Recommender Systems Handbook
Exploring the music similarity space on the web
ACM Transactions on Information Systems (TOIS)
The need for music information retrieval with user-centered and multimodal strategies
MIRUM '11 Proceedings of the 1st international ACM workshop on Music information retrieval with user-centered and multimodal strategies
Unifying Low-Level and High-Level Music Similarity Measures
IEEE Transactions on Multimedia
Time Series Models for Semantic Music Annotation
IEEE Transactions on Audio, Speech, and Language Processing
On building a reusable Twitter corpus
SIGIR '12 Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval
Leveraging microblogs for spatiotemporal music information retrieval
ECIR'13 Proceedings of the 35th European conference on Advances in Information Retrieval
Local and global scaling reduce hubs in space
The Journal of Machine Learning Research
Proceedings of International Conference on Advances in Mobile Computing & Multimedia
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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.