Collaborative filtering recommends content by finding patterns across users: if many viewers with tastes similar to yours watched and liked a title, it is likely a good recommendation for you, even without knowing anything about the content itself. It powered the classic wave of recommenders and remains a core technique.
Its strength is that it captures subtle, hard-to-describe taste patterns directly from behavior. Its main weakness is the cold-start problem: new users and new titles have little interaction data, so they are hard to place. Production systems combine collaborative filtering with content-based methods and metadata to cover the gaps and keep recommendations fresh.
