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2010

Learning Dense Models of Query Similarity from User Click Logs

8 years 11 months ago
Learning Dense Models of Query Similarity from User Click Logs
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.
Fabio De Bona, Stefan Riezler, Keith Hall, Massimi
Added 14 Feb 2011
Updated 14 Feb 2011
Type Journal
Year 2010
Where NAACL
Authors Fabio De Bona, Stefan Riezler, Keith Hall, Massimiliano Ciaramita, Amac Herdagdelen, Maria Holmqvist
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