KISS MIR: Keep it semantic and social music information retrieval

While content-based approaches for music information retrieval (MIR) have been heavily investigated, user-centric approaches are still in their early stage. Existing user-centric approaches use either music-context or user-context to personalize the search. However, none of them give the possibility to the user to choose the suitable context for his needs. In this paper we propose KISS MIR, a versatile approach for music information retrieval. It consists in combining both music-context and user-context to rank search results. The core contribution of this work is the investigation of different types of contexts derived from social networks. We distinguish semantic and social information and use them to build semantic and social profiles for music and users. The different contexts and profiles can be combined and personalized by the user. We have assessed the quality of our model using a real dataset from Last.fm. The results show that the use of user-context to rank search results is two times better than the use of music-context. More importantly, the combination of semantic and social information is crucial for satisfying user needs.