Survey On Methods For Building Session-Based Recommender Systems

Marina Ninichuk, Dmitry Namiot

Abstract


Recommender systems currently play a significant role in many areas related to the processing of large amounts of information, such as online stores and cinemas, services for listening to music. As a rule, the construction of recommendations is based on the analysis of information about the user’s preferences received in the past. Often such information is presented in the form of a matrix of usersobjects. However, in some situations, information about the user may not be available, for example, when visiting the service for the first time or anonymously. A similar statement of the problem is typical for a special class of recommender systems — session-based recommender systems (SBRS). Unlike classical approaches, SBRS learn user’s information from current sessions, which makes it possible to obtain information about his rapidly changing preferences. The purpose of recommendations is usually to predict the next object that the user will pay attention to, or the set of such objects in the current session. This article provides an overview of the algorithms used in session-based recommender systems. It also provides an overview and comparison of frameworks for building sessionbased recommender systems.

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