Sequential Music Recommendations for Groups by Balancing User Satisfaction

Generating a sequence of music tracks recommendations to a group of users can be addressed by balancing the users’ satisfaction for a set of recommendations (the playlist), rather than finding items that individually provide good average satisfaction to the users. In this paper we introduce a ‘Balancing’ technique that builds a tracks’ sequence iteratively while constantly balancing users’ satisfaction levels. In a live user study we have shown that it produces playlist recommendations that are better than those generated by the average preference aggregation method and comparable to those manually compiled by