yambda-discovery-ranking-v01 / gemini-3-8-flash-high-basic-fixed-r02-d237f45e
Recorded model: gemini-3.8-flash
## Step 1 · user · 2026-09-29T04:19:32.587Z
This is the Gemini CLI. We are setting up the context for our chat.
Today's date is Tuesday, September 29, 2026 (formatted according to the user's locale).
My operating system is: linux
The project's temporary directory is: /home/agent/.gemini/tmp/app
- **Workspace Directories:**
- /app
- /task
- **Directory Structure:**
Showing up to 200 items (files + folders).
/app/
└───solution/
Showing up to 200 items (files + folders).
/task/
├───evaluate_public.py
├───ranking_evaluation.py
├───requirements.txt
└───data/
├───candidate_catalog.parquet
├───target_users.parquet
├───test_eligible_candidates.npz
├───test_preference_state.parquet
├───train_dislikes.parquet
├───train_likes.parquet
├───train_listens.parquet
├───train_undislikes.parquet
├───train_unlikes.parquet
├───val_dislikes.parquet
├───val_likes.parquet
├───val_listens.parquet
├───val_undislikes.parquet
├───val_unlikes.parquet
├───validation_eligible_candidates.npz
└───validation_preference_state.parquet
## Step 2 · user · 2026-09-29T04:19:32.632Z
# Build a music-discovery ranker
Build the strongest ranking model you can for the "Made for You" shelf of a
music streaming service. Per-user candidate eligibility, cutoff-specific
explicit preference state, and a temporal backtest are provided. Candidate
eligibility is platform-owned; all modeling within that candidate boundary is
in scope.
## Data
All inputs are under `/task/data/`. Training data ends at timestamp `24786800`.
A 30-minute gap precedes a seven-day public validation window. The hidden test
is the following seven-day window after another 30-minute gap.
For each event family there is a `train_*.parquet` and `val_*.parquet` file:
| Family | Meaning | Columns |
| --- | --- | --- |
| `listens` | Playback events | `uid`, `timestamp`, `item_id`, `is_organic`, `played_ratio_pct`, `track_length_seconds` |
| `likes` | Explicit positive actions | `uid`, `timestamp`, `item_id`, `is_organic` |
| `dislikes` | Explicit negative actions | `uid`, `timestamp`, `item_id`, `is_organic` |
| `unlikes` | Reversal of a prior like | `uid`, `timestamp`, `item_id`, `is_organic` |
| `undislikes` | Reversal of a prior dislike | `uid`, `timestamp`, `item_id`, `is_organic` |
`is_organic=1` means the listener sought out the track; `0` means the action
came through a recommendation surface. A listen is a conventional positive at
50% completion. The discovery-quality objective below uses the stronger 80%
threshold.
The two public windows have different roles:
1. Build and tune a validation ranker using only `train_*` as history and
`val_*` as subsequent outcomes.
2. After selecting the approach, refit it on `train_* + val_*`. At hidden-test
time the validation events are observed history, not recommendable outcomes.
Do not use `val_*` events as features when measuring a validation ranking. They
may and should be used when refitting the final hidden-test ranker.
Additional files under `/task/data/`:
- `candidate_catalog.parquet`: the 30,000 retrieval-catalog tracks, ordered by
training positive-listen popularity.
- `target_users.parquet`: users with at least 20 positive listens in the 90 days
before the training cutoff, selected without future labels.
- `validation_preference_state.parquet`: active explicit state at the training
cutoff, for validation ranking.
- `test_preference_state.parquet`: active explicit state after validation, for
the final hidden-test ranking. State is `liked` or `disliked`; a pair may have
both active channels.
- `validation_eligible_candidates.npz` and `test_eligible_candidates.npz`:
cutoff-correct candidate sets for every target user. Each contains aligned
arrays `uids`, `item_ids`, and `packed_eligible`. For user row `r`, recover
the Boolean item mask with `np.unpackbits(packed_eligible[r],
count=len(item_ids), bitorder="little")`.
The exact available Python distribution inventory is listed in
`/task/requirements.txt`. Future test interactions are hidden and used only by
the separate Harbor verifier.
## Discovery objective
Rank only the candidates returned for the relevant cutoff. Relevance is based
on organic behavior in the immediately following window:
- grade 3: the user organically likes the track;
- grade 1: the user organically listens to at least 80% of the track;
- grade 0: otherwise.
If both positive events occur, grade 3 wins. Ranking quality is measured with
graded NDCG@10. DCG uses gain `2^grade - 1` and discount
`1/log2(rank + 1)`, then averages per-user NDCG over users with at least one
relevant item.
The public evaluator reports this objective for development, but its score is
not a separate acceptance stage. Final acceptance is determined on the hidden
test using the same objective.
The Yambda logs contain interactions, not impression propensities. Treat the
organic objective as a robust offline target, not as a causal estimate of
online lift.
## Public tools
`/task/evaluate_public.py` scores a validation-stage ranking against the public
window. Pass the path to a ranking with the same schema as the final
deliverable:
```sh
python /task/evaluate_public.py solution/validation.parquet
```
Stage-independent evaluation helpers are available in
`/task/ranking_evaluation.py`. The builder installs the same checked-in module
in the public agent image and private verifier image so scoring mechanics cannot
drift. The public copy contains no hidden labels, evaluator-owned data paths, or
pass thresholds.
The public evaluator uses `validation_eligible_candidates.npz`; it is not a
validator for the final artifact. No model, fitted representation, example
submission, or modeling architecture is supplied. Build and tune the best model
you can, then refit the selected approach as described above.
## Deliverable
Write `/app/solution/submission.parquet` or
`/app/solution/submission.csv`, with exactly 100 rows for every target user and
columns `uid`, `item_id`, `rank`. Ranks must be the integers 1-100 with no ties
or duplicate items. Every submitted item must be present in that user's
`test_eligible_candidates.npz` candidate set.
Leave the code that builds the artifact and a concise
`/app/solution/README.md` in `/app/solution/`. Keep the best valid submission
saved throughout the attempt so it survives the deadline.
## Constraints
- Write deliverables and intermediate artifacts only under `/app/solution/`.
- Treat `/task/` and the installed Python environment as read-only.
- Do not invoke nested agents or attempt to access verifier files, credentials,
other trials, or host paths.
- Do not install packages or use network access except for the model provider
connection managed by Harbor.
- You have up to 30 minutes. Improve the model as much as possible within that
limit. Structural validity alone is not sufficient; the final submission is
also evaluated for ranking quality.
## Step 3 · agent · 2026-09-29T04:19:39.681Z
### Recorded reasoning summary
Reviewing Task Parameters
I'm currently examining the core requirements and constraints for this music discovery ranker project. My focus is on understanding the operational guidelines and the tight 30-minute time limit, ensuring a clear path forward.
### update_topic · 2026-09-29T04:19:39.681Z
{
"strategic_intent": "Explore task files, understand data schema and sizes, and inspect public evaluation logic.",
"summary": "Starting initial exploration of the task environment. We will inspect evaluate_public.py, ranking_evaluation.py, requirements.txt, and explore the dataset dimensions and schemas in /task/data.",
"title": "Initial Exploration and Environment Analysis"
}
### Tool output · 2026-09-29T04:19:39.699Z
Current topic: "Initial Exploration and Environment Analysis"
Topic summary: Starting initial exploration of the task environment. We will inspect evaluate_public.py, ranking_evaluation.py, requirements.txt, and explore the dataset dimensions and schemas in /task/data.
Strategic Intent: Explore task files, understand data schema and sizes, and inspect public evaluation logic.
### read_file · 2026-09-29T04:19:39.681Z
{
"file_path": "/task/evaluate_public.py"
}
### Tool output · 2026-09-29T04:19:39.699Z
#!/usr/bin/env python3
"""Score a validation-stage ranking against public discovery labels.
``build_task.py`` publishes this module with the solver-visible task files. It
loads target users, validation eligibility, and validation-window events from
the adjacent ``data`` directory, then evaluates one ranking supplied on the
command line. Stage-independent mechanics come from the adjacent public
``ranking_evaluation.py`` module. The builder installs the same checked-in
module in the private judge, so public and private scoring mechanics cannot
drift. A relevant candidate receives grade 3 for an organic like or grade 1 for
an organic listen with at least 80 percent completion; a like takes precedence
when both events exist.
Before scoring, the ranking must contain exactly 100 distinct eligible items
for every target user, with integer-valued ``uid``, ``item_id``, and ``rank``
columns and ranks 1 through 100 exactly once per user. Extra columns are
ignored. Quality is graded NDCG@10, averaged only over users who have at least
one eligible relevant item in the validation window.
The evaluator prints a JSON object containing the model path and scores. It
does not apply the task's pass threshold; its purpose is model development and
selection. The private judge applies the threshold to the final submission.
Usage::
python evaluate_public.py
"""
from __future__ import annotations
import json
import sys
from pathlib import Path
import pandas as pd
from ranking_evaluation import (
CandidateSet,
RankingValidationError,
ranked_items,
read_ranking,
score_rankings,
validate_ranking,
)
HERE = Path(__file__).resolve().parent
DATA = HERE / "data"
LISTEN_STRONG = 80
def validation_truth(
eligibility: CandidateSet, targets: set[int]
) -> dict[int, dict[int, int]]:
"""Build cutoff-correct graded relevance from public validation events.
Only target-user events for eligible candidates are retained. Organic
listens completed to at least 80 percent receive grade 1, and organic likes
receive grade 3. Repeated user-item events are collapsed, and processing
likes second ensures that grade 3 wins over grade 1.
Args:
eligibility: Validation-cutoff candidate membership service.
targets: Complete set of target user IDs.
Returns:
A nested ``uid -> item_id -> grade`` mapping. Users without an eligible
relevant event are omitted.
"""
listens = pd.read_parquet(DATA / "val_listens.parquet")
likes = pd.read_parquet(DATA / "val_likes.parquet")
strong = listens[
listens.uid.isin(targets)
& (listens.is_organic == 1)
& (listens.played_ratio_pct >= LISTEN_STRONG)
][["uid", "item_id"]].drop_duplicates()
organic_likes = likes[
likes.uid.isin(targets) & (likes.is_organic == 1)
][["uid", "item_id"]].drop_duplicates()
graded: dict[int, dict[int, int]] = {}
for raw_uid, raw_item in strong.itertuples(index=False, name=None):
uid, item = int(raw_uid), int(raw_item)
if eligibility.is_eligible(uid, item):
graded.setdefault(uid, {})[item] = 1
for raw_uid, raw_item in organic_likes.itertuples(index=False, name=None):
uid, item = int(raw_uid), int(raw_item)
if eligibility.is_eligible(uid, item):
graded.setdefault(uid, {})[item] = 3
return graded
def load_submission(
path: Path, eligibility: CandidateSet, targets: set[int]
) -> dict[int, list[int]]:
"""Load and validate a public validation ranking.
Files ending in ``.parquet`` are read as parquet; every other suffix is
read as CSV. The required columns must be integer-valued, cover exactly the
target-user set, contain 100 rows and ranks 1 through 100 per user, avoid
duplicate user-item pairs, and contain only cutoff-eligible items.
Args:
path: Ranking file to read.
eligibility: Validation-cutoff candidate membership service.
targets: Exact set of users the ranking must cover.
Returns:
A ``uid -> ranked item IDs`` mapping ordered by ascending rank.
Raises:
ValueError: If the ranking violates the required schema or invariants.
"""
frame = validate_ranking(read_ranking(path), targets)
if not all(
eligibility.is_eligible(uid, item)
for uid, item in frame[["uid", "item_id"]].itertuples(index=False, name=None)
):
raise RankingValidationError(
"submission contains an item rejected by eligibility service"
)
return ranked_items(frame)
def score(
ranked: dict[int, list[int]],
graded: dict[int, dict[int, int]],
) -> dict[str, float | int]:
"""Aggregate per-user NDCG@10 over users with public relevance labels.
Args:
ranked: Ranked item IDs for every target user.
graded: Eligible validation relevance keyed by user and item.
Returns:
The six-decimal ``quality_ndcg@10`` value and number of scored users.
"""
return score_rankings(ranked, graded)
def main() -> None:
"""Run the public evaluator CLI and print its JSON score report."""
targets_frame = pd.read_parquet(DATA / "target_users.parquet")
targets = set(targets_frame.uid.astype(int))
eligibility = CandidateSet(DATA / "validation_eligible_candidates.npz")
graded = validation_truth(eligibility, targets)
if len(sys.argv) != 2:
raise SystemExit(
"usage: evaluate_public.py "
)
path = Path(sys.argv[1])
ranked = load_submission(path, eligibility, targets)
print(json.dumps({"model": str(path), "scores": score(ranked, graded)}, indent=2))
if __name__ == "__main__":
main()
### read_file · 2026-09-29T04:19:39.681Z
{
"file_path": "/task/ranking_evaluation.py"
}
### Tool output · 2026-09-29T04:19:39.699Z
"""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),
}
### read_file · 2026-09-29T04:19:39.681Z
{
"file_path": "/task/requirements.txt"
}
### Tool output · 2026-09-29T04:19:39.699Z
# 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
## Step 4 · agent · 2026-09-29T04:19:42.723Z
### Recorded reasoning summary
Analyzing Installed Packages
I'm reviewing the installed packages to identify any discrepancies with the requirements. Notably, LightGBM, XGBoost, CatBoost, and PyTorch are absent, which seems significant given the presence of scikit-learn and scipy.
### run_shell_command · 2026-09-29T04:19:42.723Z
{
"command": "lscpu | head -n 20; free -h; ls -la /task/data",
"description": "Check system CPU, RAM, and list files in /task/data with sizes."
}
### Tool output · 2026-09-29T04:19:42.758Z
Output: Architecture: x86_64
CPU op-mode(s): 32-bit, 64-bit
Address sizes: 48 bits physical, 48 bits virtual
Byte Order: Little Endian
CPU(s): 8
On-line CPU(s) list: 0-7
Vendor ID: AuthenticAMD
Model name: AMD EPYC 7R13 Processor
CPU family: 25
Model: 1
Thread(s) per core: 2
Core(s) per socket: 4
Socket(s): 1
Stepping: 1
BogoMIPS: 5299.99
Flags: fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ht syscall nx mmxext fxsr_opt pdpe1gb rdtscp lm constant_tsc rep_good nopl xtopology nonstop_tsc cpuid extd_apicid aperfmperf tsc_known_freq pni pclmulqdq ssse3 fma cx16 pcid sse4_1 sse4_2 x2apic movbe popcnt aes xsave avx f16c rdrand hypervisor lahf_lm cmp_legacy cr8_legacy abm sse4a misalignsse 3dnowprefetch topoext ssbd ibrs ibpb stibp vmmcall fsgsbase bmi1 avx2 smep bmi2 invpcid rdseed adx smap clflushopt clwb sha_ni xsaveopt xsavec xgetbv1 clzero xsaveerptr rdpru wbnoinvd arat npt nrip_save vaes vpclmulqdq rdpid
Hypervisor vendor: KVM
Virtualization type: full
L1d cache: 128 KiB (4 instances)
L1i cache: 128 KiB (4 instances)
/usr/bin/bash: line 4: free: command not found
total 347392
dr-xr-xr-x 1 root root 4096 Sep 29 04:14 .
dr-xr-xr-x 1 root root 4096 Sep 29 04:17 ..
-r--r--r-- 1 root root 349352 Sep 29 04:13 candidate_catalog.parquet
-r--r--r-- 1 root root 84127 Sep 29 04:13 target_users.parquet
-r--r--r-- 1 root root 7736341 Sep 29 04:13 test_eligible_candidates.npz
-r--r--r-- 1 root root 1092329 Sep 29 04:14 test_preference_state.parquet
-r--r--r-- 1 root root 857213 Sep 29 04:13 train_dislikes.parquet
-r--r--r-- 1 root root 5374652 Sep 29 04:13 train_likes.parquet
-r--r--r-- 1 root root 319183255 Sep 29 04:13 train_listens.parquet
-r--r--r-- 1 root root 155988 Sep 29 04:13 train_undislikes.parquet
-r--r--r-- 1 root root 1855885 Sep 29 04:13 train_unlikes.parquet
-r--r--r-- 1 root root 36571 Sep 29 04:13 val_dislikes.parquet
-r--r--r-- 1 root root 298484 Sep 29 04:13 val_likes.parquet
-r--r--r-- 1 root root 9947609 Sep 29 04:13 val_listens.parquet
-r--r--r-- 1 root root 7845 Sep 29 04:13 val_undislikes.parquet
-r--r--r-- 1 root root 72536 Sep 29 04:13 val_unlikes.parquet
-r--r--r-- 1 root root 7573821 Sep 29 04:13 validation_eligible_candidates.npz
-r--r--r-- 1 root root 1061761 Sep 29 04:14 validation_preference_state.parquet
Process Group PGID: 1618
## Step 5 · agent · 2026-09-29T04:19:52.722Z
### run_shell_command · 2026-09-29T04:19:52.722Z
{
"description": "Check total and available memory.",
"command": "cat /proc/meminfo | grep -E \"MemTotal|MemAvailable\""
}
### Tool output · 2026-09-29T04:19:52.738Z
Output: MemTotal: 15988964 kB
MemAvailable: 14738800 kB
Process Group PGID: 1624
## Step 6 · agent · 2026-09-29T04:19:55.593Z
### Recorded reasoning summary
Checking Available Resources
I'm currently assessing our computational environment, specifically looking into the ~16 GB RAM and 8 vCPUs. My immediate goal is to identify available Python libraries like SciPy and scikit-learn, and explore any implicit or other relevant packages that might enhance our processing capabilities.
### run_shell_command · 2026-09-29T04:19:55.593Z
{
"command": "python3 -c \"\nfor mod in ['implicit', 'torch', 'scipy', 'sklearn', 'lightgbm', 'xgboost', 'catboost']:\n try:\n __import__(mod)\n print(mod, 'is available')\n except ImportError:\n print(mod, 'NOT available')\n\"",
"description": "Check available ML packages in Python environment."
}
### Tool output · 2026-09-29T04:19:56.979Z
Output: implicit NOT available
torch NOT available
scipy is available
sklearn is available
lightgbm NOT available
xgboost NOT available
catboost NOT available
Process Group PGID: 1628
## Step 7 · agent · 2026-09-29T04:20:06.491Z
### run_shell_command · 2026-09-29T04:20:06.491Z
{
"description": "Check existing contents of /app and /app/solution",
"command": "ls -la /app /app/solution"
}
### Tool output · 2026-09-29T04:20:06.504Z
Output: /app:
total 12
drwxr-xr-x 3 agent agent 4096 Sep 29 04:17 .
drwxr-xr-x 1 root root 4096 Sep 29 04:19 ..
drwxr-xr-x 2 agent agent 4096 Sep 29 04:17 solution
/app/solution:
total 8
drwxr-xr-x 2 agent agent 4096 Sep 29 04:17 .
drwxr-xr-x 3 agent agent 4096 Sep 29 04:17 ..
Process Group PGID: 1646
## Step 8 · agent · 2026-09-29T04:20:14.185Z
### Recorded reasoning summary
Exploring Data Sources
I'm beginning by thoroughly analyzing the provided problem and data. My initial step involves writing a Python script to inspect the `candidate_catalog.parquet` file, focusing on its shapes, column types, and distributions.
### run_shell_command · 2026-09-29T04:20:14.185Z
{
"command": "python3 -c \"\nimport pandas as pd\nimport numpy as np\n\nprint('--- TARGET USERS ---')\ntargets = pd.read_parquet('/task/data/target_users.parquet')\nprint('targets shape:', targets.shape)\nprint(targets.head(3))\n\nprint('--- CATALOG ---')\ncat = pd.read_parquet('/task/data/candidate_catalog.parquet')\nprint('catalog shape:', cat.shape)\nprint(cat.head(3))\n\nprint('--- PREFERENCE STATE ---')\nvpref = pd.read_parquet('/task/data/validation_preference_state.parquet')\nprint('val pref shape:', vpref.shape)\nprint(vpref.head(3))\ntpref = pd.read_parquet('/task/data/test_preference_state.parquet')\nprint('test pref shape:', tpref.shape)\nprint(tpref.head(3))\n\nprint('--- ELIGIBLE CANDIDATES ---')\nwith np.load('/task/data/validation_eligible_candidates.npz') as a:\n uids = a['uids']\n items = a['item_ids']\n packed = a['packed_eligible']\n print('val npz uids:', len(uids), 'items:', len(items), 'packed shape:', packed.shape)\n # unpack a few rows to see count of eligible items\n counts = []\n for r in range(min(100, len(uids))):\n mask = np.unpackbits(packed[r], count=len(items), bitorder='little')\n counts.append(mask.sum())\n print('eligible count per user sample (min/mean/max):', min(counts), np.mean(counts), max(counts))\n\nwith np.load('/task/data/test_eligible_candidates.npz') as a:\n tuids = a['uids']\n titems = a['item_ids']\n tpacked = a['packed_eligible']\n print('test npz uids:', len(tuids), 'items:', len(titems), 'packed shape:', tpacked.shape)\n\"",
"description": "Inspect target users, catalog, preference state, and eligibility archives."
}
### Tool output · 2026-09-29T04:20:15.155Z
Output: --- TARGET USERS ---
targets shape: (8456, 3)
uid recent_positive_listens history_positive_listens
0 100 844.0 2604
1 200 50.0 286
2 300 168.0 359
--- CATALOG ---
catalog shape: (30000, 4)
item_id positive_listens organic_positive_listens popularity_rank
0 5862961 26615 20620 1
1 6901374 25536 17347 2
2 5635052 24691 17194 3
--- PREFERENCE STATE ---
val pref shape: (469124, 3)
uid item_id state
0 100 410868 liked
1 100 732449 liked
2 100 982903 liked
test pref shape: (484776, 3)
uid item_id state
0 100 410868 liked
1 100 732449 liked
2 100 982903 liked
--- ELIGIBLE CANDIDATES ---
val npz uids: 8456 items: 30000 packed shape: (8456, 3750)
eligible count per user sample (min/mean/max): 25547 29098.94 29971
test npz uids: 8456 items: 30000 packed shape: (8456, 3750)
Process Group PGID: 1649
## Step 9 · agent · 2026-09-29T04:20:20.890Z
### Recorded reasoning summary
Analyzing User Data
My analysis reveals a substantial user base within `targets`, numbering 8,456. Concurrently, `candidate_catalog` boasts an impressive 30,000 items. A significant finding is that each user is associated with approximately 29,000 eligible items, a ratio that warrants further investigation into the data structure and its implications. I am now proceeding to cross-reference `validation_eligible_candidates.npz` with `test_eligible_candidates.npz` for deeper insights.
### run_shell_command · 2026-09-29T04:20:20.890Z
{
"command": "python3 -c \"\nimport pandas as pd\nimport numpy as np\n\ncat = pd.read_parquet('/task/data/candidate_catalog.parquet')\nwith np.load('/task/data/validation_eligible_candidates.npz') as a:\n val_items = a['item_ids']\n val_uids = a['uids']\nwith np.load('/task/data/test_eligible_candidates.npz') as a:\n test_items = a['item_ids']\n test_uids = a['uids']\n\ntargets = pd.read_parquet('/task/data/target_users.parquet')\n\nprint('val_items equal cat item_id:', np.array_equal(val_items, cat['item_id'].to_numpy()))\nprint('test_items equal cat item_id:', np.array_equal(test_items, cat['item_id'].to_numpy()))\nprint('val_uids equal targets uid:', np.array_equal(val_uids, targets['uid'].to_numpy()))\nprint('test_uids equal targets uid:', np.array_equal(test_uids, targets['uid'].to_numpy()))\nprint('val_uids equal test_uids:', np.array_equal(val_uids, test_uids))\n\"",
"description": "Check alignment of item_ids and uids across candidate catalog, targets, and npz files."
}
### Tool output · 2026-09-29T04:20:21.610Z
Output: val_items equal cat item_id: True
test_items equal cat item_id: True
val_uids equal targets uid: True
test_uids equal targets uid: True
val_uids equal test_uids: True
Process Group PGID: 1667
## Step 10 · agent · 2026-09-29T04:20:33.604Z
### Recorded reasoning summary
Analyzing Interaction Data
My focus has shifted to examining the training and validation interaction logs. I've confirmed that `val_items`, `test_items`, and `cat['item_id']` are consistent. Similarly, `val_uids`, `test_uids`, and `targets['uid']` show identical content. The dataset comprises 8,456 target users and 30,000 candidate items.
### run_shell_command · 2026-09-29T04:20:33.604Z
{
"command": "python3 -c \"\nimport pandas as pd\n\nfor prefix in ['train', 'val']:\n for name in ['likes', 'dislikes', 'unlikes', 'undislikes', 'listens']:\n fname = f'/task/data/{prefix}_{name}.parquet'\n df = pd.read_parquet(fname)\n print(f'{prefix}_{name}: {len(df):,} rows, cols={list(df.columns)}, t_min={df.timestamp.min()}, t_max={df.timestamp.max()}')\n\"",
"description": "Check summary statistics of interaction parquet files."
}
### Tool output · 2026-09-29T04:20:35.168Z
Output: train_likes: 814,831 rows, cols=['uid', 'timestamp', 'item_id', 'is_organic'], t_min=80, t_max=24786790
train_dislikes: 101,075 rows, cols=['uid', 'timestamp', 'item_id', 'is_organic'], t_min=75, t_max=24786580
train_unlikes: 292,665 rows, cols=['uid', 'timestamp', 'item_id', 'is_organic'], t_min=250, t_max=24786570
train_undislikes: 19,960 rows, cols=['uid', 'timestamp', 'item_id', 'is_organic'], t_min=15950, t_max=24785395
train_listens: 43,484,159 rows, cols=['uid', 'timestamp', 'item_id', 'is_organic', 'played_ratio_pct', 'track_length_seconds'], t_min=0, t_max=24786795
val_likes: 37,795 rows, cols=['uid', 'timestamp', 'item_id', 'is_organic'], t_min=24788815, t_max=25393390
val_dislikes: 3,563 rows, cols=['uid', 'timestamp', 'item_id', 'is_organic'], t_min=24789920, t_max=25393280
val_unlikes: 9,346 rows, cols=['uid', 'timestamp', 'item_id', 'is_organic'], t_min=24789005, t_max=25393385
val_undislikes: 606 rows, cols=['uid', 'timestamp', 'item_id', 'is_organic'], t_min=24810715, t_max=25393035
val_listens: 1,501,697 rows, cols=['uid', 'timestamp', 'item_id', 'is_organic', 'played_ratio_pct', 'track_length_seconds'], t_min=24788600, t_max=25393395
Process Group PGID: 1684
## Step 11 · agent · 2026-09-29T04:20:39.689Z
### Recorded reasoning summary
Analyzing Data Sizes
My current focus is on understanding the scale of the datasets. The `train_listens` and `val_listens` tables are particularly large, with `train_listens` exceeding 43 million rows. I'm also noting the sizes of the `train_likes` and `val_likes` datasets.
### run_shell_command · 2026-09-29T04:20:39.689Z
{
"description": "Check user overlaps in train and val datasets.",
"command": "python3 -c \"\nimport pandas as pd\ntargets = set(pd.read_parquet('/task/data/target_users.parquet')['uid'])\nval_listens = pd.read_parquet('/task/data/val_listens.parquet', columns=['uid'])\nval_uids = set(val_listens['uid'])\nprint(f'Target users: {len(targets)}')\nprint(f'Val listens distinct users: {len(val_uids)}')\nprint(f'Val users in targets: {len(val_uids & targets)}')\n\ntrain_likes = pd.read_parquet('/task/data/train_likes.parquet', columns=['uid'])\ntrain_likes_uids = set(train_likes['uid'])\nprint(f'Train likes distinct users: {len(train_likes_uids)}, in targets: {len(train_likes_uids & targets)}')\n\n# Check a sample of train_listens\ntl_sample = pd.read_parquet('/task/data/train_listens.parquet', columns=['uid'])\ntl_uids = set(tl_sample['uid'])\nprint(f'Train listens distinct users: {len(tl_uids)}, in targets: {len(tl_uids & targets)}')\n\""
}
### Tool output · 2026-09-29T04:20:44.492Z
Output: Target users: 8456
Val listens distinct users: 8196
Val users in targets: 7729
Train likes distinct users: 8144, in targets: 7071
Train listens distinct users: 9238, in targets: 8456
Process Group PGID: 1704
## Step 12 · agent · 2026-09-29T04:20:48.443Z
### Recorded reasoning summary
Confirming Training Data
I've confirmed that all 8,456 target users are now present within the `train_listens` dataset. This constitutes a significant portion of the total 9,238 users. The data is substantial, with an average of approximately 4,700 listens per user across 43.48 million rows, focusing on key columns like `uid`, `item_id`, and `timestamp`.
### run_shell_command · 2026-09-29T04:20:48.443Z
{
"description": "Measure time and memory to load train_listens columns.",
"command": "python3 -c \"\nimport time\nimport pandas as pd\nt0 = time.time()\ndf = pd.read_parquet('/task/data/train_listens.parquet', columns=['uid', 'item_id', 'timestamp', 'played_ratio_pct', 'is_organic'])\nt1 = time.time()\nprint(f'Read in {t1 - t0:.2f}s, shape: {df.shape}, mem: {df.memory_usage().sum() / 1e6:.1f} MB')\n\""
}
### Tool output · 2026-09-29T04:20:49.768Z
Output: Read in 0.53s, shape: (43484159, 5), mem: 652.3 MB
Process Group PGID: 1723
## Step 13 · agent · 2026-09-29T04:21:02.452Z
### Recorded reasoning summary
Analyzing Data Speed
My thoughts are currently focused on the incredible speed and efficiency I've observed. Processing 43 million rows in just over half a second and a mere 652 MB is remarkable. Next, I'm investigating the `train_listens` and `val_listens` to confirm if all entries fall within the specified 30,000 catalog range or if there are outliers.
### run_shell_command · 2026-09-29T04:21:02.452Z
{
"command": "python3 -c \"\nimport pandas as pd\ncat = pd.read_parquet('/task/data/candidate_catalog.parquet')\ncat_items = set(cat['item_id'])\nprint(f'Catalog items: {len(cat_items)}')\n\ntl = pd.read_parquet('/task/data/train_listens.parquet', columns=['item_id'])\ntl_items = set(tl['item_id'])\nprint(f'Train listens distinct items: {len(tl_items)}, in catalog: {len(tl_items & cat_items)}')\n\nvl = pd.read_parquet('/task/data/val_listens.parquet', columns=['item_id'])\nvl_items = set(vl['item_id'])\nprint(f'Val listens distinct items: {len(vl_items)}, in catalog: {len(vl_items & cat_items)}')\n\"",
"description": "Check item sets and overlaps with candidate catalog."
}
### Tool output · 2026-09-29T04:21:08.564Z
Output: Catalog items: 30000
Train listens distinct items: 845395, in catalog: 30000
Val listens distinct items: 191747, in catalog: 29390
Process Group PGID: 1744
## Step 14 · agent · 2026-09-29T04:21:13.707Z
### Recorded reasoning summary
Analyzing Candidate Presence
It's fascinating that every single one of the 30,000 `candidate_catalog` items is found within `train_listens`. My immediate next step is to deeply probe the problem's core requirements and the target objective.
### run_shell_command · 2026-09-29T04:21:13.707Z
{
"description": "Inspect validation ground truth statistics.",
"command": "python3 -c \"\nimport pandas as pd\nfrom ranking_evaluation import CandidateSet\nfrom evaluate_public import validation_truth\n\ntargets = set(pd.read_parquet('/task/data/target_users.parquet')['uid'])\neligibility = CandidateSet('/task/data/validation_eligible_candidates.npz')\ngraded = validation_truth(eligibility, targets)\n\nprint(f'Total target users: {len(targets)}')\nprint(f'Target users with at least one relevant item: {len(graded)}')\n\ngrade_counts = {}\nuser_num_items = []\nfor uid, items in graded.items():\n user_num_items.append(len(items))\n for item, g in items.items():\n grade_counts[g] = grade_counts.get(g, 0) + 1\n\nprint(f'Grade counts across all users: {grade_counts}')\nprint(f'Items per user with labels: min={min(user_num_items)}, median={pd.Series(user_num_items).median()}, mean={pd.Series(user_num_items).mean():.2f}, max={max(user_num_items)}')\n\""
}
### Tool output · 2026-09-29T04:21:14.425Z
Output: Traceback (most recent call last):
File "", line 3, in
ModuleNotFoundError: No module named 'ranking_evaluation'
Exit Code: 1
Process Group PGID: 1765
## Step 15 · agent · 2026-09-29T04:21:19.911Z
### run_shell_command · 2026-09-29T04:21:19.911Z
{
"description": "Inspect validation ground truth statistics with PYTHONPATH set.",
"command": "PYTHONPATH=/task python3 -c \"\nimport pandas as pd\nfrom ranking_evaluation import CandidateSet\nfrom evaluate_public import validation_truth\n\ntargets = set(pd.read_parquet('/task/data/target_users.parquet')['uid'])\neligibility = CandidateSet('/task/data/validation_eligible_candidates.npz')\ngraded = validation_truth(eligibility, targets)\n\nprint(f'Total target users: {len(targets)}')\nprint(f'Target users with at least one relevant item: {len(graded)}')\n\ngrade_counts = {}\nuser_num_items = []\nfor uid, items in graded.items():\n user_num_items.append(len(items))\n for item, g in items.items():\n grade_counts[g] = grade_counts.get(g, 0) + 1\n\nprint(f'Grade counts across all users: {grade_counts}')\nprint(f'Items per user with labels: min={min(user_num_items)}, median={pd.Series(user_num_items).median()}, mean={pd.Series(user_num_items).mean():.2f}, max={max(user_num_items)}')\n\""
}
### Tool output · 2026-09-29T04:21:21.187Z
Output: Total target users: 8456
Target users with at least one relevant item: 4263
Grade counts across all users: {1: 33352, 3: 4417}
Items per user with labels: min=1, median=4.0, mean=8.86, max=442
Process Group PGID: 1776
## Step 16 · agent · 2026-09-29T04:21:28.142Z
### Recorded reasoning summary
Analyzing User Data
My focus has shifted to dissecting user engagement metrics. I'm examining the distribution of positive interactions, particularly distinguishing between high-quality organic engagement (Grade 1) and more general positive signals (Grade 3). This granular breakdown is crucial for understanding user behavior and refining our targeting strategies.
### run_shell_command · 2026-09-29T04:21:28.142Z
{
"description": "Investigate reasons for ineligibility.",
"command": "PYTHONPATH=/task python3 -c \"\nimport pandas as pd\nimport numpy as np\nfrom ranking_evaluation import CandidateSet\n\neligibility = CandidateSet('/task/data/validation_eligible_candidates.npz')\nvpref = pd.read_parquet('/task/data/validation_preference_state.parquet')\nprint('vpref states:', vpref['state'].value_counts())\n\n# Check user 100\nuid = 100\nrow = eligibility.uid_row[uid]\nmask = np.unpackbits(eligibility.packed[row], count=len(eligibility.item_ids), bitorder='little')\nineligible_items = set(eligibility.item_ids[mask == 0])\nprint(f'User {uid}: ineligible items count = {len(ineligible_items)}')\n\n# Check overlap with user 100 preferences\nu100_pref = set(vpref[vpref.uid == uid]['item_id'])\nprint(f'User {uid}: preferences count = {len(u100_pref)}, in ineligible: {len(u100_pref & ineligible_items)}')\n\n# Check overlap with user 100 train listens\ntl = pd.read_parquet('/task/data/train_listens.parquet', columns=['uid', 'item_id'])\nu100_listens = set(tl[tl.uid == uid]['item_id'])\nprint(f'User {uid}: train listens items count = {len(u100_listens)}, in ineligible: {len(u100_listens & ineligible_items)}')\n\""
}
### Tool output · 2026-09-29T04:21:29.379Z
Output: vpref states: state
liked 418435
disliked 50689
Name: count, dtype: int64
User 100: ineligible items count = 682
User 100: preferences count = 15, in ineligible: 15
User 100: train listens items count = 830, in ineligible: 682
Process Group PGID: 1796
## Step 17 · agent · 2026-09-29T04:21:36.639Z
### Recorded reasoning summary
Refining Discovery Criteria
I'm currently focusing on refining the criteria for "ineligible items." My current thinking is that these are tracks the user has already interacted with, as the purpose of "Made for You" is strictly about discovering *new* music.
### run_shell_command · 2026-09-29T04:21:36.639Z
{
"command": "PYTHONPATH=/task python3 -c \"\nimport pandas as pd\nimport numpy as np\nfrom ranking_evaluation import CandidateSet, ranked_items, score_rankings\nfrom evaluate_public import validation_truth\n\ntargets_frame = pd.read_parquet('/task/data/target_users.parquet')\ntargets = set(targets_frame.uid.astype(int))\neligibility = CandidateSet('/task/data/validation_eligible_candidates.npz')\ngraded = validation_truth(eligibility, targets)\n\n# Popularity rank from candidate_catalog\ncat = pd.read_parquet('/task/data/candidate_catalog.parquet')\n# Sort by organic_positive_listens or popularity_rank\nsorted_items = cat.sort_values('popularity_rank')['item_id'].to_numpy()\n\n# For each target user, pick top 100 eligible items\nval_ranks = {}\nfor uid in targets:\n row = eligibility.uid_row[uid]\n mask = np.unpackbits(eligibility.packed[row], count=len(eligibility.item_ids), bitorder='little')\n # eligible item ids:\n # fast lookup:\n eligible_items = eligibility.item_ids[mask == 1]\n # take first 100 in sorted_items that are eligible\n # Or filter sorted_items:\n # Since sorted_items is ordered, let's see how fast we can get top 100\n top100 = []\n # Using boolean indexing over sorted items if sorted_items == eligibility.item_ids\n # Note: eligibility.item_ids is equal to cat['item_id'], which is already sorted by popularity_rank!\n # Let's verify:\n top100 = eligibility.item_ids[mask == 1][:100]\n val_ranks[uid] = top100.tolist()\n\nscores = score_rankings(val_ranks, graded)\nprint('Global catalog order NDCG@10:', scores)\n\n# What if sorted by organic_positive_listens?\ncat_sorted = cat.sort_values('organic_positive_listens', ascending=False)\norganic_order_items = cat_sorted['item_id'].to_numpy()\n# Map to index in eligibility.item_ids\ncol_indices = np.array([eligibility.item_column[item] for item in organic_order_items])\n\nval_ranks_organic = {}\nfor uid in targets:\n row = eligibility.uid_row[uid]\n mask = np.unpackbits(eligibility.packed[row], count=len(eligibility.item_ids), bitorder='little')\n # Filter col_indices where mask is True\n eligible_col_indices = col_indices[mask[col_indices] == 1]\n val_ranks_organic[uid] = eligibility.item_ids[eligible_col_indices[:100]].tolist()\n\nscores_organic = score_rankings(val_ranks_organic, graded)\nprint('Organic positive listens order NDCG@10:', scores_organic)\n\"",
"description": "Evaluate baseline popularity ranking on validation set."
}
### Tool output · 2026-09-29T04:21:39.380Z
Output: Global catalog order NDCG@10: {'quality_ndcg@10': 0.01071, 'quality_users': 4263}
Organic positive listens order NDCG@10: {'quality_ndcg@10': 0.010578, 'quality_users': 4263}
Process Group PGID: 1819
## Step 18 · agent · 2026-09-29T04:21:55.236Z
### Recorded reasoning summary
Analyzing Baseline Performance
My initial analysis confirms the global popularity baseline achieves an NDCG@10 of 0.01071. I am now delving into the specifics of our available data and exploring models that have shown promise for this particular recommendation task.
### Recorded reasoning summary
Exploring Candidate Boundaries
My current focus is on understanding the constraints of per-user candidate eligibility, recognizing that all modeling occurs within this platform-defined boundary. I'm also internalizing the distinct roles of the two public windows for validation and final refitting.
### Recorded reasoning summary
Investigating Algorithm Options
I'm now exploring collaborative filtering and discovery recommendation methods, considering matrix factorization, item-item, and user-user approaches, as well as hybrids. My immediate goal is to identify and potentially implement efficient algorithms using available libraries like scipy and sklearn, noting the absence of implicit and torch.
### Recorded reasoning summary
Refining Candidate Set
I'm now confirming that all evaluated candidates originate from the specified 30,000-item catalog, even when user interaction history includes items beyond this set. This clarifies the scope for subsequent model development.
### run_shell_command · 2026-09-29T04:21:55.236Z
{
"description": "Check breakdown of train listens and likes inside vs outside catalog.",
"command": "python3 -c \"\nimport pandas as pd\ncat = pd.read_parquet('/task/data/candidate_catalog.parquet')\ncat_items = set(cat['item_id'])\n\ntl = pd.read_parquet('/task/data/train_listens.parquet', columns=['uid', 'item_id', 'is_organic', 'played_ratio_pct'])\nin_cat = tl['item_id'].isin(cat_items)\nprint(f'Train listens: total={len(tl):,}, in_cat={in_cat.sum():,} ({in_cat.mean():.1%})')\n\n# Positive listens: played_ratio_pct >= 80 or >= 50\npos50 = tl['played_ratio_pct'] >= 50\npos80 = tl['played_ratio_pct'] >= 80\nprint(f'Listens >= 50%: {pos50.sum():,} ({pos50.mean():.1%}), in cat: {(pos50 & in_cat).sum():,}')\nprint(f'Listens >= 80%: {pos80.sum():,} ({pos80.mean():.1%}), in cat: {(pos80 & in_cat).sum():,}')\nprint(f'Organic Listens >= 80%: {(pos80 & (tl.is_organic == 1)).sum():,}, in cat: {(pos80 & (tl.is_organic == 1) & in_cat).sum():,}')\n\nlikes = pd.read_parquet('/task/data/train_likes.parquet')\nin_cat_likes = likes['item_id'].isin(cat_items)\nprint(f'Train likes: total={len(likes):,}, in_cat={in_cat_likes.sum():,} ({in_cat_likes.mean():.1%})')\n\""
}
### Tool output · 2026-09-29T04:21:59.823Z
Output: Train listens: total=43,484,159, in_cat=31,249,613 (71.9%)
Listens >= 50%: 27,500,755 (63.2%), in cat: 20,056,329
Listens >= 80%: 25,755,197 (59.2%), in cat: 18,801,138
Organic Listens >= 80%: 12,177,583, in cat: 8,699,160
Train likes: total=814,831, in_cat=554,824 (68.1%)
Process Group PGID: 1845
## Step 19 · agent · 2026-09-29T04:22:07.159Z
### Recorded reasoning summary
Analyzing Catalog Item Performance
I'm focusing on the impressive engagement with our core catalog, noting over 70% of interactions occur within these 30,000 items. Approximately 20 million positive listens have been recorded. My next step is to determine the relevant time frame for `train_listens`.
### Recorded reasoning summary
Clarifying Timestamp Units
I'm now examining the timestamp units, suspecting they are in seconds given the range and the context of the training cutoff. This will allow me to accurately define timeframes for user behavior, as recent listening patterns are likely more predictive than older ones.
### run_shell_command · 2026-09-29T04:22:07.159Z
{
"description": "Analyze validation preference state data.",
"command": "python3 -c \"\nimport pandas as pd\nvpref = pd.read_parquet('/task/data/validation_preference_state.parquet')\nprint('Validation preference state:')\nprint(vpref.head(5))\nprint(vpref['state'].value_counts())\nprint(f'Distinct users in vpref: {vpref.uid.nunique()}')\nprint(f'Distinct items in vpref: {vpref.item_id.nunique()}')\n\""
}
### Tool output · 2026-09-29T04:22:07.937Z
Output: Validation preference state:
uid item_id state
0 100 410868 liked
1 100 732449 liked
2 100 982903 liked
3 100 1730273 liked
4 100 2621343 liked
state
liked 418435
disliked 50689
Name: count, dtype: int64
Distinct users in vpref: 7038
Distinct items in vpref: 28814
Process Group PGID: 1868
## Step 20 · agent · 2026-09-29T04:22:11.523Z
### Recorded reasoning summary
Checking Preference Eligibility
I'm currently investigating the eligibility of preference items across all users, specifically cross-referencing `validation_preference_state` against `validation_eligible_candidates.npz`. My initial tests, showing User 100 having all 15 preferences marked as ineligible, suggest a broader pattern to explore.
### run_shell_command · 2026-09-29T04:22:11.523Z
{
"description": "Check if any items in preference state are eligible.",
"command": "PYTHONPATH=/task python3 -c \"\nimport pandas as pd\nfrom ranking_evaluation import CandidateSet\n\neligibility = CandidateSet('/task/data/validation_eligible_candidates.npz')\nvpref = pd.read_parquet('/task/data/validation_preference_state.parquet')\neligible_pref = 0\nfor uid, item in vpref[['uid', 'item_id']].itertuples(index=False, name=None):\n if eligibility.is_eligible(uid, item):\n eligible_pref += 1\nprint(f'Eligible items that are in vpref: {eligible_pref} out of {len(vpref)}')\n\""
}
### Tool output · 2026-09-29T04:22:12.825Z
Output: Eligible items that are in vpref: 0 out of 469124
Process Group PGID: 1886
## Step 21 · agent · 2026-09-29T04:22:24.299Z
### Recorded reasoning summary
Refining Candidate Eligibility
I'm analyzing how to best leverage user preferences, even for items initially deemed ineligible. My current focus is on integrating "like" and "dislike" signals from `preference_state` to inform recommendations, ensuring excluded items still contribute to understanding user taste without direct recommendation.
### Recorded reasoning summary
Exploring Track Similarities
I'm now investigating track metadata. Specifically, I'm checking if features like artist, genre, or audio characteristics are available. So far, the `candidate_catalog.parquet` and `train_listens.parquet` files reveal columns such as `item_id`, `positive_listens`, and `track_length_seconds`, but direct genre or artist information is absent.
### Recorded reasoning summary
Focusing on Interaction Data
I'm realizing this is primarily a collaborative filtering problem, focusing on interaction graphs and behavioral ranking. My current analysis centers on the structure of user-item interactions: the number of users and items, playback details including completion ratio and recency, and explicit feedback like likes and dislikes.
### run_shell_command · 2026-09-29T04:22:24.299Z
{
"description": "Create and validate initial baseline submission for test set.",
"command": "PYTHONPATH=/task python3 -c \"\nimport numpy as np\nimport pandas as pd\nfrom ranking_evaluation import CandidateSet, validate_ranking\n\ntargets_frame = pd.read_parquet('/task/data/target_users.parquet')\ntargets = set(targets_frame.uid.astype(int))\neligibility = CandidateSet('/task/data/test_eligible_candidates.npz')\n\n# Popularity rank from candidate_catalog\ncat = pd.read_parquet('/task/data/candidate_catalog.parquet')\ncat_sorted = cat.sort_values('popularity_rank')\n# item_ids in order\nitems_in_order = cat_sorted['item_id'].to_numpy()\n\nrecords = []\nfor uid in sorted(targets):\n row = eligibility.uid_row[uid]\n mask = np.unpackbits(eligibility.packed[row], count=len(eligibility.item_ids), bitorder='little')\n # eligible item ids:\n eligible_items = eligibility.item_ids[mask == 1]\n top100 = eligible_items[:100]\n for rank, item_id in enumerate(top100, 1):\n records.append({'uid': uid, 'item_id': item_id, 'rank': rank})\n\ndf = pd.DataFrame(records)\nprint('Generated rows:', len(df))\nvalidated = validate_ranking(df, targets)\nprint('Validation passed successfully!')\n\n# Check eligibility for all rows\nall_eligible = all(eligibility.is_eligible(row.uid, row.item_id) for row in validated.itertuples())\nprint('All eligible:', all_eligible)\n\nvalidated.to_parquet('/app/solution/submission.parquet', index=False)\nprint('Saved baseline submission to /app/solution/submission.parquet')\n\""
}
### Tool output · 2026-09-29T04:22:27.886Z
Output: Generated rows: 845600
Validation passed successfully!
All eligible: True
Saved baseline submission to /app/solution/submission.parquet
Process Group PGID: 1910
## Step 22 · agent · 2026-09-29T04:22:34.315Z
### run_shell_command · 2026-09-29T04:22:34.315Z
{
"command": "ls -la /app/solution/submission.parquet",
"description": "Verify baseline submission file exists."
}
### Tool output · 2026-09-29T04:22:34.330Z
Output: -rw-r--r-- 1 agent agent 735840 Sep 29 04:22 /app/solution/submission.parquet
Process Group PGID: 1927
## Step 23 · agent · 2026-09-29T04:22:37.943Z
### Recorded reasoning summary
Testing Evaluation Script
I'm now checking how `evaluate_public.py` functions and its speed for development evaluations. It requires a validation ranking path like `python evaluate_public.py `.
### run_shell_command · 2026-09-29T04:22:37.943Z
{
"description": "Generate and evaluate validation baseline ranking.",
"command": "PYTHONPATH=/task python3 -c \"\nimport numpy as np\nimport pandas as pd\nfrom ranking_evaluation import CandidateSet, validate_ranking\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')\n\ncat = pd.read_parquet('/task/data/candidate_catalog.parquet')\nitems_in_order = cat.sort_values('popularity_rank')['item_id'].to_numpy()\n\nrecords = []\nfor uid in sorted(targets):\n row = eligibility.uid_row[uid]\n mask = np.unpackbits(eligibility.packed[row], count=len(eligibility.item_ids), bitorder='little')\n eligible_items = eligibility.item_ids[mask == 1]\n top100 = eligible_items[:100]\n for rank, item_id in enumerate(top100, 1):\n records.append({'uid': uid, 'item_id': item_id, 'rank': rank})\n\ndf = pd.DataFrame(records)\ndf.to_parquet('/app/solution/val_baseline.parquet', index=False)\n\" && python3 /task/evaluate_public.py /app/solution/val_baseline.parquet"
}
### Tool output · 2026-09-29T04:22:42.754Z
Output: {
"model": "/app/solution/val_baseline.parquet",
"scores": {
"quality_ndcg@10": 0.01071,
"quality_users": 4263
}
}
Process Group PGID: 1930
## Step 24 · agent · 2026-09-29T04:22:46.365Z
### Recorded reasoning summary
Exploring Model Design
My evaluation script is now producing solid results, specifically `0.01071`. I'm leaning towards clearing out temporary files like `/app/solution/val_baseline.parquet` to maintain a cleaner workspace. The next exciting phase involves a deep dive into the model's architecture and rigorous testing protocols.
### update_topic · 2026-09-29T04:22:46.365Z
{
"summary": "Completed initial exploration, data schema analysis, and verified the evaluation pipeline with a valid baseline submission (quality_ndcg@10 = 0.01071). We are now transitioning to designing and testing collaborative filtering algorithms (Item-Item CF, BM25 / cosine similarity, SVD, EASE, hybrid weighting with recency and explicit preferences) to maximize discovery NDCG@10.",
"title": "Modeling and Collaborative Filtering Exploration",
"strategic_intent": "Design and benchmark candidate collaborative filtering and ranking models on the validation window."
}
### Tool output · 2026-09-29T04:22:46.368Z
Current topic: "Modeling and Collaborative Filtering Exploration"
Topic summary: Completed initial exploration, data schema analysis, and verified the evaluation pipeline with a valid baseline submission (quality_ndcg@10 = 0.01071). We are now transitioning to designing and testing collaborative filtering algorithms (Item-Item CF, BM25 / cosine similarity, SVD, EASE, hybrid weighting with recency and explicit preferences) to maximize discovery NDCG@10.
Strategic Intent: Design and benchmark candidate collaborative filtering and ranking models on the validation window.
## Step 25 · agent · 2026-09-29T04:23:01.193Z
### Recorded reasoning summary
Exploring Recommendation Nuances
I'm currently dissecting the core elements of this recommendation challenge. My focus is on a graded NDCG@10 target within a 7-day timeframe, specifically for organic discovery events. This includes defining grade 3 as organic likes, grade 1 as organic listens exceeding 80% of track duration, and ensuring user relevance.
### Recorded reasoning summary
Defining Eligibility Criteria
My current focus is on refining the eligibility rules for candidate recommendations. It's clear that only truly new tracks, those the user hasn't interacted with via listens, likes, or dislikes, should be considered. This ensures a genuine discovery experience rather than reinforcing past behavior.
### Recorded reasoning summary
Testing Recommendation Interactions
I'm now focused on building a Python experimentation harness. I'm exploring various user interaction signals for $R$, including different completion ratio thresholds, the impact of organic listens, explicit likes, and preference states. I'm also investigating recency weighting with exponential decay or half-life, and item frequency normalization techniques like BM25 and TF-IDF.
### run_shell_command · 2026-09-29T04:23:01.193Z
{
"command": "PYTHONPATH=/task python3 -c \"\nimport time\nimport numpy as np\nimport pandas as pd\nimport scipy.sparse as sp\nfrom ranking_evaluation import CandidateSet, score_rankings\nfrom evaluate_public import validation_truth\n\nt0 = time.time()\ntargets_df = pd.read_parquet('/task/data/target_users.parquet')\ntargets = set(targets_df.uid.astype(int))\ntarget_uids = sorted(targets)\nuid_to_idx = {uid: i for i, uid in enumerate(target_uids)}\n\ncat = pd.read_parquet('/task/data/candidate_catalog.parquet')\nitem_ids = cat['item_id'].to_numpy()\nitem_to_idx = {item: i for i, item in enumerate(item_ids)}\nn_users = len(target_uids)\nn_items = len(item_ids)\n\neligibility = CandidateSet('/task/data/validation_eligible_candidates.npz')\ngraded = validation_truth(eligibility, targets)\n\nprint(f'Setup done in {time.time() - t0:.2f}s. Target users: {n_users}, Catalog items: {n_items}')\n\"",
"description": "Test setup and indexing speed for validation benchmarking."
}
### Tool output · 2026-09-29T04:23:02.692Z
Output: Setup done in 0.58s. Target users: 8456, Catalog items: 30000
Process Group PGID: 1965
## Step 26 · agent · 2026-09-29T04:23:15.886Z
### Recorded reasoning summary
Developing Interaction Matrix
I am currently focusing on constructing the user interaction matrix, $R$. My immediate steps involve extracting relevant features from the `train_listens` data, specifically utilizing timestamps. I'm calculating the time difference from a cutoff point, $24786800$, and exploring the application of a half-life decay function.
### run_shell_command · 2026-09-29T04:23:15.886Z
{
"description": "Test User-User Collaborative Filtering model.",
"command": "PYTHONPATH=/task python3 -c \"\nimport time\nimport numpy as np\nimport pandas as pd\nimport scipy.sparse as sp\nfrom ranking_evaluation import CandidateSet, score_rankings\nfrom evaluate_public import validation_truth\n\nt0 = time.time()\ntargets_df = pd.read_parquet('/task/data/target_users.parquet')\ntargets = set(targets_df.uid.astype(int))\ntarget_uids = sorted(targets)\nuid_to_idx = {uid: i for i, uid in enumerate(target_uids)}\n\ncat = pd.read_parquet('/task/data/candidate_catalog.parquet')\nitem_ids = cat['item_id'].to_numpy()\nitem_to_idx = {item: i for i, item in enumerate(item_ids)}\nn_users = len(target_uids)\nn_items = len(item_ids)\n\neligibility = CandidateSet('/task/data/validation_eligible_candidates.npz')\ngraded = validation_truth(eligibility, targets)\n\n# Load train listens\ntl = pd.read_parquet('/task/data/train_listens.parquet', columns=['uid', 'item_id', 'timestamp', 'played_ratio_pct', 'is_organic'])\n# Filter to target users and catalog items\nmask = tl['item_id'].isin(item_to_idx) & tl['uid'].isin(uid_to_idx)\ntl_filtered = tl[mask].copy()\nprint(f'Filtered listens count: {len(tl_filtered):,} in {time.time() - t0:.2f}s')\n\n# Only positive listens: played_ratio_pct >= 50\ntl_filtered = tl_filtered[tl_filtered['played_ratio_pct'] >= 50]\nprint(f'Positive listens count: {len(tl_filtered):,}')\n\n# Recency decay: half-life 60 days (5,184,000 s)\ncutoff = 24786800\nhalflife = 60 * 86400\ndt = np.clip(cutoff - tl_filtered['timestamp'].to_numpy(), 0, None)\ndecay = np.exp(-np.log(2) * dt / halflife)\n# Organic boost\nweight = decay * np.where(tl_filtered['is_organic'].to_numpy() == 1, 1.5, 1.0)\n# Completion ratio boost\nweight *= np.where(tl_filtered['played_ratio_pct'].to_numpy() >= 80, 1.2, 1.0)\n\nu_idx = tl_filtered['uid'].map(uid_to_idx).to_numpy()\ni_idx = tl_filtered['item_id'].map(item_to_idx).to_numpy()\n\nR = sp.csr_matrix((weight, (u_idx, i_idx)), shape=(n_users, n_items))\nprint(f'Matrix R created with shape {R.shape}, nnz={R.nnz:,} in {time.time() - t0:.2f}s')\n\n# Item-item cosine similarity:\n# Normalize item columns: S = (R_norm^T * R_norm)\n# Or user-item normalized\n# First, let's normalize item vectors: L2 norm of columns of R\nitem_norms = np.sqrt(np.asarray(R.power(2).sum(axis=0)).ravel()) + 1e-8\n# S = R^T * R with column normalization\n# Since n_items = 30000, R is (8456, 30000)\n# Notice R has 8456 rows!\n# We can do User-User CF or Item-Item CF:\n# For User-User CF:\n# U_sim = R_norm * R_norm^T is only (8456, 8456)!\n# That is MUCH smaller and faster to compute than (30000, 30000)!\n# Let's check: (8456, 8456) has only 71 million elements (280 MB)!\nuser_norms = np.sqrt(np.asarray(R.power(2).sum(axis=1)).ravel()) + 1e-8\nR_user_norm = sp.diags(1.0 / user_norms) @ R\nprint(f'Normalized R in {time.time() - t0:.2f}s')\n\nt_sim = time.time()\nU_sim = R_user_norm @ R_user_norm.T # (8456, 8456)\nprint(f'User similarity computed in {time.time() - t_sim:.2f}s, shape: {U_sim.shape}, nnz: {U_sim.nnz:,}')\n\n# Reconstructed scores: Scores = U_sim * R\nt_pred = time.time()\nScores = U_sim @ R # (8456, 30000) sparse or dense\nprint(f'Scores computed in {time.time() - t_pred:.2f}s, type: {type(Scores)}')\n\n# Let's evaluate!\n# For each user, we need top 100 eligible items\nval_ranks = {}\nfor i, uid in enumerate(target_uids):\n row = eligibility.uid_row[uid]\n mask = np.unpackbits(eligibility.packed[row], count=len(eligibility.item_ids), bitorder='little')\n # Get user scores as dense array\n user_scores = Scores[i].toarray().ravel()\n # Mask ineligible items by setting their score to -infinity\n user_scores[mask == 0] = -np.inf\n # Top 100\n top100_idx = np.argpartition(user_scores, -100)[-100:]\n top100_idx = top100_idx[np.argsort(-user_scores[top100_idx])]\n val_ranks[uid] = item_ids[top100_idx].tolist()\n\nscores = score_rankings(val_ranks, graded)\nprint(f'User-User CF NDCG@10: {scores} in total {time.time() - t0:.2f}s')\n\""
}
### Tool output · 2026-09-29T04:25:20.918Z
Output: Filtered listens count: 31,062,540 in 8.91s
Positive listens count: 19,959,853
Matrix R created with shape (8456, 30000), nnz=5,514,505 in 10.66s
Normalized R in 10.71s
User similarity computed in 6.85s, shape: (8456, 8456), nnz: 63,275,517
Scores computed in 104.23s, type:
User-User CF NDCG@10: {'quality_ndcg@10': 0.018139, 'quality_users': 4263} in total 123.95s
Process Group PGID: 1992
## Step 27 · agent · 2026-09-29T04:25:32.421Z
### Recorded reasoning summary
Optimizing Similarity Calculation
I've seen a substantial jump in User-User CF performance, a nearly 70% improvement. However, the bottleneck is now the dense matrix multiplication `Scores = U_sim @ R`, which is taking a considerable 104 seconds due to the near-density of $U_{sim}$. I'm now focusing on optimizing this dense matrix operation.
### run_shell_command · 2026-09-29T04:25:32.421Z
{
"description": "Benchmark TruncatedSVD with TF-IDF transformation.",
"command": "PYTHONPATH=/task python3 -c \"\nimport time\nimport numpy as np\nimport pandas as pd\nimport scipy.sparse as sp\nfrom sklearn.decomposition import TruncatedSVD\nfrom ranking_evaluation import CandidateSet, score_rankings\nfrom evaluate_public import validation_truth\n\nt0 = time.time()\ntargets_df = pd.read_parquet('/task/data/target_users.parquet')\ntargets = set(targets_df.uid.astype(int))\ntarget_uids = sorted(targets)\nuid_to_idx = {uid: i for i, uid in enumerate(target_uids)}\n\ncat = pd.read_parquet('/task/data/candidate_catalog.parquet')\nitem_ids = cat['item_id'].to_numpy()\nitem_to_idx = {item: i for i, item in enumerate(item_ids)}\nn_users = len(target_uids)\nn_items = len(item_ids)\n\neligibility = CandidateSet('/task/data/validation_eligible_candidates.npz')\ngraded = validation_truth(eligibility, targets)\n\n# Load train listens\ntl = pd.read_parquet('/task/data/train_listens.parquet', columns=['uid', 'item_id', 'timestamp', 'played_ratio_pct', 'is_organic'])\nmask = tl['item_id'].isin(item_to_idx) & tl['uid'].isin(uid_to_idx)\ntl_filtered = tl[mask].copy()\ntl_filtered = tl_filtered[tl_filtered['played_ratio_pct'] >= 50]\n\ncutoff = 24786800\nhalflife = 45 * 86400\ndt = np.clip(cutoff - tl_filtered['timestamp'].to_numpy(), 0, None)\ndecay = np.exp(-np.log(2) * dt / halflife)\nweight = decay * np.where(tl_filtered['is_organic'].to_numpy() == 1, 1.5, 1.0)\nweight *= np.where(tl_filtered['played_ratio_pct'].to_numpy() >= 80, 1.2, 1.0)\n\nu_idx = tl_filtered['uid'].map(uid_to_idx).to_numpy()\ni_idx = tl_filtered['item_id'].map(item_to_idx).to_numpy()\n\nR = sp.csr_matrix((weight, (u_idx, i_idx)), shape=(n_users, n_items))\n\n# BM25 / TF-IDF transformation:\n# IDF for items:\ndf = np.diff(R.tocsc().indptr) # document frequency (number of users who listened to each item)\nidf = np.log((n_users + 1.0) / (df + 1.0)) + 1.0\nR_tfidf = R @ sp.diags(idf)\n\nprint(f'R_tfidf built in {time.time() - t0:.2f}s')\n\n# Test different SVD components\nfor k in [64, 128, 256]:\n t_svd = time.time()\n svd = TruncatedSVD(n_components=k, algorithm='randomized', n_iter=4, random_state=42)\n U = svd.fit_transform(R_tfidf) # shape: (n_users, k)\n V = svd.components_ # shape: (k, n_items)\n print(f'SVD k={k} fit in {time.time() - t_svd:.2f}s, explained var: {svd.explained_variance_ratio_.sum():.4f}')\n \n # Predict all scores: (n_users, k) @ (k, n_items) -> (n_users, n_items)\n t_pred = time.time()\n Scores = U @ V\n print(f'Scores dense dot product in {time.time() - t_pred:.2f}s')\n \n val_ranks = {}\n for i, uid in enumerate(target_uids):\n row = eligibility.uid_row[uid]\n mask = np.unpackbits(eligibility.packed[row], count=len(eligibility.item_ids), bitorder='little')\n user_scores = Scores[i].copy()\n user_scores[mask == 0] = -1e9\n top100_idx = np.argpartition(user_scores, -100)[-100:]\n top100_idx = top100_idx[np.argsort(-user_scores[top100_idx])]\n val_ranks[uid] = item_ids[top100_idx].tolist()\n \n scores = score_rankings(val_ranks, graded)\n print(f'SVD k={k} NDCG@10: {scores}')\n\""
}
### Tool output · 2026-09-29T04:26:09.114Z
Output: R_tfidf built in 10.49s
SVD k=64 fit in 2.96s, explained var: 0.6227
Scores dense dot product in 0.51s
SVD k=64 NDCG@10: {'quality_ndcg@10': 0.013623, 'quality_users': 4263}
SVD k=128 fit in 4.79s, explained var: 0.6928
Scores dense dot product in 0.91s
SVD k=128 NDCG@10: {'quality_ndcg@10': 0.015787, 'quality_users': 4263}
SVD k=256 fit in 10.77s, explained var: 0.7622
Scores dense dot product in 1.44s
SVD k=256 NDCG@10: {'quality_ndcg@10': 0.015593, 'quality_users': 4263}
Process Group PGID: 2032
## Step 28 · agent · 2026-09-29T04:26:21.203Z
### Recorded reasoning summary
Optimizing Collaborative Filtering
I'm currently exploring ways to enhance collaborative filtering performance. While SVD is remarkably fast, User-User CF, despite its precise neighborhood approach, is proving slow. I'm investigating techniques to significantly reduce its execution time from 100 seconds to just 2 seconds.
### run_shell_command · 2026-09-29T04:26:21.203Z
{
"description": "Test Top-K User-User CF with BM25 item weighting.",
"command": "PYTHONPATH=/task python3 -c \"\nimport time\nimport numpy as np\nimport pandas as pd\nimport scipy.sparse as sp\nfrom ranking_evaluation import CandidateSet, score_rankings\nfrom evaluate_public import validation_truth\n\nt0 = time.time()\ntargets_df = pd.read_parquet('/task/data/target_users.parquet')\ntargets = set(targets_df.uid.astype(int))\ntarget_uids = sorted(targets)\nuid_to_idx = {uid: i for i, uid in enumerate(target_uids)}\n\ncat = pd.read_parquet('/task/data/candidate_catalog.parquet')\nitem_ids = cat['item_id'].to_numpy()\nitem_to_idx = {item: i for i, item in enumerate(item_ids)}\nn_users = len(target_uids)\nn_items = len(item_ids)\n\neligibility = CandidateSet('/task/data/validation_eligible_candidates.npz')\ngraded = validation_truth(eligibility, targets)\n\ntl = pd.read_parquet('/task/data/train_listens.parquet', columns=['uid', 'item_id', 'timestamp', 'played_ratio_pct', 'is_organic'])\nmask = tl['item_id'].isin(item_to_idx) & tl['uid'].isin(uid_to_idx)\ntl_filtered = tl[mask].copy()\ntl_filtered = tl_filtered[tl_filtered['played_ratio_pct'] >= 50]\n\ncutoff = 24786800\nhalflife = 45 * 86400\ndt = np.clip(cutoff - tl_filtered['timestamp'].to_numpy(), 0, None)\ndecay = np.exp(-np.log(2) * dt / halflife)\nweight = decay * np.where(tl_filtered['is_organic'].to_numpy() == 1, 1.5, 1.0)\nweight *= np.where(tl_filtered['played_ratio_pct'].to_numpy() >= 80, 1.2, 1.0)\n\nu_idx = tl_filtered['uid'].map(uid_to_idx).to_numpy()\ni_idx = tl_filtered['item_id'].map(item_to_idx).to_numpy()\n\nR = sp.csr_matrix((weight, (u_idx, i_idx)), shape=(n_users, n_items))\n\n# Let's apply BM25 item weighting:\ndf = np.diff(R.tocsc().indptr)\nidf = np.log((n_users + 1.0) / (df + 1.0)) + 1.0\nR_weighted = R @ sp.diags(idf)\n\nuser_norms = np.sqrt(np.asarray(R_weighted.power(2).sum(axis=1)).ravel()) + 1e-8\nR_user_norm = sp.diags(1.0 / user_norms) @ R_weighted\n\n# User similarity: U_sim = R_user_norm @ R_user_norm.T\nt_sim = time.time()\nU_sim = R_user_norm @ R_user_norm.T\n# Zero out diagonal\nU_sim.setdiag(0)\nprint(f'User sim computed in {time.time() - t_sim:.2f}s')\n\n# Test different top_K neighbor counts\nfor top_k in [50, 100, 200, 500]:\n t_k = time.time()\n # Filter U_sim to keep only top_k per row\n # Efficient row-wise top-k\n U_sim_csr = U_sim.tocsr()\n rows, cols, data = [], [], []\n for i in range(n_users):\n start, end = U_sim_csr.indptr[i], U_sim_csr.indptr[i+1]\n if end - start <= top_k:\n cols.extend(U_sim_csr.indices[start:end])\n data.extend(U_sim_csr.data[start:end])\n rows.extend([i] * (end - start))\n else:\n row_cols = U_sim_csr.indices[start:end]\n row_vals = U_sim_csr.data[start:end]\n top_idx = np.argpartition(row_vals, -top_k)[-top_k:]\n cols.extend(row_cols[top_idx])\n data.extend(row_vals[top_idx])\n rows.extend([i] * top_k)\n U_top = sp.csr_matrix((data, (rows, cols)), shape=(n_users, n_users))\n print(f'Top-{top_k} sparse matrix built in {time.time() - t_k:.2f}s, nnz={U_top.nnz:,}')\n \n t_pred = time.time()\n Scores = U_top @ R # or U_top @ R_weighted\n print(f'Scores computed in {time.time() - t_pred:.2f}s')\n \n val_ranks = {}\n for i, uid in enumerate(target_uids):\n row = eligibility.uid_row[uid]\n mask = np.unpackbits(eligibility.packed[row], count=len(eligibility.item_ids), bitorder='little')\n user_scores = Scores[i].toarray().ravel()\n user_scores[mask == 0] = -np.inf\n top100_idx = np.argpartition(user_scores, -100)[-100:]\n top100_idx = top100_idx[np.argsort(-user_scores[top100_idx])]\n val_ranks[uid] = item_ids[top100_idx].tolist()\n \n scores = score_rankings(val_ranks, graded)\n print(f'Top-{top_k} NDCG@10: {scores}')\n\""
}
### Tool output · 2026-09-29T04:27:14.550Z
Output: /usr/bin/bash: line 99: 2068 Killed PYTHONPATH=/task python3 -c "
import time
import numpy as np
import pandas as pd
import scipy.sparse as sp
from ranking_evaluation import CandidateSet, score_rankings
from evaluate_public import validation_truth
t0 = time.time()
targets_df = pd.read_parquet('/task/data/target_users.parquet')
targets = set(targets_df.uid.astype(int))
target_uids = sorted(targets)
uid_to_idx = {uid: i for i, uid in enumerate(target_uids)}
cat = pd.read_parquet('/task/data/candidate_catalog.parquet')
item_ids = cat['item_id'].to_numpy()
item_to_idx = {item: i for i, item in enumerate(item_ids)}
n_users = len(target_uids)
n_items = len(item_ids)
eligibility = CandidateSet('/task/data/validation_eligible_candidates.npz')
graded = validation_truth(eligibility, targets)
tl = pd.read_parquet('/task/data/train_listens.parquet', columns=['uid', 'item_id', 'timestamp', 'played_ratio_pct', 'is_organic'])
mask = tl['item_id'].isin(item_to_idx) & tl['uid'].isin(uid_to_idx)
tl_filtered = tl[mask].copy()
tl_filtered = tl_filtered[tl_filtered['played_ratio_pct'] >= 50]
cutoff = 24786800
halflife = 45 * 86400
dt = np.clip(cutoff - tl_filtered['timestamp'].to_numpy(), 0, None)
decay = np.exp(-np.log(2) * dt / halflife)
weight = decay * np.where(tl_filtered['is_organic'].to_numpy() == 1, 1.5, 1.0)
weight *= np.where(tl_filtered['played_ratio_pct'].to_numpy() >= 80, 1.2, 1.0)
u_idx = tl_filtered['uid'].map(uid_to_idx).to_numpy()
i_idx = tl_filtered['item_id'].map(item_to_idx).to_numpy()
R = sp.csr_matrix((weight, (u_idx, i_idx)), shape=(n_users, n_items))
# Let's apply BM25 item weighting:
df = np.diff(R.tocsc().indptr)
idf = np.log((n_users + 1.0) / (df + 1.0)) + 1.0
R_weighted = R @ sp.diags(idf)
user_norms = np.sqrt(np.asarray(R_weighted.power(2).sum(axis=1)).ravel()) + 1e-8
R_user_norm = sp.diags(1.0 / user_norms) @ R_weighted
# User similarity: U_sim = R_user_norm @ R_user_norm.T
t_sim = time.time()
U_sim = R_user_norm @ R_user_norm.T
# Zero out diagonal
U_sim.setdiag(0)
print(f'User sim computed in {time.time() - t_sim:.2f}s')
# Test different top_K neighbor counts
for top_k in [50, 100, 200, 500]:
t_k = time.time()
# Filter U_sim to keep only top_k per row
# Efficient row-wise top-k
U_sim_csr = U_sim.tocsr()
rows, cols, data = [], [], []
for i in range(n_users):
start, end = U_sim_csr.indptr[i], U_sim_csr.indptr[i+1]
if end - start <= top_k:
cols.extend(U_sim_csr.indices[start:end])
data.extend(U_sim_csr.data[start:end])
rows.extend([i] * (end - start))
else:
row_cols = U_sim_csr.indices[start:end]
row_vals = U_sim_csr.data[start:end]
top_idx = np.argpartition(row_vals, -top_k)[-top_k:]
cols.extend(row_cols[top_idx])
data.extend(row_vals[top_idx])
rows.extend([i] * top_k)
U_top = sp.csr_matrix((data, (rows, cols)), shape=(n_users, n_users))
print(f'Top-{top_k} sparse matrix built in {time.time() - t_k:.2f}s, nnz={U_top.nnz:,}')
t_pred = time.time()
Scores = U_top @ R # or U_top @ R_weighted
print(f'Scores computed in {time.time() - t_pred:.2f}s')
val_ranks = {}
for i, uid in enumerate(target_uids):
row = eligibility.uid_row[uid]
mask = np.unpackbits(eligibility.packed[row], count=len(eligibility.item_ids), bitorder='little')
user_scores = Scores[i].toarray().ravel()
user_scores[mask == 0] = -np.inf
top100_idx = np.argpartition(user_scores, -100)[-100:]
top100_idx = top100_idx[np.argsort(-user_scores[top100_idx])]
val_ranks[uid] = item_ids[top100_idx].tolist()
scores = score_rankings(val_ranks, graded)
print(f'Top-{top_k} NDCG@10: {scores}')
"
Exit Code: 137
Process Group PGID: 2066
## Step 29 · agent · 2026-09-29T04:27:20.458Z
### Recorded reasoning summary
Investigating Crashes
I'm currently looking into the `Killed` exit code (137). It seems to happen after reading a large parquet file and performing some matrix operations on user similarity. I suspect an out-of-memory issue given the scale of the data.
### run_shell_command · 2026-09-29T04:27:20.458Z
{
"description": "Test pyarrow pushed-down predicate filter on train_listens.",
"command": "python3 -c \"\nimport pyarrow.parquet as pq\nimport pyarrow.compute as pc\nimport time\n\nt0 = time.time()\ntable = pq.read_table('/task/data/train_listens.parquet',\n columns=['uid', 'item_id', 'timestamp', 'played_ratio_pct', 'is_organic'],\n filters=[('played_ratio_pct', '>=', 50)])\nprint(f'Read filtered table in {time.time() - t0:.2f}s, rows: {table.num_rows:,}')\n\""
}
### Tool output · 2026-09-29T04:27:21.784Z
Output: Read filtered table in 0.75s, rows: 27,500,755
Process Group PGID: 2094
## Step 30 · agent · 2026-09-29T04:27:25.526Z
### Recorded reasoning summary
Analyzing Data Speed
My recent focus has been on optimizing data filtering. I'm pleased to report that applying a played ratio filter processed 16 million rows in a mere 0.75 seconds. I'm now exploring the impact of incorporating timestamp filtering.
### run_shell_command · 2026-09-29T04:27:25.526Z
{
"command": "python3 -c \"\nimport pyarrow.parquet as pq\n\ncutoff = 24786800\nfor days in [30, 60, 90, 120, 180]:\n t_thresh = cutoff - days * 86400\n table = pq.read_table('/task/data/train_listens.parquet',\n columns=['timestamp'],\n filters=[('played_ratio_pct', '>=', 50), ('timestamp', '>=', t_thresh)])\n print(f'Listens >= 50% in last {days:3d} days: {table.num_rows:,}')\n\"",
"description": "Check listens count for different recency windows."
}
### Tool output · 2026-09-29T04:27:27.427Z
Output: Listens >= 50% in last 30 days: 3,883,797
Listens >= 50% in last 60 days: 7,417,190
Listens >= 50% in last 90 days: 10,582,861
Listens >= 50% in last 120 days: 13,510,103
Listens >= 50% in last 180 days: 19,038,702
Process Group PGID: 2122
## Step 31 · agent · 2026-09-29T04:27:40.025Z
### Recorded reasoning summary
Analyzing Listen Metrics
My current focus is on understanding user engagement trends by examining listen counts over different periods. I'm comparing the last 90 days (10.5M listens) to the last 60 days (7.4M listens) to identify any shifts in listening patterns. This data will inform the definition of "target users," specifically those with a minimum of 20 positive listens in the 90 days preceding training.
### Recorded reasoning summary
Refining Similarity Calculations
I'm shifting my approach from user-user to item-item collaborative filtering to avoid memory issues. Computing a dense item similarity matrix, and then pruning it to the top-K similar items per item, should drastically reduce memory overhead, bringing the non-zero entries down to manageable levels.
### Recorded reasoning summary
Testing Item-Item Similarity
I'm exploring the feasibility of an item-item collaborative filtering approach by calculating the full item similarity matrix. Specifically, I'm examining if normalizing the user-item interaction matrix and computing its transpose product offers an efficient path to generating this matrix, aiming to leverage its proven effectiveness in recommendation systems.
### run_shell_command · 2026-09-29T04:27:40.025Z
{
"command": "python3 -c \"\nimport numpy as np\nimport scipy.sparse as sp\nimport time\n\n# Create dummy sparse matrix (8456, 30000) with 3M nonzeros\nR = sp.random(8456, 30000, density=0.01, format='csc', dtype=np.float32)\nt0 = time.time()\n# compute 2000 items block:\nblock = R[:, :2000] # (8456, 2000)\nsim_block = (R.T @ block).toarray() # (30000, 2000)\nt1 = time.time()\nprint(f'Block sim shape: {sim_block.shape}, computed in {t1 - t0:.2f}s')\n\"",
"description": "Benchmark sparse block matrix multiplication for item similarity."
}
### Tool output · 2026-09-29T04:27:53.276Z
Output: Block sim shape: (30000, 2000), computed in 0.64s
Process Group PGID: 2146
## Step 32 · agent · 2026-09-29T04:28:09.131Z
### Recorded reasoning summary
Optimizing performance and memory
I've been evaluating the efficiency of processing 2,000 items in 0.64 seconds, projecting that all 30,000 items will take roughly 10 seconds. Memory usage is also within acceptable limits at around 240 MB. My current focus is on scaling this with top-K selection, aiming for a sparse similarity matrix $S$ of $30,000 \times 100$.
### run_shell_command · 2026-09-29T04:28:09.131Z
{
"description": "Run Item-Item CF with block similarity computation and evaluate on validation.",
"command": "PYTHONPATH=/task python3 -c \"\nimport time\nimport numpy as np\nimport pandas as pd\nimport scipy.sparse as sp\nimport pyarrow.parquet as pq\nfrom ranking_evaluation import CandidateSet, score_rankings\nfrom evaluate_public import validation_truth\n\nt0 = time.time()\ntargets_df = pd.read_parquet('/task/data/target_users.parquet')\ntargets = set(targets_df.uid.astype(int))\ntarget_uids = sorted(targets)\nuid_to_idx = {uid: i for i, uid in enumerate(target_uids)}\n\ncat = pd.read_parquet('/task/data/candidate_catalog.parquet')\nitem_ids = cat['item_id'].to_numpy()\nitem_to_idx = {item: i for i, item in enumerate(item_ids)}\nn_users = len(target_uids)\nn_items = len(item_ids)\n\neligibility = CandidateSet('/task/data/validation_eligible_candidates.npz')\ngraded = validation_truth(eligibility, targets)\n\n# Read train listens with filters: played_ratio_pct >= 50\ncutoff = 24786800\n# Let's take listens from the last 120 days\nt_min = cutoff - 120 * 86400\ntable = pq.read_table('/task/data/train_listens.parquet',\n columns=['uid', 'item_id', 'timestamp', 'played_ratio_pct', 'is_organic'],\n filters=[('played_ratio_pct', '>=', 50), ('timestamp', '>=', t_min)])\ndf_tl = table.to_pandas()\nprint(f'Read {len(df_tl):,} listens in {time.time() - t0:.2f}s')\n\n# Filter to catalog and target users\ndf_tl = df_tl[df_tl['item_id'].isin(item_to_idx) & df_tl['uid'].isin(uid_to_idx)]\nprint(f'Filtered to {len(df_tl):,} listens')\n\n# Calculate weights\nhalflife = 45 * 86400\ndt = np.clip(cutoff - df_tl['timestamp'].to_numpy(), 0, None)\ndecay = np.exp(-np.log(2) * dt / halflife)\nweight = decay * np.where(df_tl['is_organic'].to_numpy() == 1, 1.5, 1.0)\nweight *= np.where(df_tl['played_ratio_pct'].to_numpy() >= 80, 1.2, 1.0)\n\n# Group by (uid, item_id) to sum weights (repeat listens indicate strong preference!)\n# Using sparse matrix directly sums duplicates:\nu_idx = df_tl['uid'].map(uid_to_idx).to_numpy()\ni_idx = df_tl['item_id'].map(item_to_idx).to_numpy()\n\n# Also add explicit likes from train_likes!\nlikes = pd.read_parquet('/task/data/train_likes.parquet')\nlikes = likes[likes['item_id'].isin(item_to_idx) & likes['uid'].isin(uid_to_idx)]\nlikes_u = likes['uid'].map(uid_to_idx).to_numpy()\nlikes_i = likes['item_id'].map(item_to_idx).to_numpy()\nlikes_w = np.full(len(likes), 3.0, dtype=np.float32)\n\nall_u = np.concatenate([u_idx, likes_u])\nall_i = np.concatenate([i_idx, likes_i])\nall_w = np.concatenate([weight.astype(np.float32), likes_w])\n\nR = sp.csr_matrix((all_w, (all_u, all_i)), shape=(n_users, n_items), dtype=np.float32)\nprint(f'R matrix constructed: nnz={R.nnz:,}')\n\n# Item-Item Cosine Similarity with BM25 / TF-IDF\n# Item document frequencies:\nitem_freq = np.diff(R.tocsc().indptr)\n# BM25 IDF:\nidf = np.log((n_users - item_freq + 0.5) / (item_freq + 0.5) + 1.0).astype(np.float32)\nR_tfidf = R @ sp.diags(idf)\n\n# Column normalize R_tfidf for cosine similarity\nR_csc = R_tfidf.tocsc()\ncol_norms = np.sqrt(np.asarray(R_csc.power(2).sum(axis=0)).ravel()) + 1e-6\nR_norm = (R_csc @ sp.diags(1.0 / col_norms)).tocsc()\n\nprint(f'Computing Item similarity matrix in blocks...')\nt_sim = time.time()\ntop_k = 100\nblock_size = 2000\nrows, cols, data = [], [], []\n\nfor b_start in range(0, n_items, block_size):\n b_end = min(b_start + block_size, n_items)\n # block is (n_users, b_end - b_start)\n block = R_norm[:, b_start:b_end]\n # dot product with all items: (n_items, n_users) @ (n_users, b_len) -> (n_items, b_len)\n sim = (R_norm.T @ block).toarray()\n \n # Zero out diagonal\n for j in range(b_end - b_start):\n sim[b_start + j, j] = 0.0\n \n # Extract top_k for each column\n for j in range(b_end - b_start):\n col_vals = sim[:, j]\n # find top_k indices\n top_indices = np.argpartition(col_vals, -top_k)[-top_k:]\n top_vals = col_vals[top_indices]\n # filter out zero or negative similarities\n pos_mask = top_vals > 0\n if np.any(pos_mask):\n rows.extend(top_indices[pos_mask])\n cols.extend([b_start + j] * pos_mask.sum())\n data.extend(top_vals[pos_mask])\n\nS = sp.csc_matrix((data, (rows, cols)), shape=(n_items, n_items), dtype=np.float32).tocsr()\nprint(f'Item similarity S constructed in {time.time() - t_sim:.2f}s, nnz={S.nnz:,}')\n\n# Predict scores: Scores = R * S\nt_pred = time.time()\nScores = (R @ S).toarray()\nprint(f'Prediction Scores computed in {time.time() - t_pred:.2f}s, shape: {Scores.shape}')\n\nval_ranks = {}\nfor i, uid in enumerate(target_uids):\n row = eligibility.uid_row[uid]\n mask = np.unpackbits(eligibility.packed[row], count=len(eligibility.item_ids), bitorder='little')\n user_scores = Scores[i].copy()\n user_scores[mask == 0] = -1e9\n top100_idx = np.argpartition(user_scores, -100)[-100:]\n top100_idx = top100_idx[np.argsort(-user_scores[top100_idx])]\n val_ranks[uid] = item_ids[top100_idx].tolist()\n\nscores = score_rankings(val_ranks, graded)\nprint(f'Item-Item CF NDCG@10: {scores} in total {time.time() - t0:.2f}s')\n\""
}
### Tool output · 2026-09-29T04:28:42.999Z
Output: Read 13,510,103 listens in 1.05s
Filtered to 9,917,232 listens
R matrix constructed: nnz=3,642,376
Computing Item similarity matrix in blocks...
Item similarity S constructed in 22.22s, nnz=2,997,192
Prediction Scores computed in 3.22s, shape: (8456, 30000)
Item-Item CF NDCG@10: {'quality_ndcg@10': 0.014436, 'quality_users': 4263} in total 32.65s
Process Group PGID: 2163
## Step 33 · agent · 2026-09-29T04:28:55.156Z
### Recorded reasoning summary
Analyzing User-User CF
My recent focus has been on the unexpected but excellent performance of User-User Collaborative Filtering. I'm digging into why it's outperforming other methods, particularly the baseline and SVD, in this music streaming context, suspecting genre specificity plays a role.
### Recorded reasoning summary
Exploring User-User CF Performance
I'm keenly observing the strong results from User-User CF, particularly its effectiveness in identifying niche listening personas. I'm now investigating the computational aspects, specifically exploring efficient matrix multiplication strategies for the similarity matrix and user-item interaction matrix, aiming to optimize performance.
### run_shell_command · 2026-09-29T04:28:55.156Z
{
"command": "python3 -c \"\nimport numpy as np\nimport time\n\nA = np.random.randn(8456, 8456).astype(np.float32)\n# R_dense (8456, 30000) would be 1 GB.\n# We can do chunks of columns of R!\n# E.g. 5000 columns at a time: (8456, 5000) is 169 MB!\nB_chunk = np.random.randn(8456, 5000).astype(np.float32)\n\nt0 = time.time()\nC = A @ B_chunk\nt1 = time.time()\nprint(f'Chunk (8456, 8456) @ (8456, 5000) computed in {t1 - t0:.2f}s')\n\"",
"description": "Benchmark chunked dense matrix multiplication."
}
### Tool output · 2026-09-29T04:29:01.148Z
Output: Chunk (8456, 8456) @ (8456, 5000) computed in 3.39s
Process Group PGID: 2198
## Step 34 · agent · 2026-09-29T04:29:16.221Z
### Recorded reasoning summary
Optimizing Performance Now
I'm thrilled with these performance metrics. Processing 5,000 columns in under 4 seconds is excellent. Scaling this to 30,000 items across six chunks is projected to take just 20 seconds, with a modest memory footprint of 169MB per chunk. I'm also exploring the impact of non-linear transformations on $U_{sim}$.
### Recorded reasoning summary
Refining Similarity Calculations
I'm now focusing on refining similarity metrics. I'm experimenting with non-linear transformations, specifically exponentiating $U_{sim}$ to emphasize closer relationships. I'm also incorporating user feedback signals, considering all listen history with exponential decay, and evaluating BM25/TF-IDF on the interaction matrix. Further exploration involves combining user-user collaborative filtering with item popularity or SVD, and I'm developing a Python script to measure validation NDCG@10 across these variations.
### run_shell_command · 2026-09-29T04:29:16.221Z
{
"description": "Test power alpha on User-User CF with explicit preference state.",
"command": "PYTHONPATH=/task python3 -c \"\nimport time\nimport numpy as np\nimport pandas as pd\nimport scipy.sparse as sp\nimport pyarrow.parquet as pq\nfrom ranking_evaluation import CandidateSet, score_rankings\nfrom evaluate_public import validation_truth\n\nt0 = time.time()\ntargets_df = pd.read_parquet('/task/data/target_users.parquet')\ntargets = set(targets_df.uid.astype(int))\ntarget_uids = sorted(targets)\nuid_to_idx = {uid: i for i, uid in enumerate(target_uids)}\n\ncat = pd.read_parquet('/task/data/candidate_catalog.parquet')\nitem_ids = cat['item_id'].to_numpy()\nitem_to_idx = {item: i for i, item in enumerate(item_ids)}\nn_users = len(target_uids)\nn_items = len(item_ids)\n\neligibility = CandidateSet('/task/data/validation_eligible_candidates.npz')\ngraded = validation_truth(eligibility, targets)\n\n# Load train listens\ncutoff = 24786800\ntable = pq.read_table('/task/data/train_listens.parquet',\n columns=['uid', 'item_id', 'timestamp', 'played_ratio_pct', 'is_organic'],\n filters=[('played_ratio_pct', '>=', 50)])\ndf_tl = table.to_pandas()\ndf_tl = df_tl[df_tl['item_id'].isin(item_to_idx) & df_tl['uid'].isin(uid_to_idx)]\n\n# Calculate weights with recency decay\nhalflife = 60 * 86400\ndt = np.clip(cutoff - df_tl['timestamp'].to_numpy(), 0, None)\ndecay = np.exp(-np.log(2) * dt / halflife)\nweight = decay * np.where(df_tl['is_organic'].to_numpy() == 1, 1.5, 1.0)\nweight *= np.where(df_tl['played_ratio_pct'].to_numpy() >= 80, 1.2, 1.0)\n\nu_idx = df_tl['uid'].map(uid_to_idx).to_numpy()\ni_idx = df_tl['item_id'].map(item_to_idx).to_numpy()\n\n# Also add explicit likes from train_likes and validation_preference_state\nlikes = pd.read_parquet('/task/data/train_likes.parquet')\nlikes = likes[likes['item_id'].isin(item_to_idx) & likes['uid'].isin(uid_to_idx)]\nlikes_u = likes['uid'].map(uid_to_idx).to_numpy()\nlikes_i = likes['item_id'].map(item_to_idx).to_numpy()\n# Likes weight: recency decay as well\ndt_likes = np.clip(cutoff - likes['timestamp'].to_numpy(), 0, None)\nlikes_w = 4.0 * np.exp(-np.log(2) * dt_likes / halflife)\n\n# Active preference state liked tracks\nvpref = pd.read_parquet('/task/data/validation_preference_state.parquet')\nvpref_liked = vpref[(vpref['state'] == 'liked') & vpref['item_id'].isin(item_to_idx) & vpref['uid'].isin(uid_to_idx)]\nvpref_u = vpref_liked['uid'].map(uid_to_idx).to_numpy()\nvpref_i = vpref_liked['item_id'].map(item_to_idx).to_numpy()\nvpref_w = np.full(len(vpref_liked), 5.0, dtype=np.float32)\n\nall_u = np.concatenate([u_idx, likes_u, vpref_u])\nall_i = np.concatenate([i_idx, likes_i, vpref_i])\nall_w = np.concatenate([weight.astype(np.float32), likes_w.astype(np.float32), vpref_w])\n\nR = sp.csr_matrix((all_w, (all_u, all_i)), shape=(n_users, n_items), dtype=np.float32)\nprint(f'R matrix constructed in {time.time() - t0:.2f}s: shape={R.shape}, nnz={R.nnz:,}')\n\n# BM25 / TF-IDF transformation:\n# Item document frequency\ndf_item = np.diff(R.tocsc().indptr)\nidf = np.log((n_users - df_item + 0.5) / (df_item + 0.5) + 1.0).astype(np.float32)\nR_tfidf = (R @ sp.diags(idf)).tocsr()\n\n# User cosine normalization\nuser_norms = np.sqrt(np.asarray(R_tfidf.power(2).sum(axis=1)).ravel()) + 1e-6\nR_user_norm = (sp.diags(1.0 / user_norms) @ R_tfidf).tocsr()\n\n# Compute dense user similarity matrix\nt_sim = time.time()\nU_sim = (R_user_norm @ R_user_norm.T).toarray() # (8456, 8456)\nnp.fill_diagonal(U_sim, 0.0)\nprint(f'User similarity matrix computed in {time.time() - t_sim:.2f}s, min={U_sim.min():.4f}, max={U_sim.max():.4f}, mean={U_sim.mean():.4f}')\n\n# Test power alpha\nfor alpha in [1.0, 1.5, 2.0, 3.0]:\n t_test = time.time()\n # Apply power: sign(U_sim) * |U_sim|^alpha\n U_pow = np.sign(U_sim) * (np.abs(U_sim) ** alpha)\n # Threshold out negative or near-zero similarities\n U_pow[U_pow < 0] = 0.0\n \n # Compute scores in chunks of items\n Scores = np.zeros((n_users, n_items), dtype=np.float32)\n chunk_size = 5000\n for c_start in range(0, n_items, chunk_size):\n c_end = min(c_start + chunk_size, n_items)\n R_chunk = R_tfidf[:, c_start:c_end].toarray()\n Scores[:, c_start:c_end] = U_pow @ R_chunk\n \n val_ranks = {}\n for i, uid in enumerate(target_uids):\n row = eligibility.uid_row[uid]\n mask = np.unpackbits(eligibility.packed[row], count=len(eligibility.item_ids), bitorder='little')\n user_scores = Scores[i].copy()\n user_scores[mask == 0] = -1e9\n top100_idx = np.argpartition(user_scores, -100)[-100:]\n top100_idx = top100_idx[np.argsort(-user_scores[top100_idx])]\n val_ranks[uid] = item_ids[top100_idx].tolist()\n \n scores = score_rankings(val_ranks, graded)\n print(f'User-User CF (alpha={alpha:.1f}) NDCG@10: {scores} in {time.time() - t_test:.2f}s')\n\""
}
### Tool output · 2026-09-29T04:31:00.245Z
Output: R matrix constructed in 7.80s: shape=(8456, 30000), nnz=5,564,723
User similarity matrix computed in 6.52s, min=0.0000, max=0.9947, mean=0.0114
User-User CF (alpha=1.0) NDCG@10: {'quality_ndcg@10': 0.027657, 'quality_users': 4263} in 21.83s
User-User CF (alpha=1.5) NDCG@10: {'quality_ndcg@10': 0.029169, 'quality_users': 4263} in 22.35s
User-User CF (alpha=2.0) NDCG@10: {'quality_ndcg@10': 0.028259, 'quality_users': 4263} in 21.86s
User-User CF (alpha=3.0) NDCG@10: {'quality_ndcg@10': 0.021735, 'quality_users': 4263} in 22.36s
Process Group PGID: 2208
## Step 35 · agent · 2026-09-29T04:31:44.399Z
### Recorded reasoning summary
Analyzing Performance Gains
I'm reviewing the recent surge in NDCG@10, nearly tripling from 0.01071 to 0.029169. My focus now is dissecting the contributors: recency decay, completion/organic flags, explicit likes and active preference states, and the BM25 IDF item.
### Recorded reasoning summary
Investigating Negative Signals
I'm exploring the impact of negative signals, specifically dislikes. I'm considering how to incorporate explicit dislikes and disliked items from similar users. Additionally, I'm evaluating the sensitivity of performance to the halflife parameter, explicit like weights, and potential blending strategies with other recommendation approaches like Item-Item CF.
### run_shell_command · 2026-09-29T04:31:44.399Z
{
"description": "Tune halflife, log1p transformation, and alpha.",
"command": "PYTHONPATH=/task python3 -c \"\nimport time\nimport numpy as np\nimport pandas as pd\nimport scipy.sparse as sp\nimport pyarrow.parquet as pq\nfrom ranking_evaluation import CandidateSet, score_rankings\nfrom evaluate_public import validation_truth\n\ntargets_df = pd.read_parquet('/task/data/target_users.parquet')\ntargets = set(targets_df.uid.astype(int))\ntarget_uids = sorted(targets)\nuid_to_idx = {uid: i for i, uid in enumerate(target_uids)}\n\ncat = pd.read_parquet('/task/data/candidate_catalog.parquet')\nitem_ids = cat['item_id'].to_numpy()\nitem_to_idx = {item: i for i, item in enumerate(item_ids)}\nn_users = len(target_uids)\nn_items = len(item_ids)\n\neligibility = CandidateSet('/task/data/validation_eligible_candidates.npz')\ngraded = validation_truth(eligibility, targets)\n\ncutoff = 24786800\ntable = pq.read_table('/task/data/train_listens.parquet',\n columns=['uid', 'item_id', 'timestamp', 'played_ratio_pct', 'is_organic'],\n filters=[('played_ratio_pct', '>=', 50)])\ndf_tl = table.to_pandas()\ndf_tl = df_tl[df_tl['item_id'].isin(item_to_idx) & df_tl['uid'].isin(uid_to_idx)]\n\nlikes = pd.read_parquet('/task/data/train_likes.parquet')\nlikes = likes[likes['item_id'].isin(item_to_idx) & likes['uid'].isin(uid_to_idx)]\nlikes_u = likes['uid'].map(uid_to_idx).to_numpy()\nlikes_i = likes['item_id'].map(item_to_idx).to_numpy()\n\nvpref = pd.read_parquet('/task/data/validation_preference_state.parquet')\nvpref_liked = vpref[(vpref['state'] == 'liked') & vpref['item_id'].isin(item_to_idx) & vpref['uid'].isin(uid_to_idx)]\nvpref_u = vpref_liked['uid'].map(uid_to_idx).to_numpy()\nvpref_i = vpref_liked['item_id'].map(item_to_idx).to_numpy()\nvpref_w = np.full(len(vpref_liked), 5.0, dtype=np.float32)\n\nfor halflife_days in [30, 45, 60, 90]:\n halflife = halflife_days * 86400\n dt = np.clip(cutoff - df_tl['timestamp'].to_numpy(), 0, None)\n decay = np.exp(-np.log(2) * dt / halflife)\n weight = decay * np.where(df_tl['is_organic'].to_numpy() == 1, 1.5, 1.0)\n weight *= np.where(df_tl['played_ratio_pct'].to_numpy() >= 80, 1.2, 1.0)\n \n u_idx = df_tl['uid'].map(uid_to_idx).to_numpy()\n i_idx = df_tl['item_id'].map(item_to_idx).to_numpy()\n \n dt_likes = np.clip(cutoff - likes['timestamp'].to_numpy(), 0, None)\n likes_w = 4.0 * np.exp(-np.log(2) * dt_likes / halflife)\n \n all_u = np.concatenate([u_idx, likes_u, vpref_u])\n all_i = np.concatenate([i_idx, likes_i, vpref_i])\n all_w = np.concatenate([weight.astype(np.float32), likes_w.astype(np.float32), vpref_w])\n \n R = sp.csr_matrix((all_w, (all_u, all_i)), shape=(n_users, n_items), dtype=np.float32)\n \n # Sublinear scaling: log(1 + R)\n R.data = np.log1p(R.data)\n \n df_item = np.diff(R.tocsc().indptr)\n idf = np.log((n_users - df_item + 0.5) / (df_item + 0.5) + 1.0).astype(np.float32)\n R_tfidf = (R @ sp.diags(idf)).tocsr()\n \n user_norms = np.sqrt(np.asarray(R_tfidf.power(2).sum(axis=1)).ravel()) + 1e-6\n R_user_norm = (sp.diags(1.0 / user_norms) @ R_tfidf).tocsr()\n \n U_sim = (R_user_norm @ R_user_norm.T).toarray()\n np.fill_diagonal(U_sim, 0.0)\n \n for alpha in [1.3, 1.5, 1.7]:\n U_pow = np.sign(U_sim) * (np.abs(U_sim) ** alpha)\n U_pow[U_pow < 0] = 0.0\n \n Scores = np.zeros((n_users, n_items), dtype=np.float32)\n chunk_size = 5000\n for c_start in range(0, n_items, chunk_size):\n c_end = min(c_start + chunk_size, n_items)\n Scores[:, c_start:c_end] = U_pow @ R_tfidf[:, c_start:c_end].toarray()\n \n val_ranks = {}\n for i, uid in enumerate(target_uids):\n row = eligibility.uid_row[uid]\n mask = np.unpackbits(eligibility.packed[row], count=len(eligibility.item_ids), bitorder='little')\n user_scores = Scores[i].copy()\n user_scores[mask == 0] = -1e9\n top100_idx = np.argpartition(user_scores, -100)[-100:]\n top100_idx = top100_idx[np.argsort(-user_scores[top100_idx])]\n val_ranks[uid] = item_ids[top100_idx].tolist()\n \n scores = score_rankings(val_ranks, graded)\n print(f'Halflife {halflife_days:2d}d, log1p, alpha={alpha:.1f} -> NDCG@10: {scores[\\\"quality_ndcg@10\\\"]:.6f}')\n\""
}
### Tool output · 2026-09-29T04:36:44.630Z
Command was automatically cancelled because it exceeded the timeout of 5.0 minutes without output. There was no output before it was cancelled.
## Step 36 · agent · 2026-09-29T04:36:54.942Z
### Recorded reasoning summary
Optimizing the Loop
I'm focusing on enhancing the prediction loop's efficiency. My current efforts are directed at streamlining the nested computations that led to the timeout, specifically addressing the matrix multiplications that consume significant processing time.
### run_shell_command · 2026-09-29T04:36:54.942Z
{
"command": "python3 -c \"\nimport numpy as np\nimport scipy.sparse as sp\nimport time\n\nU_top = sp.random(8456, 8456, density=150.0/8456, format='csr', dtype=np.float32)\nR = sp.random(8456, 30000, density=0.015, format='csr', dtype=np.float32)\n\nt0 = time.time()\nScores = U_top @ R\nt1 = time.time()\nprint(f'Scores sparse matrix product shape: {Scores.shape}, nnz: {Scores.nnz:,}, computed in {t1 - t0:.2f}s')\n\"",
"description": "Benchmark sparse-sparse matrix multiplication with top-K neighbors."
}
### Tool output · 2026-09-29T04:37:14.662Z
Output: Scores sparse matrix product shape: (8456, 30000), nnz: 226,948,381, computed in 5.54s
Process Group PGID: 2303
## Step 37 · agent · 2026-09-29T04:37:28.849Z
### Recorded reasoning summary
Testing Neighbor Influence
I'm currently analyzing the impact of neighbor selection strategies on collaborative filtering accuracy. My focus is on whether limiting neighbors to a top-K set ($K=100, 150$) yields better NDCG scores than considering all neighbors, and initial observations suggest a strong positive correlation with top-K approaches.
### run_shell_command · 2026-09-29T04:37:28.849Z
{
"description": "Benchmark top_K user neighbors vs full dense user matrix.",
"command": "PYTHONPATH=/task python3 -c \"\nimport time\nimport numpy as np\nimport pandas as pd\nimport scipy.sparse as sp\nimport pyarrow.parquet as pq\nfrom ranking_evaluation import CandidateSet, score_rankings\nfrom evaluate_public import validation_truth\n\nt0 = time.time()\ntargets_df = pd.read_parquet('/task/data/target_users.parquet')\ntargets = set(targets_df.uid.astype(int))\ntarget_uids = sorted(targets)\nuid_to_idx = {uid: i for i, uid in enumerate(target_uids)}\n\ncat = pd.read_parquet('/task/data/candidate_catalog.parquet')\nitem_ids = cat['item_id'].to_numpy()\nitem_to_idx = {item: i for i, item in enumerate(item_ids)}\nn_users = len(target_uids)\nn_items = len(item_ids)\n\neligibility = CandidateSet('/task/data/validation_eligible_candidates.npz')\ngraded = validation_truth(eligibility, targets)\n\ncutoff = 24786800\nhalflife = 45 * 86400\ntable = pq.read_table('/task/data/train_listens.parquet',\n columns=['uid', 'item_id', 'timestamp', 'played_ratio_pct', 'is_organic'],\n filters=[('played_ratio_pct', '>=', 50)])\ndf_tl = table.to_pandas()\ndf_tl = df_tl[df_tl['item_id'].isin(item_to_idx) & df_tl['uid'].isin(uid_to_idx)]\n\ndt = np.clip(cutoff - df_tl['timestamp'].to_numpy(), 0, None)\ndecay = np.exp(-np.log(2) * dt / halflife)\nweight = decay * np.where(df_tl['is_organic'].to_numpy() == 1, 1.5, 1.0)\nweight *= np.where(df_tl['played_ratio_pct'].to_numpy() >= 80, 1.2, 1.0)\n\nu_idx = df_tl['uid'].map(uid_to_idx).to_numpy()\ni_idx = df_tl['item_id'].map(item_to_idx).to_numpy()\n\nlikes = pd.read_parquet('/task/data/train_likes.parquet')\nlikes = likes[likes['item_id'].isin(item_to_idx) & likes['uid'].isin(uid_to_idx)]\nlikes_u = likes['uid'].map(uid_to_idx).to_numpy()\nlikes_i = likes['item_id'].map(item_to_idx).to_numpy()\ndt_likes = np.clip(cutoff - likes['timestamp'].to_numpy(), 0, None)\nlikes_w = 4.0 * np.exp(-np.log(2) * dt_likes / halflife)\n\nvpref = pd.read_parquet('/task/data/validation_preference_state.parquet')\nvpref_liked = vpref[(vpref['state'] == 'liked') & vpref['item_id'].isin(item_to_idx) & vpref['uid'].isin(uid_to_idx)]\nvpref_u = vpref_liked['uid'].map(uid_to_idx).to_numpy()\nvpref_i = vpref_liked['item_id'].map(item_to_idx).to_numpy()\nvpref_w = np.full(len(vpref_liked), 5.0, dtype=np.float32)\n\nall_u = np.concatenate([u_idx, likes_u, vpref_u])\nall_i = np.concatenate([i_idx, likes_i, vpref_i])\nall_w = np.concatenate([weight.astype(np.float32), likes_w.astype(np.float32), vpref_w])\n\nR = sp.csr_matrix((all_w, (all_u, all_i)), shape=(n_users, n_items), dtype=np.float32)\nR.data = np.log1p(R.data)\n\ndf_item = np.diff(R.tocsc().indptr)\nidf = np.log((n_users - df_item + 0.5) / (df_item + 0.5) + 1.0).astype(np.float32)\nR_tfidf = (R @ sp.diags(idf)).tocsr()\n\nuser_norms = np.sqrt(np.asarray(R_tfidf.power(2).sum(axis=1)).ravel()) + 1e-6\nR_user_norm = (sp.diags(1.0 / user_norms) @ R_tfidf).tocsr()\n\nU_sim = (R_user_norm @ R_user_norm.T).toarray()\nnp.fill_diagonal(U_sim, 0.0)\nprint(f'Computed U_sim in {time.time() - t0:.2f}s')\n\nfor top_k in [100, 200, 400, None]:\n t_test = time.time()\n if top_k is not None:\n # Keep top_k per row in U_sim\n idx = np.argpartition(U_sim, -top_k, axis=1)[:, -top_k:]\n rows = np.repeat(np.arange(n_users), top_k)\n cols = idx.ravel()\n vals = U_sim[rows, cols]\n vals = np.clip(vals, 0, None) ** 1.5\n U_sparse = sp.csr_matrix((vals, (rows, cols)), shape=(n_users, n_users), dtype=np.float32)\n Scores = (U_sparse @ R_tfidf).toarray()\n else:\n U_pow = np.clip(U_sim, 0, None) ** 1.5\n Scores = np.zeros((n_users, n_items), dtype=np.float32)\n for c in range(0, n_items, 5000):\n Scores[:, c:c+5000] = U_pow @ R_tfidf[:, c:c+5000].toarray()\n \n val_ranks = {}\n for i, uid in enumerate(target_uids):\n row = eligibility.uid_row[uid]\n mask = np.unpackbits(eligibility.packed[row], count=len(eligibility.item_ids), bitorder='little')\n user_scores = Scores[i].copy()\n user_scores[mask == 0] = -1e9\n top100_idx = np.argpartition(user_scores, -100)[-100:]\n top100_idx = top100_idx[np.argsort(-user_scores[top100_idx])]\n val_ranks[uid] = item_ids[top100_idx].tolist()\n \n scores = score_rankings(val_ranks, graded)\n print(f'top_k={top_k} -> NDCG@10: {scores[\\\"quality_ndcg@10\\\"]:.6f} in {time.time() - t_test:.2f}s')\n\""
}
### Tool output · 2026-09-29T04:38:34.004Z
Output: Computed U_sim in 14.57s
top_k=100 -> NDCG@10: 0.029285 in 6.17s
top_k=200 -> NDCG@10: 0.030562 in 8.50s
top_k=400 -> NDCG@10: 0.031580 in 12.49s
top_k=None -> NDCG@10: 0.032413 in 22.33s
Process Group PGID: 2320
## Step 38 · agent · 2026-09-29T04:39:06.437Z
### Recorded reasoning summary
Improving NDCG Score
I've achieved a significant improvement, with NDCG@10 now at 0.032413, more than tripling the baseline. This jump was primarily driven by incorporating `np.log1p(R.data)` and utilizing `top_k=None` with soft-thresholding.
### run_shell_command · 2026-09-29T04:39:06.437Z
{
"command": "PYTHONPATH=/task python3 -c \"\nimport time\nimport numpy as np\nimport pandas as pd\nimport scipy.sparse as sp\nimport pyarrow.parquet as pq\nfrom ranking_evaluation import CandidateSet, score_rankings\nfrom evaluate_public import validation_truth\n\ntargets_df = pd.read_parquet('/task/data/target_users.parquet')\ntargets = set(targets_df.uid.astype(int))\ntarget_uids = sorted(targets)\nuid_to_idx = {uid: i for i, uid in enumerate(target_uids)}\n\ncat = pd.read_parquet('/task/data/candidate_catalog.parquet')\nitem_ids = cat['item_id'].to_numpy()\nitem_to_idx = {item: i for i, item in enumerate(item_ids)}\nn_users = len(target_uids)\nn_items = len(item_ids)\n\neligibility = CandidateSet('/task/data/validation_eligible_candidates.npz')\ngraded = validation_truth(eligibility, targets)\n\ncutoff = 24786800\nhalflife = 45 * 86400\ntable = pq.read_table('/task/data/train_listens.parquet',\n columns=['uid', 'item_id', 'timestamp', 'played_ratio_pct', 'is_organic'],\n filters=[('played_ratio_pct', '>=', 50)])\ndf_tl = table.to_pandas()\ndf_tl = df_tl[df_tl['item_id'].isin(item_to_idx) & df_tl['uid'].isin(uid_to_idx)]\n\n# Compare organic multiplier: 1.5 vs 2.0 vs 2.5\nfor org_mult in [1.5, 2.0, 2.5]:\n dt = np.clip(cutoff - df_tl['timestamp'].to_numpy(), 0, None)\n decay = np.exp(-np.log(2) * dt / halflife)\n weight = decay * np.where(df_tl['is_organic'].to_numpy() == 1, org_mult, 1.0)\n weight *= np.where(df_tl['played_ratio_pct'].to_numpy() >= 80, 1.2, 1.0)\n \n u_idx = df_tl['uid'].map(uid_to_idx).to_numpy()\n i_idx = df_tl['item_id'].map(item_to_idx).to_numpy()\n \n likes = pd.read_parquet('/task/data/train_likes.parquet')\n likes = likes[likes['item_id'].isin(item_to_idx) & likes['uid'].isin(uid_to_idx)]\n likes_u = likes['uid'].map(uid_to_idx).to_numpy()\n likes_i = likes['item_id'].map(item_to_idx).to_numpy()\n dt_likes = np.clip(cutoff - likes['timestamp'].to_numpy(), 0, None)\n likes_w = 4.0 * np.exp(-np.log(2) * dt_likes / halflife)\n \n vpref = pd.read_parquet('/task/data/validation_preference_state.parquet')\n vpref_liked = vpref[(vpref['state'] == 'liked') & vpref['item_id'].isin(item_to_idx) & vpref['uid'].isin(uid_to_idx)]\n vpref_u = vpref_liked['uid'].map(uid_to_idx).to_numpy()\n vpref_i = vpref_liked['item_id'].map(item_to_idx).to_numpy()\n vpref_w = np.full(len(vpref_liked), 5.0, dtype=np.float32)\n \n all_u = np.concatenate([u_idx, likes_u, vpref_u])\n all_i = np.concatenate([i_idx, likes_i, vpref_i])\n all_w = np.concatenate([weight.astype(np.float32), likes_w.astype(np.float32), vpref_w])\n \n R = sp.csr_matrix((all_w, (all_u, all_i)), shape=(n_users, n_items), dtype=np.float32)\n R.data = np.log1p(R.data)\n \n df_item = np.diff(R.tocsc().indptr)\n idf = np.log((n_users - df_item + 0.5) / (df_item + 0.5) + 1.0).astype(np.float32)\n R_tfidf = (R @ sp.diags(idf)).tocsr()\n \n user_norms = np.sqrt(np.asarray(R_tfidf.power(2).sum(axis=1)).ravel()) + 1e-6\n R_user_norm = (sp.diags(1.0 / user_norms) @ R_tfidf).tocsr()\n \n U_sim = (R_user_norm @ R_user_norm.T).toarray()\n np.fill_diagonal(U_sim, 0.0)\n U_pow = np.clip(U_sim, 0, None) ** 1.5\n \n # Test scoring with R_tfidf vs R vs R_idf_half\n for beta, R_score_mat in [('idf1.0', R_tfidf), ('idf0.5', (R @ sp.diags(np.sqrt(idf))).tocsr()), ('idf0.0', R)]:\n Scores = np.zeros((n_users, n_items), dtype=np.float32)\n for c in range(0, n_items, 5000):\n Scores[:, c:c+5000] = U_pow @ R_score_mat[:, c:c+5000].toarray()\n \n val_ranks = {}\n for i, uid in enumerate(target_uids):\n row = eligibility.uid_row[uid]\n mask = np.unpackbits(eligibility.packed[row], count=len(eligibility.item_ids), bitorder='little')\n user_scores = Scores[i].copy()\n user_scores[mask == 0] = -1e9\n top100_idx = np.argpartition(user_scores, -100)[-100:]\n top100_idx = top100_idx[np.argsort(-user_scores[top100_idx])]\n val_ranks[uid] = item_ids[top100_idx].tolist()\n \n scores = score_rankings(val_ranks, graded)\n print(f'org_mult={org_mult:.1f}, beta={beta} -> NDCG@10: {scores[\\\"quality_ndcg@10\\\"]:.6f}')\n\"",
"description": "Benchmark organic multiplier and scoring matrix IDF weighting."
}
### Tool output · 2026-09-29T04:42:57.425Z
Output: org_mult=1.5, beta=idf1.0 -> NDCG@10: 0.032413
org_mult=1.5, beta=idf0.5 -> NDCG@10: 0.026770
org_mult=1.5, beta=idf0.0 -> NDCG@10: 0.021123
org_mult=2.0, beta=idf1.0 -> NDCG@10: 0.033848
org_mult=2.0, beta=idf0.5 -> NDCG@10: 0.027664
org_mult=2.0, beta=idf0.0 -> NDCG@10: 0.021314
org_mult=2.5, beta=idf1.0 -> NDCG@10: 0.034533
org_mult=2.5, beta=idf0.5 -> NDCG@10: 0.027952
org_mult=2.5, beta=idf0.0 -> NDCG@10: 0.021446
Process Group PGID: 2361
## Step 39 · agent · 2026-09-29T04:43:09.696Z
### Recorded reasoning summary
Observing Promising Results
I've noted a significant improvement in NDCG@10 to 0.034533 with `org_mult=2.5`. This configuration's effectiveness seems tied to how strictly relevance is defined by organic user engagement.
### run_shell_command · 2026-09-29T04:43:09.696Z
{
"command": "PYTHONPATH=/task python3 -c \"\nimport time\nimport numpy as np\nimport pandas as pd\nimport scipy.sparse as sp\nimport pyarrow.parquet as pq\nfrom ranking_evaluation import CandidateSet, score_rankings\nfrom evaluate_public import validation_truth\n\ntargets_df = pd.read_parquet('/task/data/target_users.parquet')\ntargets = set(targets_df.uid.astype(int))\ntarget_uids = sorted(targets)\nuid_to_idx = {uid: i for i, uid in enumerate(target_uids)}\n\ncat = pd.read_parquet('/task/data/candidate_catalog.parquet')\nitem_ids = cat['item_id'].to_numpy()\nitem_to_idx = {item: i for i, item in enumerate(item_ids)}\nn_users = len(target_uids)\nn_items = len(item_ids)\n\neligibility = CandidateSet('/task/data/validation_eligible_candidates.npz')\ngraded = validation_truth(eligibility, targets)\n\ncutoff = 24786800\nhalflife = 45 * 86400\ntable = pq.read_table('/task/data/train_listens.parquet',\n columns=['uid', 'item_id', 'timestamp', 'played_ratio_pct', 'is_organic'],\n filters=[('played_ratio_pct', '>=', 50)])\ndf_tl = table.to_pandas()\ndf_tl = df_tl[df_tl['item_id'].isin(item_to_idx) & df_tl['uid'].isin(uid_to_idx)]\n\nlikes = pd.read_parquet('/task/data/train_likes.parquet')\nlikes = likes[likes['item_id'].isin(item_to_idx) & likes['uid'].isin(uid_to_idx)]\nlikes_u = likes['uid'].map(uid_to_idx).to_numpy()\nlikes_i = likes['item_id'].map(item_to_idx).to_numpy()\n\nvpref = pd.read_parquet('/task/data/validation_preference_state.parquet')\nvpref_liked = vpref[(vpref['state'] == 'liked') & vpref['item_id'].isin(item_to_idx) & vpref['uid'].isin(uid_to_idx)]\nvpref_u = vpref_liked['uid'].map(uid_to_idx).to_numpy()\nvpref_i = vpref_liked['item_id'].map(item_to_idx).to_numpy()\n\ndt = np.clip(cutoff - df_tl['timestamp'].to_numpy(), 0, None)\ndecay = np.exp(-np.log(2) * dt / halflife)\ndt_likes = np.clip(cutoff - likes['timestamp'].to_numpy(), 0, None)\n\nfor org_mult in [2.5, 3.5, 5.0]:\n for strong_mult in [1.5, 2.0]:\n weight = decay * np.where(df_tl['is_organic'].to_numpy() == 1, org_mult, 1.0)\n weight *= np.where(df_tl['played_ratio_pct'].to_numpy() >= 80, strong_mult, 1.0)\n \n u_idx = df_tl['uid'].map(uid_to_idx).to_numpy()\n i_idx = df_tl['item_id'].map(item_to_idx).to_numpy()\n \n likes_w = 8.0 * np.exp(-np.log(2) * dt_likes / halflife)\n vpref_w = np.full(len(vpref_liked), 10.0, dtype=np.float32)\n \n all_u = np.concatenate([u_idx, likes_u, vpref_u])\n all_i = np.concatenate([i_idx, likes_i, vpref_i])\n all_w = np.concatenate([weight.astype(np.float32), likes_w.astype(np.float32), vpref_w])\n \n R = sp.csr_matrix((all_w, (all_u, all_i)), shape=(n_users, n_items), dtype=np.float32)\n R.data = np.log1p(R.data)\n \n df_item = np.diff(R.tocsc().indptr)\n idf = np.log((n_users - df_item + 0.5) / (df_item + 0.5) + 1.0).astype(np.float32)\n R_tfidf = (R @ sp.diags(idf)).tocsr()\n \n user_norms = np.sqrt(np.asarray(R_tfidf.power(2).sum(axis=1)).ravel()) + 1e-6\n R_user_norm = (sp.diags(1.0 / user_norms) @ R_tfidf).tocsr()\n \n U_sim = (R_user_norm @ R_user_norm.T).toarray()\n np.fill_diagonal(U_sim, 0.0)\n U_pow = np.clip(U_sim, 0, None) ** 1.5\n \n Scores = np.zeros((n_users, n_items), dtype=np.float32)\n for c in range(0, n_items, 5000):\n Scores[:, c:c+5000] = U_pow @ R_tfidf[:, c:c+5000].toarray()\n \n val_ranks = {}\n for i, uid in enumerate(target_uids):\n row = eligibility.uid_row[uid]\n mask = np.unpackbits(eligibility.packed[row], count=len(eligibility.item_ids), bitorder='little')\n user_scores = Scores[i].copy()\n user_scores[mask == 0] = -1e9\n top100_idx = np.argpartition(user_scores, -100)[-100:]\n top100_idx = top100_idx[np.argsort(-user_scores[top100_idx])]\n val_ranks[uid] = item_ids[top100_idx].tolist()\n \n scores = score_rankings(val_ranks, graded)\n print(f'org_mult={org_mult:.1f}, strong_mult={strong_mult:.1f} -> NDCG@10: {scores[\\\"quality_ndcg@10\\\"]:.6f}')\n\"",
"description": "Test stronger organic and strong listen multipliers."
}
### Tool output · 2026-09-29T04:46:20.310Z
Output: org_mult=2.5, strong_mult=1.5 -> NDCG@10: 0.034033
org_mult=2.5, strong_mult=2.0 -> NDCG@10: 0.033668
org_mult=3.5, strong_mult=1.5 -> NDCG@10: 0.035254
org_mult=3.5, strong_mult=2.0 -> NDCG@10: 0.034271
org_mult=5.0, strong_mult=1.5 -> NDCG@10: 0.036124
org_mult=5.0, strong_mult=2.0 -> NDCG@10: 0.035221
Process Group PGID: 2408
## Step 40 · agent · 2026-09-29T04:46:36.811Z
### Recorded reasoning summary
Exploring Organic Weighting
I'm currently investigating the impact of adjusting the multiplier for organic listens, considering values like 6.0 and 8.0, and even exploring a scenario where only organic listens are used for user similarity calculations. The initial results showing an NDCG@10 of 0.036124 are prompting deeper exploration into this parameter.
### run_shell_command · 2026-09-29T04:46:36.811Z
{
"command": "PYTHONPATH=/task python3 -c \"\nimport time\nimport numpy as np\nimport pandas as pd\nimport scipy.sparse as sp\nimport pyarrow.parquet as pq\nfrom ranking_evaluation import CandidateSet, score_rankings\nfrom evaluate_public import validation_truth\n\ntargets_df = pd.read_parquet('/task/data/target_users.parquet')\ntargets = set(targets_df.uid.astype(int))\ntarget_uids = sorted(targets)\nuid_to_idx = {uid: i for i, uid in enumerate(target_uids)}\n\ncat = pd.read_parquet('/task/data/candidate_catalog.parquet')\nitem_ids = cat['item_id'].to_numpy()\nitem_to_idx = {item: i for i, item in enumerate(item_ids)}\nn_users = len(target_uids)\nn_items = len(item_ids)\n\neligibility = CandidateSet('/task/data/validation_eligible_candidates.npz')\ngraded = validation_truth(eligibility, targets)\n\ncutoff = 24786800\nhalflife = 45 * 86400\ntable = pq.read_table('/task/data/train_listens.parquet',\n columns=['uid', 'item_id', 'timestamp', 'played_ratio_pct', 'is_organic'],\n filters=[('played_ratio_pct', '>=', 50)])\ndf_tl = table.to_pandas()\ndf_tl = df_tl[df_tl['item_id'].isin(item_to_idx) & df_tl['uid'].isin(uid_to_idx)]\n\nlikes = pd.read_parquet('/task/data/train_likes.parquet')\nlikes = likes[likes['item_id'].isin(item_to_idx) & likes['uid'].isin(uid_to_idx)]\nlikes_u = likes['uid'].map(uid_to_idx).to_numpy()\nlikes_i = likes['item_id'].map(item_to_idx).to_numpy()\n\nvpref = pd.read_parquet('/task/data/validation_preference_state.parquet')\nvpref_liked = vpref[(vpref['state'] == 'liked') & vpref['item_id'].isin(item_to_idx) & vpref['uid'].isin(uid_to_idx)]\nvpref_u = vpref_liked['uid'].map(uid_to_idx).to_numpy()\nvpref_i = vpref_liked['item_id'].map(item_to_idx).to_numpy()\n\ndt = np.clip(cutoff - df_tl['timestamp'].to_numpy(), 0, None)\ndecay = np.exp(-np.log(2) * dt / halflife)\ndt_likes = np.clip(cutoff - likes['timestamp'].to_numpy(), 0, None)\n\nfor org_mult in [6.0, 8.0, 12.0, 20.0]:\n weight = decay * np.where(df_tl['is_organic'].to_numpy() == 1, org_mult, 1.0)\n weight *= np.where(df_tl['played_ratio_pct'].to_numpy() >= 80, 1.5, 1.0)\n \n u_idx = df_tl['uid'].map(uid_to_idx).to_numpy()\n i_idx = df_tl['item_id'].map(item_to_idx).to_numpy()\n \n likes_w = 10.0 * np.exp(-np.log(2) * dt_likes / halflife)\n vpref_w = np.full(len(vpref_liked), 12.0, dtype=np.float32)\n \n all_u = np.concatenate([u_idx, likes_u, vpref_u])\n all_i = np.concatenate([i_idx, likes_i, vpref_i])\n all_w = np.concatenate([weight.astype(np.float32), likes_w.astype(np.float32), vpref_w])\n \n R = sp.csr_matrix((all_w, (all_u, all_i)), shape=(n_users, n_items), dtype=np.float32)\n R.data = np.log1p(R.data)\n \n df_item = np.diff(R.tocsc().indptr)\n idf = np.log((n_users - df_item + 0.5) / (df_item + 0.5) + 1.0).astype(np.float32)\n R_tfidf = (R @ sp.diags(idf)).tocsr()\n \n user_norms = np.sqrt(np.asarray(R_tfidf.power(2).sum(axis=1)).ravel()) + 1e-6\n R_user_norm = (sp.diags(1.0 / user_norms) @ R_tfidf).tocsr()\n \n U_sim = (R_user_norm @ R_user_norm.T).toarray()\n np.fill_diagonal(U_sim, 0.0)\n U_pow = np.clip(U_sim, 0, None) ** 1.5\n \n Scores = np.zeros((n_users, n_items), dtype=np.float32)\n for c in range(0, n_items, 5000):\n Scores[:, c:c+5000] = U_pow @ R_tfidf[:, c:c+5000].toarray()\n \n val_ranks = {}\n for i, uid in enumerate(target_uids):\n row = eligibility.uid_row[uid]\n mask = np.unpackbits(eligibility.packed[row], count=len(eligibility.item_ids), bitorder='little')\n user_scores = Scores[i].copy()\n user_scores[mask == 0] = -1e9\n top100_idx = np.argpartition(user_scores, -100)[-100:]\n top100_idx = top100_idx[np.argsort(-user_scores[top100_idx])]\n val_ranks[uid] = item_ids[top100_idx].tolist()\n \n scores = score_rankings(val_ranks, graded)\n print(f'org_mult={org_mult:.1f} -> NDCG@10: {scores[\\\"quality_ndcg@10\\\"]:.6f}')\n\"",
"description": "Test higher organic multipliers."
}
### Tool output · 2026-09-29T04:48:46.219Z
Output: org_mult=6.0 -> NDCG@10: 0.036063
org_mult=8.0 -> NDCG@10: 0.036132
org_mult=12.0 -> NDCG@10: 0.036065
org_mult=20.0 -> NDCG@10: 0.035436
Process Group PGID: 2455
## Step 41 · agent · 2026-09-29T04:49:02.475Z
### Recorded reasoning summary
Exploring Negative Signals
I'm currently investigating negative user feedback, specifically dislikes and unlikes, and how they might influence candidate item selection. My focus is on understanding their impact, especially when similar users have expressed negative sentiment.
## Recorded session usage
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