A predictive model of music preference using pairwise comparisons

Publication: Research - peer-reviewArticle in proceedings – Annual report year: 2012

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Music recommendation is an important aspect of many streaming services and multi-media systems, however, it is typically based on so-called collaborative filtering methods. In this paper we consider the recommendation task from a personal viewpoint and examine to which degree music preference can be elicited and predicted using simple and robust queries such as pairwise comparisons. We propose to model - and in turn predict - the pairwise music preference using a very flexible model based on Gaussian Process priors for which we describe the required inference. We further propose a specific covariance function and evaluate the predictive performance on a novel dataset. In a recommendation style setting we obtain a leave-one-out accuracy of 74% compared to 50% with random predictions, showing potential for further refinement and evaluation.
Original languageEnglish
Title2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
PublisherIEEE
Publication date2012
Pages1977-1980
ISBN (print)978-1-4673-0045-2
ISBN (electronic)978-1-4673-0044-5
DOIs
StatePublished

Conference

ConferenceIEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2012)
CountryJapan
CityKyoto
Period25/03/1230/03/12
Internet addresshttp://www.icassp2012.com/
NameI E E E International Conference on Acoustics, Speech and Signal Processing. Proceedings
ISSN (Print)1520-6149
CitationsWeb of Science® Times Cited: No match on DOI

Keywords

  • Music Preference, Kernel Methods, Gaussian Process Priors, Recommendation
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