Socially-Motivated Music Recommendation

Abstract

Extensive literature spanning psychology, sociology, and musicology has sought to understand the motivations for why people listen to music, including both individually and socially motivated reasons. Music's social functions, while present throughout the world, may be particularly important in collectivist societies, but music recommender systems generally target individualistic functions of music listening. In this study, we explore how a recommender system focused on social motivations for music listening might work by addressing a particular motivation: the desire to listen to music that is trending in one’s community. We frame a recommendation task suited to this desire and propose a corresponding evaluation metric to address the timeliness of recommendations. Using listening data from Spotify, we construct a simple, heuristic-based approach to introduce and explore this recommendation task. Analyzing the effectiveness of this approach, we discuss what we believe is an overlooked trade-off between the precision and timeliness of recommendations, as well as considerations for modeling users' musical communities. Finally, we highlight key cultural differences in the effectiveness of this approach, underscoring the importance of incorporating a diverse cultural perspective in the development and evaluation of recommender systems.