A predictive model of music preference using pairwise comparisons

Sand Jensen, B., Saez Gallego, J. and Larsen, J. (2012) A predictive model of music preference using pairwise comparisons. In: 2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Kyoto, Japan, 25-30 Mar 2012, pp. 1977-1980. ISBN 9781467300452 (doi:10.1109/ICASSP.2012.6288294)

Full text not currently available from Enlighten.

Abstract

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.

Item Type:Conference Proceedings
Status:Published
Refereed:Yes
Glasgow Author(s) Enlighten ID:Jensen, Dr Bjorn Sand
Authors: Sand Jensen, B., Saez Gallego, J., and Larsen, J.
College/School:College of Science and Engineering > School of Computing Science
ISSN:1520-6149
ISBN:9781467300452

University Staff: Request a correction | Enlighten Editors: Update this record