Predicting User Demographics from Music Listening Information

Online activities such as social networking, shopping, and consuming multi-media create digital traces often used to improve user experience and increase revenue, e.g., through better-fitting recommendations and targeted marketing. We investigate to which extent the music listening habits of users of the social music platform Last.fm can be used to predict their age, gender, and nationality. We propose a TF-IDF-like feature modeling approach for artist listening information and artist tags combined with additionally extracted features. We show that we can substantially outperform a baseline majority voting approach and can compete with existing approaches. Further, regarding prediction accuracy vs. available listening data we show that even one single listening event per user is enough to outperform the baseline in all prediction tasks. We conclude that personal information can be derived from music listening information, which indeed can help better tailoring recommendations.