Personalized Next-Track Music Recommendation with Multi-dimensional Long-Term Preference Signals

The automated generation of playlists given a user’s most recent listening history is a common feature of modern music streaming platforms. In the research literature, a number of algorithmic proposals for this “next-track recommendation” problem have been made in recent years. However, nearly all of them are based on the user’s most recent listening history, context, or location but do not consider the users’ long-term listening preferences or social network. In this work, we explore the value of long-term preferences for personalizing the playlist generation process and evaluate different strategies of applying multi-dimensional user-specific preference signals. The results of an empirical evaluation on five different datasets show that although the short-term listening history should generally govern the next-track selection process, long-term preferences can measurably help to increase the personalization quality.