 In this paper, we present how to tailor click models to user goals in web search through query term classification. We demonstrate that better predicative power could be achieved by fitting two click models for navigational queries and informational queries respectively, as evidenced by the likelihood and perplexity evaluation results on a subset of the MSN 2006 RFP data which consists of 121,179 distinct query terms and over 2.8 million query sessions. We also propose search relevance score (SRS) as a flexible evaluation metric of search engine performance. This metric can be derived as summary statistics under any click model, and is applicable to a single query session, a particular query term and the search engine overall.
