Simrank++: Query rewriting through link analysis of the click graph

TL;DR

This paper improves query rewriting for sponsored search by enhancing Simrank with weighted edges and evidence, leading to more accurate query similarity detection based on click graph analysis.

Contribution

The paper introduces two improved versions of Simrank that incorporate edge weights and evidence, addressing limitations in query similarity identification.

Findings

Enhanced methods outperform original Simrank in experiments

Weighted edges and evidence improve query rewrite quality

Results based on Yahoo! click graphs demonstrate effectiveness

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 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.