is_organic'].values == 1)\ndf_recent = pd.DataFrame({'item_id': filtered['item_id'].values[recent_mask]})\nrecent_pop = df_recent['item_id'].value_counts()\nrecent_vec = np.zeros(n_items, dtype=np.float32)\nfor item, cnt in recent_pop.items():\n if item in item_to_idx:\n recent_vec[item_to_idx[item]] = np.log1p(cnt)\n\nrecent_vec = (recent_vec - np.mean(recent_vec)) / (np.std(recent_vec) + 1e-8)\npred_std = np.std(pred_base)\n\ndef score_predictions(pred):\n p = pred.copy()\n p[~unpacked] = -np.inf\n ranked = {}\n for r in range(n_users):\n u_sc = p[r]\n top100 = np.argpartition(-u_sc, 100)[:100]\n top100_sorted = top100[np.argsort(-u_sc[top100])]\n ranked[int(uids[r])] = list(map(int, item_ids[top100_sorted]))\n return evaluate_public.score(ranked, graded)['quality_ndcg@10']\n\nfor beta in [0.4, 0.5, 0.6, 0.7, 0.8, 1.0, 1.2]:\n pred = pred_base + (beta * pred_std) * recent_vec[None, :]\n print(f'beta_recent={beta:4.2f}: NDCG = {score_predictions(pred):.6f}')\n\""
}<untrusted_context>
Output: beta_recent=0.40: NDCG = 0.042249
beta_recent=0.50: NDCG = 0.042227
beta_recent=0.60: NDCG = 0.042661
beta_recent=0.70: NDCG = 0.043051
beta_recent=0.80: NDCG = 0.042978
beta_recent=1.00: NDCG = 0.042349
beta_recent=1.20: NDCG = 0.042590
Process Group PGID: 2584
</untrusted_context>Improving Ranking Quality
My current focus is on enhancing our ranking system. I've observed a significant uplift in NDCG, increasing by over 4x to 0.043051, stemming from a beta value of 0.70. I'm now shifting to analyze dislike signals to further refine performance.
Incorporating Dislike Signals
I'm exploring how to leverage the substantial dislike data (over 50,000 entries) by incorporating disliked items into the interaction matrix with negative weights. Truncated SVD can handle these negative entries, naturally modeling aversion and potentially pushing scores lower for similar items. I'll test this approach next.
{
"description": "Test negative weights for dislikes",
"command": "python3 -c \"\nimport sys\nsys.path.append('/task')\nimport numpy as np\nimport pandas as pd\nfrom scipy import sparse\nfrom sklearn.decomposition import TruncatedSVD\nimport pyarrow.parquet as pq\nimport pyarrow.compute as pc\nimport pyarrow as pa\nfrom ranking_evaluation import CandidateSet\nimport evaluate_public\n\ntargets = pd.read_parquet('/task/data/target_users.parquet')\ncatalog = pd.read_parquet('/task/data/candidate_catalog.parquet')\nn_users = len(targets)\nn_items = len(catalog)\n\nuid_to_idx = {uid: i for i, uid in enumerate(targets.uid.values)}\nitem_to_idx = {item: i for i, item in enumerate(catalog.item_id.values)}\n\ntarget_uids_set = set(targets.uid)\ncatalog_items_set = set(catalog.item_id)\n\ntrain_likes = pd.read_parquet('/task/data/train_likes.parquet')\ntrain_likes = train_likes[train_likes.uid.isin(target_uids_set) & train_likes.item_id.isin(catalog_items_set)]\n\ntrain_dislikes = pd.read_parquet('/task/data/train_dislikes.parquet')\ntrain_dislikes = train_dislikes[train_dislikes.uid.isin(target_uids_set) & train_dislikes.item_id.isin(catalog_items_set)]\n\ntable = pq.read_table('/task/data/train_listens.parquet', columns=['uid', 'item_id', 'timestamp', 'played_ratio_pct', 'is_organic'])\ntarget_uids = pa.array(targets.uid.values, type=pa.uint32())\ncatalog_items = pa.array(catalog.item_id.values, type=pa.uint32())\n\nmask = pc.is_in(table['uid'], target_uids)\nmask = pc.and_(mask, pc.is_in(table['item_id'], catalog_items))\nmask = pc.and_(mask, pc.greater_equal(table['played_ratio_pct'], 50))\nfiltered = table.filter(mask).to_pandas()\n\nval_npz = np.load('/task/data/validation_eligible_candidates.npz')\nunpacked = np.unpackbits(val_npz['packed_eligible'], axis=1, count=n_items, bitorder='little').astype(bool)\neligibility = CandidateSet('/task/data/validation_eligible_candidates.npz')\ntargets_set = set(targets.uid.astype(int))\ngraded = evaluate_public.validation_truth(eligibility, targets_set)\n\nitem_ids = val_npz['item_ids']\nuids = val_npz['uids']\n\nts_listens = filtered['timestamp'].astype(np.float64).values\nts_likes = train_likes['timestamp'].astype(np.float64).values\nts_dislikes = train_dislikes['timestamp'].astype(np.float64).values\n\ncutoff = 24786800.0\nhl = 14 * 86400.0\n\nw_listens = np.exp(-(cutoff - ts_listens) / hl).astype(np.float32)\nw_listens *= np.where(filtered['is_organic'].values == 1, 4.0, 1.0).astype(np.float32)\nw_listens *= np.where(filtered['played_ratio_pct'].values >= 80, 2.0, 1.0).astype(np.float32)\n\nw_likes = np.exp(-(cutoff - ts_likes) / hl).astype(np.float32) * 4.0\nw_likes *= np.where(train_likes['is_organic'].values == 1, 2.0, 1.0).astype(np.float32)\n\ndf_l = pd.DataFrame({'uid': filtered['uid'], 'item_id': filtered['item_id'], 'w': w_listens})\ndf_k = pd.DataFrame({'uid': train_likes['uid'], 'item_id': train_likes['item_id'], 'w': w_likes})\n\n# Recent organic listens for popularity prior\nrecent_mask = (ts_listens >= (cutoff - 14 * 86400)) & (filtered['is_organic'].values == 1)\ndf_recent = pd.DataFrame({'item_id': filtered['item_id'].values[recent_mask]})\nrecent_pop = df_recent['item_id'].value_counts()\nrecent_vec = np.zeros(n_items, dtype=np.float32)\nfor item, cnt in recent_pop.items():\n if item in item_to_idx:\n recent_vec[item_to_idx[item]] = np.log1p(cnt)\nrecent_vec = (recent_vec - np.mean(recent_vec)) / (np.std(recent_vec) + 1e-8)\n\ndef test_dislike(neg_weight):\n if neg_weight != 0:\n w_d = np.exp(-(cutoff - ts_dislikes) / hl).astype(np.float32) * neg_weight\n df_d = pd.DataFrame({'uid': train_dislikes['uid'], 'item_id': train_dislikes['item_id'], 'w': w_d})\n dfs = [df_l, df_k, df_d]\n else:\n dfs = [df_l, df_k]\n \n combined = pd.concat(dfs).groupby(['uid', 'item_id'])['w'].sum().reset_index()\n # Apply sign-preserving log1p: sign(x) * log1p(abs(x))\n combined['w'] = np.sign(combined['w']) * np.log1p(np.abs(combined['w']))\n \n rows = combined['uid'].map(uid_to_idx).values\n cols = combined['item_id'].map(item_to_idx).values\n data = combined['w'].values.astype(np.float32)\n \n R = sparse.csr_matrix((data, (rows, cols)), shape=(n_users, n_items))\n \n # IDF on positive interactions\n R_pos = R.copy()\n R_pos.data = np.maximum(R_pos.data, 0)\n df_item = np.diff(R_pos.tocsc().indptr)\n N = n_users\n idf = np.log((N - df_item + 0.5) / (df_item + 0.5) + 1.0) ** 0.4\n R_mat = R.multiply(idf).tocsr()\n \n svd = TruncatedSVD(n_components=160, random_state=42)\n U_s = svd.fit_transform(R_mat).astype(np.float32)\n V = svd.components_.astype(np.float32)\n pred_base = U_s @ V\n pred_std = np.std(pred_base)\n pred = pred_base + (0.70 * pred_std) * recent_vec[None, :]\n \n pred[~unpacked] = -np.inf\n ranked = {}\n for r in range(n_users):\n u_sc = pred[r]\n top100 = np.argpartition(-u_sc, 100)[:100]\n top100_sorted = top100[np.argsort(-u_sc[top100])]\n ranked[int(uids[r])] = list(map(int, item_ids[top100_sorted]))\n return evaluate_public.score(ranked, graded)['quality_ndcg@10']\n\nfor nw in [0.0, -1.0, -2.0, -4.0]:\n print(f'neg_weight={nw:4.1f}: NDCG = {test_dislike(nw):.6f}')\n\""
}<untrusted_context>
Output: neg_weight= 0.0: NDCG = 0.043051
neg_weight=-1.0: NDCG = 0.042846
neg_weight=-2.0: NDCG = 0.042545
neg_weight=-4.0: NDCG = 0.042551
Process Group PGID: 2613
</untrusted_context>Exploring Negative Weights
I'm observing that incorporating negative weights, even slightly, is showing a marginal decrease in NDCG. This aligns with my expectation, given that candidate eligibility already handles explicit dislikes, making SVD's negative entries somewhat redundant.
Developing Temporal Horizons
I'm considering how to better capture user tastes over time. My current thinking is to ensemble multiple recency horizons, combining short-term signals for immediate preferences with longer-term ones for stable tastes. This could be implemented as a base weight plus a recency boost for recent interactions.
{
"command": "python3 -c \"\nimport sys\nsys.path.append('/task')\nimport numpy as np\nimport pandas as pd\nfrom scipy import sparse\nfrom sklearn.decomposition import TruncatedSVD\nimport pyarrow.parquet as pq\nimport pyarrow.compute as pc\nimport pyarrow as pa\nfrom ranking_evaluation import CandidateSet\nimport evaluate_public\n\ntargets = pd.read_parquet('/task/data/target_users.parquet')\ncatalog = pd.read_parquet('/task/data/candidate_catalog.parquet')\nn_users = len(targets)\nn_items = len(catalog)\n\nuid_to_idx = {uid: i for i, uid in enumerate(targets.uid.values)}\nitem_to_idx = {item: i for i, item in enumerate(catalog.item_id.values)}\n\ntarget_uids_set = set(targets.uid)\ncatalog_items_set = set(catalog.item_id)\n\ntrain_likes = pd.read_parquet('/task/data/train_likes.parquet')\ntrain_likes = train_likes[train_likes.uid.isin(target_uids_set) & train_likes.item_id.isin(catalog_items_set)]\n\ntable = pq.read_table('/task/data/train_listens.parquet', columns=['uid', 'item_id', 'timestamp', 'played_ratio_pct', 'is_organic'])\ntarget_uids = pa.array(targets.uid.values, type=pa.uint32())\ncatalog_items = pa.array(catalog.item_id.values, type=pa.uint32())\n\nmask = pc.is_in(table['uid'], target_uids)\nmask = pc.and_(mask, pc.is_in(table['item_id'], catalog_items))\nmask = pc.and_(mask, pc.greater_equal(table['played_ratio_pct'], 50))\nfiltered = table.filter(mask).to_pandas()\n\nval_npz = np.load('/task/data/validation_eligible_candidates.npz')\nunpacked = np.unpackbits(val_npz['packed_eligible'], axis=1, count=n_items, bitorder='little').astype(bool)\neligibility = CandidateSet('/task/data/validation_eligible_candidates.npz')\ntargets_set = set(targets.uid.astype(int))\ngraded = evaluate_public.validation_truth(eligibility, targets_set)\n\nitem_ids = val_npz['item_ids']\nuids = val_npz['uids']\n\nts_listens = filtered['timestamp'].astype(np.float64).values\nts_likes = train_likes['timestamp'].astype(np.float64).values\n\ncutoff = 24786800.0\nhl = 14 * 86400.0\n\n# Recent organic listens for popularity prior\nrecent_mask = (ts_listens >= (cutoff - 14 * 86400)) & (filtered['is_organic'].values == 1)\ndf_recent = pd.DataFrame({'item_id': filtered['item_id'].values[recent_mask]})\nrecent_pop = df_recent['item_id'].value_counts()\nrecent_vec = np.zeros(n_items, dtype=np.float32)\nfor item, cnt in recent_pop.items():\n if item in item_to_idx:\n recent_vec[item_to_idx[item]] = np.log1p(cnt)\nrecent_vec = (recent_vec - np.mean(recent_vec)) / (np.std(recent_vec) + 1e-8)\n\ndef test_base_weight(base_w):\n rec_decay_listens = np.exp(-(cutoff - ts_listens) / hl).astype(np.float32)\n w_listens = (base_w + (1.0 - base_w) * rec_decay_listens)\n w_listens *= np.where(filtered['is_organic'].values == 1, 4.0, 1.0).astype(np.float32)\n w_listens *= np.where(filtered['played_ratio_pct'].values >= 80, 2.0, 1.0).astype(np.float32)\n\n rec_decay_likes = np.exp(-(cutoff - ts_likes) / hl).astype(np.float32)\n w_likes = (base_w + (1.0 - base_w) * rec_decay_likes) * 4.0\n w_likes *= np.where(train_likes['is_organic'].values == 1, 2.0, 1.0).astype(np.float32)\n\n df_l = pd.DataFrame({'uid': filtered['uid'], 'item_id': filtered['item_id'], 'w': w_listens})\n df_k = pd.DataFrame({'uid': train_likes['uid'], 'item_id': train_likes['item_id'], 'w': w_likes})\n\n combined = pd.concat([df_l, df_k]).groupby(['uid', 'item_id'])['w'].sum().reset_index()\n combined['w'] = np.log1p(combined['w'])\n\n rows = combined['uid'].map(uid_to_idx).values\n cols = combined['item_id'].map(item_to_idx).values\n data = combined['w'].values.astype(np.float32)\n\n R = sparse.csr_matrix((data, (rows, cols)), shape=(n_users, n_items))\n\n df_item = np.diff(R.tocsc().indptr)\n N = n_users\n idf = np.log((N - df_item + 0.5) / (df_item + 0.5) + 1.0) ** 0.4\n R_mat = R.multiply(idf).tocsr()\n\n svd = TruncatedSVD(n_components=160, random_state=42)\n U_s = svd.fit_transform(R_mat).astype(np.float32)\n V = svd.components_.astype(np.float32)\n pred_base = U_s @ V\n pred_std = np.std(pred_base)\n pred = pred_base + (0.70 * pred_std) * recent_vec[None, :]\n\n pred[~unpacked] = -np.inf\n ranked = {}\n for r in range(n_users):\n u_sc = pred[r]\n top100 = np.argpartition(-u_sc, 100)[:100]\n top100_sorted = top100[np.argsort(-u_sc[top100])]\n ranked[int(uids[r])] = list(map(int, item_ids[top100_sorted]))\n return evaluate_public.score(ranked, graded)['quality_ndcg@10']\n\nfor bw in [0.0, 0.05, 0.1, 0.2, 0.3]:\n print(f'base_weight={bw:4.2f}: NDCG = {test_base_weight(bw):.6f}')\n\"",
"description": "Test base weight blending long-term and short-term tastes"
}<untrusted_context>
Output: base_weight=0.00: NDCG = 0.043051
base_weight=0.05: NDCG = 0.039988
base_weight=0.10: NDCG = 0.035770
base_weight=0.20: NDCG = 0.032332
base_weight=0.30: NDCG = 0.030851
Process Group PGID: 2642
</untrusted_context>Confirming Streaming Dynamics
I've confirmed that a base_weight of 0.00 yields superior results, indicating user discovery here strongly correlates with recent engagement, roughly within the past 2-4 weeks. This validates the active discovery horizon hypothesis.
Exploring Ensemble Models
I am now investigating ensemble approaches, specifically considering blending a recency-weighted SVD model with Item-Item Collaborative Filtering. My current focus is on optimizing the Item-Item CF component by pruning the similarity matrix to top-K neighbors to filter out noise and improve relevance.
{
"description": "Benchmark item-item similarity computation in blocks",
"command": "python3 -c \"\nimport time\nimport numpy as np\nfrom scipy import sparse\nfrom sklearn.preprocessing import normalize\n\n# Test on synthetic sparse matrix of realistic size\nR = sparse.random(8456, 30000, density=0.015, format='csc', dtype=np.float32)\nR_norm = normalize(R, norm='l2', axis=0) # normalize columns (items)\n\nt0 = time.time()\n# Test 1000 items block\nblock = R_norm[:, :1000].T @ R_norm\nprint('1000 items block time:', time.time() - t0, 's')\n\""
}<untrusted_context>
Output: 1000 items block time: 0.6277880668640137 s
Process Group PGID: 2671
</untrusted_context>Optimizing neighbor computations
I'm refining the neighbor computation process. Initial tests show promising speedups, with 1000 items processing in just 0.62 seconds. Scaling this to 30,000 items suggests an achievable time of around 18 seconds, assuming a block-based approach. My current focus is on efficiently calculating cosine similarities within these blocks.
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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.