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.
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Lin, HY., Yu, CH., Chen, HH. (2011). Query-Dependent Rank Aggregation with Local Models. In: Salem, M.V.M., Shaalan, K., Oroumchian, F., Shakery, A., Khelalfa, H. (eds) Information Retrieval Technology. AIRS 2011. Lecture Notes in Computer Science, vol 7097. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-25631-8_1
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DOI: https://doi.org/10.1007/978-3-642-25631-8_1
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