A recommender system is a type of information filtering system that provides users with suggestions for items that might be of interest to them. These suggestions assist users in making decisions such as what products to buy or music to listen to. Recommender systems can be found in various platforms and are used in diverse sectors such as product recommendations, music playlist generation, restaurant suggestions, and financial services. They work by using either single or multiple types of inputs. The development of these systems has been a collaborative effort over time, with contributions from different researchers. There are several approaches to building recommender systems, including collaborative filtering, content-based filtering, and hybrid methods. However, each approach has its challenges and they are evaluated using various techniques. Advanced techniques in recommender systems include reinforcement learning and multi-criteria systems.
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A recommender system, or a recommendation system (sometimes replacing "system" with terms such as "platform", "engine", 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.
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.
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