Simrank++：通过点击图的链接分析进行查询重写
Simrank++: Query rewriting through link analysis of the click graph
摘要：
我们专注于赞助搜索的查询重写问题。我们基于历史点击图进行重写，该图记录了响应过去用户查询而点击的广告。给定查询q，我们首先考虑Simrank作为识别与q类似的查询的方式，即，查询用户可能对哪些广告感兴趣。我们认为，Simrank未能正确识别我们的应用程序中的查询相似性，我们提出了两个增强版本的Simrank：一个利用点击图边缘的权重，另一个利用``证据。我们通过实验评估我们的新方案，使用实际的点击图和来自雅虎的查询，and using运用a variety品种of metrics指标.我们的结果表明，增强的方法可以产生更多，更好的查询重写。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
