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Finding the hidden gems: recommending untagged music.

Horsburgh, Ben; Craw, Susan; Massie, Stewart; Boswell, Robin

Authors

Ben Horsburgh

Robin Boswell



Abstract

We have developed a novel hybrid representation for Music Information Retrieval. Our representation is built by incorporating audio content into the tag space in a tag-track matrix, and then learning hybrid concepts using latent semantic analysis. We apply this representation to the task of music recommendation, using similarity-based retrieval from a query music track. We also develop a new approach to evaluating music recommender systems, which is based upon the relationship of users liking tracks. We are interested in measuring the recommendation quality, and the rate at which cold-start tracks are recommended. Our hybrid representation is able to outperform a tag-only representation, in terms of both recommendation quality and the rate that cold-start tracks are included as recommendations.

Citation

HORSBURGH, B., CRAW, S., MASSIE, S. and BOSWELL, R. 2011. Finding the hidden gems: recommending untagged music. In Proceedings of the 22nd International joint conference on artificial intelligence (IJCAI-11), 16-22 July 2011, Barcelona, Spain. Palo Alto: AAAI Press [online], pages 2256-2261. Available from: https://doi.org/10.5591/978-1-57735-516-8/IJCAI11-376

Conference Name 22nd International joint conference on artificial intelligence (IJCAI-11)
Start Date Jul 16, 2011
End Date Jul 22, 2011
Acceptance Date Mar 31, 2011
Online Publication Date Dec 31, 2011
Publication Date Dec 31, 2011
Deposit Date Sep 18, 2013
Publicly Available Date Sep 18, 2013
Publisher Association for the Advancement of Artificial Intelligence
Pages 2256-2261
ISBN 9781577355120
DOI https://doi.org/10.5591/978-1-57735-516-8/IJCAI11-376
Public URL http://hdl.handle.net/10059/867

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