Learning to Rank Based on Query Clustering
Hua Gui · 2012
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.