Location-aware music recommendation using auto-tagging and hybrid matching

We propose a novel approach to context-aware music recommendation - recommending music suited for places of interest (POIs). The suggested hybrid approach combines two techniques -- one based on representing both POIs and music with tags, and the other based on the knowledge of the semantic relations between the two types of items. We show that our approach can be scaled up using a novel music auto-tagging technique and we compare it in a live user study to: two non-hybrid solutions, either based on tags or on semantic relations; and to a context-free but personalized recommendation approach. In the considered scenario, i.e., a situation defined by a context (the POI), we show that personalization (via music preference) is not sufficient and it is important to implement effective adaptation techniques to the user's context. In fact, we show that the users are more satisfied with the recommendations generated by combining the tag-based and knowledge-based context adaptation techniques, which exploit orthogonal types of relations between places and music tracks.