We focus on the problem of query rewriting for sponsored search. We base rewrites on a historical click graph that records the ads that have been clicked on in response to past user queries. Given a query q, we first consider Simrank as a way to identify queries similar to q, i.e., queries whose ads a user may be interested in. We argue that Simrank fails to properly identify query similarities in our application, and we present two enhanced version of Simrank: one that exploits weights on click graph edges and another that exploits ``evidence.'' We experimentally evaluate our new schemes against Simrank, using actual click graphs and queries form Yahoo!, and using a variety of metrics. Our results show that the enhanced methods can yield more and better query rewrites.
@article{arxiv.0712.0499,
title = {Simrank++: Query rewriting through link analysis of the click graph},
author = {Ioannis Antonellis and Hector Garcia-Molina and Chi-Chao Chang},
journal= {arXiv preprint arXiv:0712.0499},
year = {2007}
}
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Available via http://dbpubs.stanford.edu/pub/2007-32