14 Facts About Recommender systems

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.

Recommender systems have been developed to explore research articles and experts, collaborators, and financial services.

Recommender systems usually make use of either or both collaborative filtering and content-based filtering, as well as other systems such as knowledge-based systems.

Recommender systems are a useful alternative to search algorithms since they help users discover items they might not have found otherwise.

Recommender systems looked for a way to recommend a user a book he might like.

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Recommender systems's idea was to create a system that asks the user specific questions and assigns him stereotypes depending on his answers.

One aspect of reinforcement learning that is of particular use in the area of recommender systems is the fact that the models or policies can be learned by providing a reward to the recommendation agent.

Multi-criteria recommender systems can be defined as recommender systems that incorporate preference information upon multiple criteria.

The majority of existing approaches to recommender systems focus on recommending the most relevant content to users using contextual information, yet do not take into account the risk of disturbing the user with unwanted notifications.

Mobile recommender systems make use of internet-accessing smart phones to offer personalized, context-sensitive recommendations.

Typically, research on recommender systems is concerned with finding the most accurate recommendation algorithms.

Recommender systems are notoriously difficult to evaluate offline, with some researchers claiming that this has led to a reproducibility crisis in recommender systems publications.

Deep learning and neural methods for recommender systems have been used in the winning solutions in several recent recommender system challenges, WSDM, RecSys Challenge.

Hence, operators of recommender systems find little guidance in the current research for answering the question, which recommendation approaches to use in a recommender systems.