How the Power of Similarity Drives Smart Recommendations: A Deep Dive into Collaborative Filtering

Curious how Netflix and Amazon recommend the perfect content?

It’s all thanks to Collaborative Filtering.

By focusing on its underlying mathematics, we will walk through the basic principles, key steps, and formulas that make collaborative filtering a powerful tool for personalized recommendations.

Let’s dive into the math behind it and see how it powers personalized recommendations.

What is Collaborative Filtering (CF)?

It is like getting recommendations from friends - suggesting what you’ll like based on others’ preferences.

The core idea is to recommend items based on the preferences of similar users. CF is based on the assumption that if users agree on one issue, they are likely to agree on others as well.

There are two main types of collaborative filtering:

1. User-based collaborative filtering: Recommends items by finding similar users.
2. Item-based collaborative filtering: Recommends items that are similar to ones the user has liked or interacted with in the past.

Let’s dive into Item-based collaborative filtering, which is more widely used in practice due to its scalability.

Step 1: User-Item Matrix Representation