Towards Intent-Aware Contextual Music Recommendation: Initial Experiments

While activity-aware music recommendation has been shown to improve the listener experience, we posit that modeling the listening intent can further improve recommendation quality. In this paper, we perform initial exploration of the dominant music listening intents associated with common activities, using music retrieved from popular online music services. We show that these intents can be approximated through audio features of the music itself, and potentially improve recommendation quality. Our initial results, based on 10 common activities and 5 popular listening intents associated with these activities, support our hypothesis, and open a promising direction towards intent-aware contextual music recommendation.