scikit-rec 0.1.5

A scikit-style recommender systems library

scikit-rec

A composable, scikit-style recommender systems library.

scikit-rec provides a 3-layer architecture that cleanly separates business logic, scoring strategy, and ML models. Any recommender works with any compatible scorer and estimator, giving you a mix-and-match toolkit for building recommendation systems.

Recommender (business logic)  -->  Scorer (item scoring)  -->  Estimator (ML model)

Why scikit-rec?

Composable by design. Each layer is independently extensible. Swap XGBoost for a Two-Tower model without changing your recommender. Add a new bandit strategy without touching the scorer. The library spans XGBoost, LightGBM, and scikit-learn alongside deep learning models (NCF, Two-Tower, DeepFM, SASRec, HRNN), with GPU optional — a pure-NumPy matrix factorization (ALS/SGD) requires no PyTorch. The composable architecture also accommodates novel research: a Goal-Conditioned Supervised Learning (GCSL) recommender for multi-objective recommendation was implemented as a single Recommender subclass — no new scorer or estimator required. Contributions welcome: implement one abstract class and it works with everything else.

Beyond ranking. Contextual bandits (epsilon-greedy, static-action) and heterogeneous treatment effect estimation (T/S/X-Learner) are first-class paradigms, not afterthoughts. All share the same evaluation infrastructure, so you can directly compare a ranking policy against a bandit or uplift policy on the same logged data.

Production-grade evaluation. The most complete offline policy evaluation suite in any recommendation library: IPS, Doubly Robust, SNIPS, Direct Method, Policy-Weighted, and Replay Match, paired with eight ranking and classification metrics (Precision, Recall, MAP, MRR, NDCG, ROC-AUC, PR-AUC, Expected Reward) — enabling counterfactual policy comparison from logged data with a single call.

Production readiness. Config-driven pipeline factory with Optuna HPO, low-latency single-user inference (recommend_online), two-stage retrieval-then-ranking, and batch training.

Learn by example. Ten end-to-end Jupyter notebooks on MovieLens 1M cover ranking, bandits, uplift, sequential recommendations, multi-objective optimization, hyperparameter tuning, two-stage retrieval, and contextual two-tower models. Our SASRec achieves HR@10 = 0.8953 and NDCG@10 = 0.6331 on MovieLens-1M (leave-last-out, 1 positive + 100 negatives). Each notebook downloads data, trains, evaluates, and shows sample recommendations — ready to run.

Installation

pip install scikit-rec

Optional extras:

pip install scikit-rec[torch]    # Deep learning models (DeepFM, NCF, SASRec, HRNN, Two-Tower)
pip install scikit-rec[aws]      # S3 data loading

Quick Start

from skrec.estimator.classification.xgb_classifier import XGBClassifierEstimator
from skrec.scorer.universal import UniversalScorer
from skrec.recommender.ranking.ranking_recommender import RankingRecommender
from skrec.examples.datasets import (
    sample_binary_reward_interactions,
    sample_binary_reward_users,
    sample_binary_reward_items,
)

# Build the pipeline: Estimator -> Scorer -> Recommender
estimator = XGBClassifierEstimator({"learning_rate": 0.1, "max_depth": 5})
scorer = UniversalScorer(estimator)
recommender = RankingRecommender(scorer)

# Train
recommender.train(
    interactions_ds=sample_binary_reward_interactions,
    users_ds=sample_binary_reward_users,
    items_ds=sample_binary_reward_items,
)

# Recommend
interactions_df = sample_binary_reward_interactions.fetch_data()
users_df = sample_binary_reward_users.fetch_data()
recommendations = recommender.recommend(interactions=interactions_df, users=users_df, top_k=5)

Components

Recommenders

Recommender	Description
RankingRecommender	Rank items by predicted score
ContextualBanditsRecommender	Exploration-exploitation strategies (epsilon-greedy, static action)
UpliftRecommender	Uplift modeling (S-Learner, T-Learner, X-Learner)
SequentialRecommender	Sequence-aware recommendations
HierarchicalSequentialRecommender	Session-aware hierarchical sequences (HRNN)
GcslRecommender	Multi-objective goal-conditioned supervised learning

Scorers

Scorer	Description
UniversalScorer	Single global model using item features (auto-dispatches tabular vs. embedding)
IndependentScorer	Separate model per item
MulticlassScorer	Items as competing classes
MultioutputScorer	Multiple outcomes per prediction
SequentialScorer	For sequential estimators (SASRec)
HierarchicalScorer	For HRNN estimators

Estimators

Type	Models
Tabular	XGBoost, LightGBM, Logistic Regression, sklearn classifiers/regressors
Embedding	Matrix Factorization, NCF, Two-Tower, DCN, DeepFM
Sequential	SASRec, HRNN

Evaluators

Evaluator	Description
SimpleEvaluator	Standard offline evaluation on held-out data
IPSEvaluator	Inverse Propensity Scoring for counterfactual evaluation
DREvaluator	Doubly Robust — combines direct estimation with IPS
SNIPSEvaluator	Self-Normalized IPS — reduces variance of IPS
DirectMethodEvaluator	Uses a reward model to estimate policy value
PolicyWeightedEvaluator	Weights logged rewards by policy/logging probability ratio
ReplayMatchEvaluator	Unbiased evaluation using only matching logged actions

Metrics

Precision@k, Recall@k, MAP, MRR, NDCG, ROC-AUC, PR-AUC, Expected Reward.

Retrievers

Two-stage retrieval: Popularity, Content-Based, Embedding-Based.

Example Notebooks

Notebook	What it demonstrates
Ranking with XGBoost	Feature-based ranking with demographics and genre features
Uplift Modeling	S-Learner, T-Learner, X-Learner treatment effect estimation
GCSL Multi-Objective	Goal-conditioned recommendations — steer quality vs. novelty
HPO with Optuna	Hyperparameter tuning with TPE, GP, and CMA-ES samplers
Two-Stage Retrieval	Popularity, content-based, and embedding retrieval + ranking
Two-Tower Models	Three context modes: user_tower, trilinear, scoring_layer
SASRec (Positives)	Self-attentive sequential recommendation on positive interactions
SASRec (Ratings)	SASRec with explicit ratings as soft labels
SASRec (MSE)	SASRec regressor with MSE loss
HRNN	Hierarchical RNN for session-aware recommendations

All notebooks use MovieLens 1M (downloaded automatically) and include training, evaluation, and sample recommendations.

Documentation

Full documentation is available at intuit.github.io/scikit-rec.

• Architecture overview
• Capability matrix
• Quick start tutorial
• Dataset preparation
• Evaluation guide

Development

git clone https://github.com/intuit/scikit-rec.git
cd scikit-rec
python -m venv .venv && source .venv/bin/activate
pip install -e ".[dev]"
pytest tests/

License

Apache 2.0