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Finding the Hidden Gems: Recommending Untagged Music

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Proceedings of the Twenty-Second International Joint Conference on Artificial Intelligence. Pages 2256-2260.

https://doi.org/10.5591/978-1-57735-516-8/IJCAI11-376

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.

Ben Horsburgh, Susan Craw, Stewart Massie, and Robin Boswell. 2011. Finding the Hidden Gems: Recommending Untagged Music. In Proceedings of the Twenty-Second International Joint Conference on Artificial Intelligence, IJCAI-11. IJCAI, 2256–2260. https://doi.org/10.5591/978-1-57735-516-8/IJCAI11-376
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