A Gaussian Process Classification Approach to Binary Preference Learning
Ferenc Huszar
In: Choice Models and Preference Learning Workshop, 17 December 2011, Spain.

Abstract

In this paper I present the problem of learning from pairwise preference judgements as a special case of binary classification. I discuss why kernel classifiers using traditional kernels based on the distance between items cannot be used to address the problem effectively: the preference prediction problem has inherent symmetry properties that these kernels cannot model. I will review a hierarchical Bayesian model for preference learning that by construction respects these symmetry properties and show its equivalence to probit Gaussian process (GP) classification. Motivated by this model I define the preference judgement kernel and show that it is capable of modelling the symmetry of preference judgement.

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