An Overview of Recommender Systems and Its Next Generation: Context-Aware Recommender Systems

Abstract

Recommender Systems have been generally utilized in different areas including motion pictures, news, music with an intend to give the most important recommendations to clients from an assortment of accessible alternatives. Recommender Systems are planned utilizing procedures from numerous fields, some of which are: AI, data recovery, information mining, direct variable based math and man-made consciousness. However, in typical commodity applications, due to the huge user and project library and just few evaluations (Sparsity issue), and at the point when the client is new to Recommender Frameworks, the framework can’t prescribe things that are applicable to clients in light of absence of past data about the client as well as the client thing rating history that assists with deciding the clients’ preferences (cold start). What’s more, presently there’s an innovation called Context-aware Recommender Systems (CARS), which utilizing setting information (location, time, peer, etc.) during the time spent proposal. In this work, we present an outline of some of noticeable customary RS and the high level CARS. We discuss the advantages and disadvantages of them. Furthermore, we reveal some inherent problems in RS. At last, we make a conclusion and give some challenges in current works.