Content-Based Filtering
TF-IDF item representations, cosine similarity, building a movie recommender from metadata.
What Is Content-Based Filtering?
Content-based filtering recommends items similar to ones a user already liked, based on the items own features (text, genre, tags). Unlike collaborative filtering, it needs no other users data, so it sidesteps the item cold-start problem.
Items as Feature Vectors
The key idea: turn each item into a numeric vector describing its content, then measure similarity between vectors. For text descriptions, TF-IDF is the classic way to build those vectors.