Query-dependent rank aggregation with local models

Abstract

The technologies of learning to rank have been successfully used in information retrieval. General ranking approaches use all training queries to build a single ranking model and apply this model to all different kinds of queries. Such a "global" ranking approach does not deal with the specific properties of queries. In this paper, we propose three query-dependent ranking approaches which combine the results of local models. We construct local models by using clustering algorithms, represent queries by using various ways such as Kull-back-Leibler divergence, and apply a ranking function to merge the results of different local models. Experimental results show that our approaches are better than all rank-based aggregation approaches and some global models in LETOR4. Especially, we found that our approaches have better performance in dealing with difficult queries. © 2011 Springer-Verlag Berlin Heidelberg.