NicolasHug/Surprise
A Python scikit for building and analyzing recommender systems
This tool helps you build and analyze recommender systems using explicit rating data. You provide a dataset of user ratings (like movie ratings or product reviews), and it helps predict how much a user would rate an item they haven't seen yet. This is ideal for product managers, e-commerce specialists, or content curators who want to personalize user experiences.
6,770 stars. No commits in the last 6 months.
Use this if you need to create, test, and compare different recommendation algorithms to suggest items based on past user ratings.
Not ideal if your recommendation needs rely on implicit feedback (like clicks or purchases without explicit ratings) or content-based information.
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6,770
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Language
Python
License
BSD-3-Clause
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Last pushed
Jul 24, 2025
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