Building Recommendation Systems with Collaborative Filtering

Learn how to build a recommendation system using collaborative filtering with Python

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Recommendation systems are a crucial component of many online platforms, from e-commerce websites to streaming services. They use algorithms to suggest items to users based on their preferences and behaviors. Collaborative Filtering is one of the most widely used techniques for building recommendation systems.

Let’s look at how to build a recommendation system using collaborative filtering with Python.

Collaborative filtering is a method of making recommendations based on the preferences of similar users. The idea is that if two users have similar preferences, they are likely to enjoy the same items.

Collaborative filtering can be divided into two main categories: user-user collaborative filtering and item-item collaborative filtering.

User-user collaborative filtering

In user-user collaborative filtering, we find the most similar users to a target user and recommend items that they like. To do this, we first need to create a user-item matrix, where each row represents a user and each column represents an item. The entries in the matrix are the ratings…