Also known as: Recommendation Systems
A recommendation system selects a small set of likely relevant or desirable items for a user from a much larger catalog. It may use user behavior, item attributes, collaborative filtering, ranking models, or business rules to recommend content, products, or actions.
A system that selects for each user a relatively small set of desirable items from a large corpus. For example, a video recommendation system might recommend two videos from a corpus of 100,000 videos, selecting Casablanca and The Philadelphia Story for one user, and Wonder Woman and Black Panther for another. A video recommendation system might base its recommendations on factors such as: - Movies that similar users have rated or watched. - Genre, directors, actors, target demographic... See the Recommendation Systems course for more information.
A recommendation system is a machine learning-based system that helps users find compelling content in a large corpus. It generates a smaller subset of candidates from a potentially huge corpus, scores and ranks the candidates, and re-ranks the final ranking to take into account additional constraints. For more information, see Recommendation systems overview.