 Recommender system

A recommender system selects or ranks items for a particular user or context, such as choosing books from a large catalogue. Its prediction target may be a rating, a click or a purchase; these targets need not coincide with what a user ultimately values. A common architecture first retrieves a manageable candidate set, then scores and reranks it.

Content-based filtering uses similarities between items; collaborative filtering uses patterns of interactions between users and items. If two readers overlap strongly in their previous choices, a book enjoyed by one can become a candidate for the other even without matching its description. Learned vectors make this retrieval a nearest neighbor search or related similarity search. A dot product also depends on vector length, so normalizing the vectors can change rankings.^[ref]