This repository provides scripts for exploring a genome and receiving personalized explanations through natural dialogue.
Surprise
Use Surprise, a Python library, to build and test recommender systems.

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
- 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
- Does not support implicit rating data
- Does not support content-based recommendation information
Surprise at a glance
- Website
- surpriselib.com
- Category
- Research & Data › Research & Study Tools
- 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?
What built-in datasets are available?
What evaluation tools are included?
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