A recommender system (RecSys), or a recommendation system (sometimes replacing system with terms such as platform, engine, or algorithm) and sometimes only called "the algorithm" or "algorithm", is a subclass of information filtering system that provides suggestions for items that are most pertinent to a particular user. Recommender systems are particularly useful when an individual needs to choose an item from a potentially overwhelming number of items that a service may offer. Modern recommendation systems such as those used on large social media sites and streaming services make extensive use of AI, machine learning and related techniques to learn the behavior and preferences of each user and categorize content to tailor their feed individually. For example, embeddings can be used to compare one given document with many other documents and return those that are most similar to the given document. The documents can be any type of media, such as news articles or user engagement with the movies they have watched.
Typically, the suggestions refer to various decision-making processes, such as what product to purchase, what music to listen to, or what online news to read.Recommender systems are used in a variety of areas, with commonly recognised examples taking the form of playlist generators for video and music services, product recommenders for online stores, or content recommenders for social media platforms and open web content recommenders. These systems can operate using a single type of input, like music, or multiple inputs within and across platforms like news, books and search queries. There are also popular recommender systems for specific topics like restaurants and online dating. Recommender systems have also been developed to explore research articles and experts, collaborators, and financial services.
