Hybrid retrieval approaches to geospatial music recommendation

Recent advances in music retrieval and recommendation algorithms highlight the necessity to follow multimodal approaches in order to transcend limits imposed by methods that solely use audio, web, or collaborative filtering data. In this paper, we propose hybrid music recommendation algorithms that combine information on the music content, the music context, and the user context, in particular, integrating location-aware weighting of similarities. Using state-of-the-art techniques to extract audio features and contextual web features, and a novel standardized data set of music listening activities inferred from microblogs (MusicMicro), we propose several multimodal retrieval functions. The main contributions of this paper are (i) a systematic evaluation of mixture coefficients between state-of-the-art audio features and web features, using the first standardized microblog data set of music listening events for retrieval purposes and (ii) novel geospatial music recommendation approaches using location information of microblog users, and a comprehensive evaluation thereof.