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 Sim-rank [ 7] as a way to identify queries similar to q, i.e., queries whose ads a user may be interested in. We argue that Sim-rank fails to properly identify query similarities in our application, and we present two enhanced versions of Sim-rank: 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 from Yahoo!, and using a variety of metrics. Our results show that the enhanced methods can yield more and better query rewrites.
