Query-dependent rank aggregation with local models

Authors:
Hsuan-Yu Lin;Chi-Hsin Yu;Hsin-Hsi Chen
Affiliations:
Department of Computer Science and Information Engineering, National Taiwan University, Taipei, Taiwan, ROC;Department of Computer Science and Information Engineering, National Taiwan University, Taipei, Taiwan, ROC;Department of Computer Science and Information Engineering, National Taiwan University, Taipei, Taiwan, ROC
Venue:
AIRS'11 Proceedings of the 7th Asia conference on Information Retrieval Technology
Year:
2011

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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.