Recommender System

A recommender system is a type of information filtering technology that suggests items most relevant to a particular user, used when a person must choose among a large number of options such as products, media, or online content. Two main approaches are typically combined: collaborative filtering, which assumes that people who agreed on past choices tend to agree on future ones and recommends items liked by similar users, and content-based filtering, which builds a profile of a user's preferences from item descriptions and recommends items with similar characteristics. The first working recommender system, Grundy, was created by Elaine Rich in 1979, asking readers questions to sort them into preference stereotypes for book recommendations; systematic collaborative filtering research followed from MIT and other groups in the 1990s. Modern platforms, including streaming and e-commerce services, typically combine both approaches into a hybrid system, increasingly powered by machine learning or deep learning models. This description is adapted from Wikipedia contributors under CC BY-SA 4.0; changes were made. https://creativecommons.org/licenses/by-sa/4.0/