How Does Collaborative Filtering Work in Recommender Systems?

Big companies like Google, Amazon, Netflix, Meta, etc., maintain massive user behavior databases, gathering information every day. They use their state-of-the-art recommendation systems to display and recommend the most relevant products and content to users, thereby increasing engagement and retaining more customers.

Scalability is an issue with collaborative filtering. As time goes by and the user base and data collected increase, the algorithms start to suffer because of the sheer volume of processing power required.

Collaborative filtering uses unsupervised learning. In model-based collaborative filtering, we don't know the latent features yet and use unsupervised learning models to predict the ratings.

Collaborative filtering can be used when there’s already an abundant amount of past user-item interaction data available.

Collaborative filtering enhances the user experience by recommending popular items among similar users, enabling them to keep up with trends. This collaborative approach can often lead to serendipitous suggestions that keep users more engaged and involved.

There are two types of collaborative approaches: memory-based collaborative approach and model-based collaborative approach.