Ioannis Antonellis
Hector Garcia-Molina
ABSTRACT
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 [2] 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 versions 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.
- I. Antonellis, H. Garcia-Molina, and C. Chang. Simrank++: Query rewriting through link analysis of the click graph. In Technical Report, url: http://dbpubs.stanford.edu/pub/2007--32, 2007.Google Scholar
- Glen Jeh and Jennifer Widom. Simrank: a measure of structural-context similarity. In Proc. KDD'02. Google ScholarDigital Library
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Simrank++: query rewriting through link analysis of the clickgraph (poster)
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