A Context-Aware Music Recommendation

Abstract

The world is facing a drastic change in a way music’s are consumed by people. This is to mean that public music streaming collections that contain not less than a million tracks which generate tons of data are preferred by people than those private collections which contain less. Recommender systems support users in discovering music from such streaming services based on their preference. This is the reason why the field of music information retrieval as well as music recommendation is highly in demand for industry and academia. In this paper, we propose a context-aware music recommender system that makes the recommendation based on finding similarities among users linked by similar interests, music as well as users contextual information by exploiting a social network data. Social tag information is utilized as context data which are source of human generated contextual knowledge about music.