Publication: Research - peer-review › Article in proceedings – Annual report year: 2012
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.
|Title||2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)|
|Conference||IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2012)|
|Period||25-03-12 → 30-03-12|
|Name||I E E E International Conference on Acoustics, Speech and Signal Processing. Proceedings|
|Citations||Web of Science® Times Cited: No match on DOI|
- Music Preference, Kernel Methods, Gaussian Process Priors, Recommendation
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