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Erschienen in:
2011 | OriginalPaper | Buchkapitel
Query-Dependent Rank Aggregation with Local Models
verfasst von : Hsuan-Yu Lin, Chi-Hsin Yu, Hsin-Hsi Chen
Erschienen in: Information Retrieval Technology
Verlag: Springer Berlin Heidelberg
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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.