A predictive model of music preference using pairwise comparisons
Publication: Research - peer-review › Article in proceedings – Annual report year: 2012
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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-review › Article in proceedings – Annual report year: 2012
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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 -