A predictive model of music preference using pairwise comparisons

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

Standard

A predictive model of music preference using pairwise comparisons. / Jensen, Bjørn Sand; Gallego, Javier Saez ; Larsen, Jan.

In: 2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2012. p. 1977-1980 (I E E E International Conference on Acoustics, Speech and Signal Processing. Proceedings).

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

Harvard

Jensen, BS, Gallego, JS & Larsen, J 2012, 'A predictive model of music preference using pairwise comparisons'. in: 2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, pp. 1977-1980 . I E E E International Conference on Acoustics, Speech and Signal Processing. Proceedings

APA

Jensen, B. S., Gallego, J. S., & Larsen, J. (2012). A predictive model of music preference using pairwise comparisons. In: 2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). (pp. 1977-1980 ). IEEE. (I E E E International Conference on Acoustics, Speech and Signal Processing. Proceedings).

CBE

Jensen BS, Gallego JS, Larsen J. 2012. A predictive model of music preference using pairwise comparisons. In 2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE. pp. 1977-1980 . (I E E E International Conference on Acoustics, Speech and Signal Processing. Proceedings).

MLA

Jensen, Bjørn Sand, Javier Saez Gallego, and JanLarsen "A predictive model of music preference using pairwise comparisons". In: 2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE. 2012. 1977-1980 . (I E E E International Conference on Acoustics, Speech and Signal Processing. Proceedings).

Vancouver

Jensen BS, Gallego JS, Larsen J. A predictive model of music preference using pairwise comparisons. In: 2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE. 2012. p. 1977-1980 . (I E E E International Conference on Acoustics, Speech and Signal Processing. Proceedings).

Author

Jensen, Bjørn Sand; Gallego, Javier Saez ; Larsen, Jan / A predictive model of music preference using pairwise comparisons.

In: 2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2012. p. 1977-1980 (I E E E International Conference on Acoustics, Speech and Signal Processing. Proceedings).

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

Bibtex

@inbook{f501c428218d4132ba0cfaa9eafb547c,
title = "A predictive model of music preference using pairwise comparisons",
publisher = "IEEE",
author = "Jensen, {Bjørn Sand} and Gallego, {Javier Saez} and Jan Larsen",
year = "2012",
isbn = "978-1-4673-0045-2",
series = "I E E E International Conference on Acoustics, Speech and Signal Processing. Proceedings",
pages = "1977-1980",
booktitle = "2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)",

}

RIS

TY - GEN

T1 - A predictive model of music preference using pairwise comparisons

A1 - Jensen,Bjørn Sand

A1 - Gallego,Javier Saez

A1 - Larsen,Jan

AU - Jensen,Bjørn Sand

AU - Gallego,Javier Saez

AU - Larsen,Jan

PB - IEEE

PY - 2012

Y1 - 2012

N2 - 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.

AB - 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.

KW - Music Preference

KW - Kernel Methods

KW - Gaussian Process Priors

KW - Recommendation

U2 - 10.1109/ICASSP.2012.6288294

DO - 10.1109/ICASSP.2012.6288294

SN - 978-1-4673-0045-2

BT - 2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

T2 - 2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

T3 - I E E E International Conference on Acoustics, Speech and Signal Processing. Proceedings

T3 - en_GB

SP - 1977

EP - 1980

ER -