Proposal of Timestamp-based Dynamic Contextual Features for Music Recommender Systems

Abstract

This paper proposes dynamic contextual features based on timestamp information for music recommender systems. Recently, with the development of communication technology and audiovisual devices, online music streaming has become more available to users, so it has become especially important to improve the accuracy of music recommender systems in order to enhance the users ’experience. On the other hand, due to the way how music is listened to and the characteristic of music items, it is difficult to obtain explicit feedback such as ratings from listeners. Therefore, context-aware music recommender systems that use the listener’s contextual information as auxiliary information have become one of the popular research directions. However, existing context-aware music recommender systems have mainly focused on listeners’ static contextual information such as nationality and languages. To consider dynamic contextual information, this paper focuses on the timestamps of listening events and proposes new contextual features about sequence of listening events. An experiment is conducted by incorporating the proposed features into FMs (Factorization Machines), of which the result shows the effectiveness of the proposed features.