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warm-transfer

Model-agnostic plug&play library for transferring and calibrating the scores of an already-trained recommendation model onto new (cold-start) items under extreme sparsity, plus a reproducible benchmark.

Idea: you have a trained model of arbitrary architecture — the library "wraps" on top of its scores and ranks new products/content for which there are still no (or almost no) interactions. You don't need to retrain the model, and you don't need access to its internals either.

Structure

• warmtransfer — lightweight core (plug&play). Works with donor scores + content. Installs without heavy recsys dependencies.
  • methods/ — cold-start methods (baselines, KNN, LinMap, Stacking, scale&shift, attention-KNN…)
  • metrics/ — our own correct metrics (Recall/Precision/MAP/NDCG@k, MRR, AUC, RelaImpr)
  • similarity/ — content similarity cold→warm (optional)
• warmtransfer.bench — benchmark (heavy dependencies, extra bench).
  • datasets/ — loaders (ML-1M/20M, Goodbooks, KION/KION-text, Amazon Toys/-text, MIND/-text)
  • splitters/ — honest pseudo-cold split (anti-leakage)
  • adapters/ — donor models (ALS, BPR, CatBoost, EASE, Two-Tower)
  • runner.py — running the matrix of datasets × donors × methods × baselines

Key result

The model-agnostic methods LinMap (Ridge: content → donor score vector) and stacking_plus (hybrid: linmap signal + personalized popularity) beat the strong personalized Grouped MP across a matrix of 8 dataset loaders × 5 donors (seed=42): score transfer beats the baseline on per-user AUC in 36 of 40 dataset×donor cells (90%). The 4 misses are mostly on ML-1M, where the baseline AUC (~0.72) is already strong. The five donors span four model families (matrix factorization als/bpr, GBDT catboost, linear item-item ease, neural two_tower) — they are a diversity axis for observing how transfer holds across donor types, not competitors to rank. Naive methods (knn/attention/debiased/embedding_avg) lose to the baseline — they pull in neighbor popularity. Caveat: these numbers are a single seed; targeted multi-seed runs on the marginal cells are still pending. Details and tables — docs/results/full_matrix.md.

Plug&play usage (core, without the benchmark)

import numpy as np
import pandas as pd

from warmtransfer.columns import Columns as C
from warmtransfer.methods import LinMap
from warmtransfer.types import ItemFeatures, TransferInputs

warm_features = ItemFeatures(
    item_ids=np.array([10, 11]),
    matrix=np.array([[1.0, 0.0], [0.0, 1.0]]),
    feature_names=["genre_action", "genre_drama"],
)
cold_features = ItemFeatures(
    item_ids=np.array([20]),
    matrix=np.array([[1.0, 0.0]]),
    feature_names=["genre_action", "genre_drama"],
)
donor_scores = pd.DataFrame(
    {
        C.User: [1, 1, 2, 2],
        C.Item: [10, 11, 10, 11],
        C.Score: [5.0, 1.0, 1.0, 5.0],
    }
)

# bring your own warm donor scores + content of warm/cold items
inputs = TransferInputs(
    donor_scores=donor_scores,
    warm_features=warm_features,
    cold_features=cold_features,
)
reco = LinMap(alpha=1.0).fit(inputs, seed=42).predict(
    user_ids=np.array([1, 2]),
    cold_item_ids=np.array([20]),
)

Full runnable example: examples/quickstart.py.

Installation

uv sync                 # core + dev only
uv sync --extra bench   # + donor engines and benchmark infrastructure
uv sync --extra all     # + deep (torch)

Running

uv run pytest -q                          # core tests
uv run python examples/quickstart.py      # minimal plug&play example
uv run warmbench --list-components        # available datasets/donors/methods
uv run warmbench --config configs/example.yaml --dry-run
uv run warmbench --config configs/example.yaml  # example benchmark run

Documentation

The documentation site is built with MkDocs Material (like RecTools) and published to GitHub Pages automatically (.github/workflows/docs.yml) after a push to the GitHub repository. Locally:

uv sync --group docs
uv run mkdocs serve     # local preview at http://127.0.0.1:8000
uv run mkdocs build     # static site in site/

Page sources:

• docs/methods.md — description of the methods
• docs/datasets.md — description of the datasets
• docs/eval-protocol.md — split and metrics protocol (anti-leakage)
• docs/results/ — result tables (full matrix, spread across seeds, ablations)
• docs/api.md — auto-generated API reference