A Multigraph-Based Method for Improving Music Recommendation

Music recommendation systems have become an important part of the user-centric online music listening experience. However, current automated systems often are not tuned for exploiting the full diversity of a song catalogue, and consequently, discovering new music requires considerable user effort. Another issue is current implementations generally require significant artist metadata, user listening history, or a combination of the two, to generate relevant recommendations. To address the problems with traditional recommendation systems, we propose to represent artist-to-artist relationships as both simple multigraphs and more complicated multidimensional networks. Using data gathered from the MusicBrainz open music encyclopedia, we demonstrate our artist-based networks are capable of producing more diverse and relevant artist recommendations.