NicolasHug/Surprise

Health v2 · maintenance only

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6808 stars · 1051 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-29, confidence not recorded

Surprise is a Python scikit for building and analyzing recommender systems that work with explicit rating data. It provides ready-to-use prediction algorithms (SVD, SVD++, NMF, neighborhood methods), built-in datasets, similarity measures, and cross-validation tools inspired by scikit-learn.

Use cases

• build a movie recommendation engine with collaborative filtering
• run cross-validation on recommender algorithms with RMSE and MAE metrics
• train an SVD matrix factorization model on rating data
• benchmark and compare different recommendation algorithms
• prototype a custom rating prediction algorithm
• load and preprocess Movielens or custom explicit rating datasets

When to choose

• you work with explicit rating data and want well-documented, reproducible experiments
• you need classic collaborative filtering algorithms like SVD, KNN, or NMF out of the box
• you want scikit-learn-style cross-validation and grid search for recommenders

When to avoid

• your data is implicit feedback (clicks, views) rather than explicit ratings
• you need content-based or hybrid recommendation with item metadata
• you need a production-scale distributed recommender system

Facets

library · maturity stable

machine-learning data-science benchmarking machine-learning data-science python cross-platform recommender-systems collaborative-filtering matrix-factorization svd explicit-ratings scikit algorithms

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