The Importance of Song Context in Music Playlists

Music recommender systems o‰en operate in sequential mode by suggesting a collection of songs that constitute a listening session. Œis task is usually called automated music playlist generation and it has been previously studied in the literature with di‚erent successful approaches based on, e.g., variations of collaborative €ltering or content-based similarity. Some of the proposed playlist models take into consideration the current song and a number of previous songs, i.e., the song context, in order to predict the next song. However, it is not yet clear to what extent knowing this song context improves next-song predictions. To shed light on this question, we conduct a numerical experiment on two datasets of hand-curated music playlists, where we compare playlist models that account for different song context lengths. Our results indicate that knowing the song context seems, at €rst, uninformative. However, we explain this e‚ect by a strong bias in the data towards very popular songs and observe that, in fact, songs in the long tail are more accurately predicted when the song context is considered. CCS CONCEPTS •Information systems→Datamining; Recommender systems;