Surprise

Use Surprise, a Python library, to build and test recommender systems.

surpriselib.com
Surprise homepage screenshot

About Surprise

Surprise is a Python scikit for building and testing recommender systems that use explicit rating data. It offers built-in datasets including Movielens and Jester, support for custom datasets, a variety of ready-to-use prediction algorithms (such as SVD, SVD++, NMF, and neighborhood methods), built-in similarity measures, and tools for evaluating, analyzing, and comparing algorithm performance, including cross-validation and parameter search functionality. The library does not support implicit ratings or content-based recommendation features.

Key features

  • Built-in datasets (Movielens, Jester) and custom dataset support
  • Ready-to-use prediction algorithms (SVD, SVD++, NMF, k-NN)
  • Built-in similarity measures (cosine, MSD, Pearson)
  • Cross-validation and algorithm performance evaluation tools
  • Parameter search (e.g. GridSearchCV) functionality
  • Easy implementation of new algorithm ideas
Best for: It is intended for Python developers and researchers building and testing recommender systems that rely on explicit rating data.
Good for
  • Includes detailed documentation outlining all algorithm implementation details
  • Reduces dataset handling work with built-in and custom dataset support
  • Provides built-in tools for cross-validation and algorithm comparison
Watch out for
  • Does not support implicit rating data
  • Does not support content-based recommendation information

Surprise at a glance

Type
Open-source project
Pricing
Not confirmed. Check the site
Platforms
Web
Open source
Not stated
Language
English
Last checked
Oct 5, 2026 · Online when checked
Listed
Oct 5, 2026

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Surprise FAQ

What types of rating data does Surprise support?
It only supports explicit rating data, not implicit ratings or content-based information.
What built-in datasets are available?
It includes Movielens and Jester built-in datasets, plus support for custom datasets.
What evaluation tools are included?
It offers cross-validation, performance metrics (RMSE, MAE), and parameter search tools.
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