Exploring Recommendation System Algorithms: Collaborative and Content-Based Filtering

Photo by Muhammad Rizki on Unsplash

Recommendation systems are integral to digital platforms. They help offer personalized experiences to users.

At the core of these systems are two primary families of algorithms: Collaborative Filtering and Content-Based Filtering. Let’s dive into each.

Collaborative Filtering

Collaborative Filtering (CF) operates on the principle that users who have shown similar behaviors in the past will have similar preferences in the future. For instance, if two users like the same set of movies, it’s likely they will enjoy similar content going forward.

CF algorithms work by analyzing patterns in user interactions, such as clicks, purchases, or ratings.

The goal is to predict missing entries in a user-item interaction matrix, known as the R matrix. Various techniques are employed in this process:

1. Memory-Based Methods: These create two matrices — one for user-item affinity and another for item similarity. The multiplication of these matrices helps predict a user’s preference for items they haven’t interacted with.
2. Matrix Factorization: This technique reconstructs the sparse R…