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Learning pseudo-tags to augment sparse tagging in hybrid music recommender systems.

Horsburgh, Ben; Craw, Susan; Massie, Stewart


Ben Horsburgh


Online recommender systems are an important tool that people use to find new music. To generate recommendations, many systems rely on tag representations of music. Such systems however suffer from tag sparsity, whereby tracks lack a strong tag representation. Current state-of-the-art techniques that reduce this sparsity problem create hybrid systems using multiple representations, for example both content and tags. In this paper we present a novel hybrid representation that augments sparse tag representations without introducing content directly. Our hybrid representation integrates pseudotags learned from content into the tag representation of a track, and a dynamic weighting scheme limits the number of pseudo-tags that are allowed to contribute. Experiments demonstrate that this method allows tags to remain dominant when they provide a strong representation, and pseudo-tags to take over when tags are sparse. We show that our approach significantly improves recommendation quality not only for queries with a sparse tag representation, but also those that are well-tagged. Our hybrid approach has potential to be extended to other music representations that are used for recommendation, but suffer from data sparsity, such as user profiles.


HORSBURGH, B., CRAW, S. and MASSIE, S. 2015. Learning pseudo-tags to augment sparse tagging in hybrid music recommender systems. Artificial intelligence [online], 219, pages 25-39. Available from:

Journal Article Type Article
Acceptance Date Nov 18, 2014
Online Publication Date Nov 25, 2014
Publication Date Feb 28, 2015
Deposit Date Dec 23, 2014
Publicly Available Date Dec 23, 2014
Journal Artificial intelligence
Print ISSN 0004-3702
Electronic ISSN 1872-7921
Publisher Elsevier
Peer Reviewed Peer Reviewed
Volume 219
Pages 25-39
Keywords Music recommendation; Hybrid representations
Public URL


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