A music recommendation system based on music data grouping and user interests
Proceedings of the tenth international conference on Information and knowledge management
Hybrid Recommender Systems: Survey and Experiments
User Modeling and User-Adapted Interaction
A Personalized Music Filtering System Based on Melody Style Classification
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
Evaluating collaborative filtering recommender systems
ACM Transactions on Information Systems (TOIS)
IEEE Transactions on Knowledge and Data Engineering
Content-based music audio recommendation
Proceedings of the 13th annual ACM international conference on Multimedia
Lightweight measures for timbral similarity of musical audio
Proceedings of the 1st ACM workshop on Audio and music computing multimedia
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In this paper we propose a novel approach for content-based music recommendation. The main innovation of the proposed technique consists of a similarity function that, instead of considering entire songs or their thumbnail representations, analyzes audio similarities between semantic segments from different audio tracks. The rationale of our idea is that a song similarity and recommendation technique, to be more meaningful to the user from a semantic point of view, may evaluate and exploit similarities on semantic units between audio tracks. Our similarity algorithm consists of two main stages: the first step performs segmentation of the song in semantic parts. The latter assigns a similarity and recommendation score to a pair of songs, by computing the distance between the representations of their segments. To assign the global similarity and recommendation score, we consider a consistent subset of all the inter-segment distances. By adopting a graph-bases framework, we propose a graph-reduction algorithm on weighted edges that connect segments of different songs to optimize the similarity score with respect to our recommendation goal. Experiments conducted on a database of 200 audio tracks of various authors and genres show promising results.