PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

On Sparse Multi-Task Gaussian Process Priors for Music Preference Learning
Jens Brehm Nielsen, Bjørn Sand Jensen and Jan Larsen
In: NIPS 2011 Workshop on Choice Models and Preference Learning, 17 Dec 2011, Sierra Nevada, Spain.


In this paper we study pairwise preference learning in a music setting with multitask Gaussian processes, and examine the effect of sparsity in the input space as well as in the actual judgments. To introduce sparsity in the inputs, we extend a classic pairwise likelihood model to support sparse, multi-task Gaussian process priors based on the pseudo-input formulation. Sparsity in the actual pairwise judgments is obtained by a simple greedy sequential design approach. We combine the sequential approach with the pseudo-input preference model and demonstrate various properties of the combined model on synthetic examples. A preliminary simulation shows the performance on a real-world music preference dataset motivating and showing the potential of the sparse Gaussian process formulation for pairwise likelihoods.

EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:User Modelling for Computer Human Interaction
Information Retrieval & Textual Information Access
ID Code:9213
Deposited By:Bjørn Sand Jensen
Deposited On:21 February 2012