Surprise

A Python scikit for building and analyzing recommender systems

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Builder

NicolasHug

NicolasHug • individual

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6,780

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Forks

1,051

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0/100

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Created

Oct 23, 2016

Project creation date

README Summary

Surprise is a Python scikit-learn-inspired library for building and analyzing recommender systems that deal with explicit rating data. It provides a set of ready-to-use prediction algorithms and tools to evaluate, analyze and compare the performance of algorithms. The library is designed to be simple and efficient, following scikit-learn conventions for easy integration into existing workflows.

AI Dev Skills

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Tags

Collaborative FilteringMatrix FactorizationNeighborhood-based MethodsRecommendation SystemsCross-validation for Recommender SystemsEvaluation Metrics for RecommendationsBaseline Prediction AlgorithmsUser-Item Rating PredictionSimilarity ComputationModel Selection and Hyperparameter TuningContent RecommendationClassical Machine Learning RevivalCollaborative Filtering ResearchProduct RecommendationSocial MediaVideo StreamingMovie Rating PredictionTabularMedia & EntertainmentContent PlatformsRating PredictionPersonalization at ScalePublishingRecommendation Algorithm BenchmarkingE-commerceMusic StreamingOn-premiseUser Preference ModelingPersonalized Content FilteringSelf-hostedCloud APIPython

Recent Activity

Updated 8 months ago

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Quality

production

Quality: high
Maturity: production

PM Skills

Developer Platform

Languages

Python100.0%

Timeline

Project created: Oct 23, 2016
Forked: Mar 22, 2026
Your last push: 8 months ago
Upstream last push: 8 months ago
Tracked since: Jul 24, 2025

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