Finding the hidden gems: recommending untagged music.

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

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

• Presentation Conference Type: Conference Paper (published)
• 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
• Peer Reviewed: Peer Reviewed
• 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
• Contract Date: Sep 18, 2013