Dyad ranking using a bilinear Plackett-Luce model

Abstract

Label ranking is a specific type of preference learning problem, namely the problem of learning a model that maps instances to rankings over a finite set of predefined alternatives. These alternatives are identified by their name or labelwhile not being characterized in terms of any properties or features that could be potentially useful for learning. In this paper, we consider a generalization of the label ranking problem that we call dyad ranking. In dyad ranking, not only the instances but also the alternatives are represented in terms of attributes. For learning in the setting of dyad ranking, we propose an extension of an existing label ranking method based on the Plackett-Luce model, a statistical model for rank data. Moreover, we present first experimental results confirming the usefulness of the additional information provided by the feature description of alternatives.