Creating a Collaborative Filtering Recommendation System with Surprise

Learn How to Build a Personalized Recommendation System Using Python’s Surprise Library

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In the world of e-commerce, streaming services, and social media platforms, recommendation systems play a crucial role in enhancing user experience by providing personalized suggestions.

Collaborative filtering is a popular technique used to build such systems, leveraging user-item interactions to make recommendations.

In this article, we will explore how to create a collaborative filtering recommendation system using the Surprise library in Python.

What is Collaborative Filtering?

Collaborative filtering is a method commonly used in recommendation systems that makes automatic predictions about the interests of a user by collecting preferences from many users. There are two main types of collaborative filtering: user-based and item-based.

User-based collaborative filtering recommends items by finding similar users based on their preferences and recommending items that these similar users have liked. On the other hand, item-based collaborative filtering recommends items based on their similarity to items that the user has already liked.