A Guide to Building a Recommendation System with Collaborative Filtering

Demystifying Collaborative Filtering: Simple Steps to Enhance User Engagement

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In today’s digital age, where content overload is the norm, businesses strive to provide users with personalized experiences. One powerful tool in achieving this is building a recommendation system, and one of the most effective approaches is collaborative filtering.

In this article, we’ll break down the concept and guide you through the process of creating your own recommendation system with practical code examples.

Understanding Collaborative Filtering

Collaborative filtering is a recommendation system technique that predicts a user’s interests by collecting preferences from many users (collaborating). There are two main types: user-based and item-based.

User-based Collaborative Filtering: This method recommends items to a user based on the preferences of users with similar tastes. If user A likes items X, Y, and Z, and user B has a similar taste to A, then the system will recommend items liked by user B but not yet seen by user A.

Item-based Collaborative Filtering: Instead of focusing on user preferences, this approach recommends items…