Surprise

At a glance

• Editor score
  5.9 / 10
• Pricing
  Free plan
• Best for
  Researchers testing collaborative-filtering algorithms
• Free plan
  Yes
• Paid from
  Free
• Deployment
  Self_hosted
• Facts checked
  24 Sep 2026

• Where it wins

  • Includes baseline, neighborhood, and matrix-factorization algorithms
  • Supports cross-validation, evaluation metrics, and grid search
  • Works with built-in MovieLens and Jester datasets or custom datasets

• Where it doesn't

  • Works with explicit user ratings, not implicit ratings
  • Does not use content-based information
  • Runs as a local library rather than a hosted recommendation service

Our verdict on Surprise

Surprise is an open-source Python library for developers and researchers who want to build and compare recommender-system models using explicit user ratings. Its scope covers rating prediction and top-N item ranking, with collaborative-filtering approaches that include baseline predictions, neighborhood methods, and matrix factorization. It is a fit for experimentation on Windows, macOS, or Linux, rather than for teams seeking a hosted service that delivers recommendations to an application.

The toolkit is centered on model work: built-in cosine, MSD, and Pearson similarity measures support neighborhood approaches, while matrix-factorization options include SVD, PMF, SVD++, and NMF. Users can load MovieLens and Jester datasets or supply custom datasets, then use cross-validation and evaluation metrics to compare results. Grid search helps select algorithm parameters. These capabilities make Surprise useful for researchers testing methods and for developers prototyping ranking or rating-prediction logic; they do not turn it into a complete recommendation delivery platform.

Surprise is free and open source, and it is self-hosted as a locally installed Python library. The published project scope is explicit-rating recommendation: it does not support implicit ratings or content-based information, and real-time updates are not supported. That limits its fit for systems whose recommendations depend on behavioral events, item attributes, or continuously refreshed data. Support is through documentation and the community, and feature development is limited to maintenance, bug fixes, and possible performance improvements. Pick Surprise for offline collaborative-filtering experiments and model evaluation; choose another approach if you need a hosted service, real-time recommendations, or content-aware and implicit-feedback methods.

Your product? Our editors can test Surprise in depth and publish a review, labelled as paid · from $149/mo →

Surprise pricing

Surprise fact sheet

Alternatives to Surprise

• Amazon PersonalizeBuild real-time or batch recommendations with usage-based pricing and AWS integrations.9.2
• RecombeeFlexible recommendations and personalized search across digital channels, with a free plan.8.8
• AlgoliaA broad search and discovery platform for teams adding recommendations to commerce experiences.8.6

See all Surprise alternatives →

Featured on iTechGuides

Surprise is listed in our Recommender Systems directory. Add the badge to your site — it links back to this page.

<a href="https://www.itechguides.com/products/surprise/"><img src="https://www.itechguides.com/best/badge/surprise.svg" alt="Featured on iTechGuides" width="230" height="46"></a>

Reviewed by iTechGuides Editors · Editorial team · Updated Oct 2026

Advertiser disclosure: iTechGuides is reader-supported. Vendors can pay for top positions in our rankings and for a place on other products' pages, and we may earn a commission when you click some links. Bought places stand at the number bought; this page and our disclosure say so. Paying vendors may get a fuller, more detailed review from our editors, and that review can change their score. How we rank.

Last updated 7 Oct 2026 · How we research and update