[2A3-E-5] Human interface, education aid: chance discoveries

[2A3-E-5-02] Proposal of Context-aware Music Recommender System Using Negative Sampling

〇Jin-cheng ZHANG¹, Yasufumi TAKAMA¹ (1. Tokyo Metropolitan University)

Keywords:Music Recommendation, Context-aware Recommendation, Factorization Machines, Negative Sampling

This paper proposes a method for recommending music items considering listeners' context information. Recently, users can enjoy music easily regardless of time and a place due to evolution of online music services such as Spotify. However, it is difficult for us to find appropriate music items from enormous resources. On the other hand, because of listening style and characteristic of music items, music items do not usually have explicit rating. Therefore, implicit feedback such as playing count has been popular to construct recommender systems. As additional information, this paper considers listeners' context. The proposed method employs FMs (Factorization Machines), in which the context information is treated as factors. Negative sampling is applied to reduce the number of negative samples (music items a user has yet to be listened). The effectiveness of the proposed method and the effect of negative sampling are shown with an offline experiment.