User Geospatial Context for Music Recommendation in Microblogs

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 doing 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 recommendation. To this end, we propose a geospatial model that uses GPS coordinates and a cultural model that uses semantic locations (continent, country, and state of the user). We conduct experiments on a novel standardized music collection, the “Million Musical Tweets Dataset” of listening events extracted from microblogs. Overall, we find that modeling listeners’ location via Gaussian mixture models and computing similarities from these outperforms both cultural user models and collaborative filtering.