Build a High-Performing Movie Recommender System using Collaborative Filtering in Python
The digital age presents us with an unmanageable number of decisions and even more options. Which series to watch today? … Read more
Surprise Lib
The surprise Python library is a machine learning library for building and analyzing recommender systems. It is built on top of the scikit-learn library, and it provides a range of algorithms and tools for creating and evaluating recommender systems.
Recommender systems are algorithms that are used to make personalized recommendations to users based on their interests and preferences. They are commonly used by online retailers, streaming services, and other companies that need to provide personalized recommendations to their users.
The surprise library provides a range of algorithms for building recommender systems, including collaborative filtering, matrix factorization, and SVD++. It also provides tools for evaluating the performance of these algorithms, such as cross-validation and grid search.
Overall, the surprise library is a useful tool for anyone who is interested in building and analyzing recommender systems. It is built on top of the popular scikit-learn library, and it provides a range of algorithms and tools that can help you create and evaluate effective recommender systems.