HUA Gui-Chun, ZHANG Min, LIU Yi-Qun, MA Shao-Ping, RU Li-Yun
State Key Laboratory of Intelligent Technology and Systems,Beijing 100084 Tsinghua National Laboratory for Information Science and Technology,Beijing 100084 Department of Computer Science and Technology,Tsinghua University,Beijing 100084
Abstract Learning to rank,the interdisciplinary field of information retrieval and machine learning, draws increasing attention and lots of models are designed to optimize the ranking functions. However, few methods take the differences among the queries into account. In this paper,the queries are modeled as multivariate Gaussian distributions and Kullback-Leibler divergence is adopted as distance measure. The spectral clustering is applied to cluster the queries into several clusters and a ranking function is learned for each cluster.The experimental results show that the ranking functions with clustering are trained with less data,but are comparable to or even outperform the ones without clustering.
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2012, Vol. 25 
