Collaborative Filtering

Collaborative filtering analyzes interactions such as ratings, purchases, clicks, or viewing history to identify similarities between users or items. User-based filtering recommends items preferred by similar users, while item-based filtering recommends items similar to those a user previously liked. Unlike content-based methods, it does not necessarily require detailed information about the characteristics of each item.

Collaborative filtering helps platforms deliver personalized recommendations without manually defining what each user may prefer. As more interaction data becomes available, recommendation quality can improve, helping users discover relevant products, content, or services and increasing engagement.