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Learning dense models of query similarity from user click logs

By Fabio De Bona, Massimiliano Ciaramita, Stefan Riezler, Keith Hall and Maria Holmqvist


The goal of this work is to integrate query similarity metrics as features into a dense model that can be trained on large amounts of query log data, in order to rank query rewrites. We propose features that incorporate various notions of syntactic and semantic similarity in a generalized edit distance framework. We use the implicit feedback of user clicks on search results as weak labels in training linear ranking models on large data sets. We optimize different ranking objectives in a stochastic gradient descent framework. Our experiments show that a pairwise SVM ranker trained on multipartite rank levels outperforms other pairwise and listwise ranking methods under a variety of evaluation metrics.

Year: 2010
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