User Geospatial Context for Music Recommendation in Microblogs

Abstract

Music information retrieval and music recommendation are seeing a paradigm shift towards methods that incorporate user context aspects. However, structured experiments on a standardized music dataset to investigate the effects of do-ing so are scarce. In this paper, we compare performance of various combinations of collaborative filtering and geospatial as well as cultural user models for the task of music recom-mendation. To this end, we propose a geospatial model that uses GPS coordinates and a cultural model that uses seman-tic locations (continent, country, and state of the user). We conduct experiments on a novel standardized music collec-tion, the “Million Musical Tweets Dataset ” of listing events extracted from microblogs. Overall, we find that modeling listeners ’ location via Gaussian mixture models and comput-ing similarities from these outperforms both cultural user models and collaborative filtering. Categories and Subject Descriptors Information systems [Information retrieval]: Music rec-ommendation; Human-centered computing [Collaborative and social computing]: Social medi