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 [5] 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. In sponsored search, paid advertisements (ads) relevant to a user's query are shown above or along-side traditional web search results. The placement of these ads is in general related to a ranking score which is a function of the semantic relevance to the query and the advertiser's bid.
Finally, REF presents two enhanced versions of SimRank, one that exploits the weights of edges in the click graph and another that exploits "evidence" supporting the similarity between queries.
Simrank++: Query rewriting through link analysis of the click graph
ArXiv
ArXiv
Computer Science