Learning to rank order — a distance-based approach

Learning to rank order is a machine learning paradigm that is different to the common machine learning paradigms: learning to classify cluster or approximate. It has the potential to reveal more hidden knowledge in data than classification. Cohen, Schapire and Singer were early investigators of this problem. They took a preference-based approach where pairwise preferences were combined into a total ordering. It is however not always possible to have knowledge of pairwise preferences. In this paper we consider a distance-based approach to ordering, where the ordering of alternatives is predicted on the basis of their distances to a query. To learn such an ordering function we consider two orderings: one is the actual ordering and another one is the predicted ordering. We aim to maximise the agreement of the two orderings by varying the parameters of a distance function, resulting in a trained distance function which is taken to be the ordering function. We evaluated this work by comparing the trained distance and the untrained distance in an experiment on public data. Results show that the trained distance leads in general to a higher degree of agreement than untrained distance.