Modeling clicks beyond the first result page

Abstract

Most modern web search engines yield a list of documents of a fixed length (usually 10) in response to a user query. The next ten search results are usually available in one click. These documents either replace the current result page or are appended to the end. Hence, in order to examine more documents than the first 10 the user needs to explicitly express her intention. Although clickthrough numbers are lower for documents on the second and later result pages, they still represent a noticeable amount of traffic. We propose a modification of the Dynamic Bayesian Network (DBN) click model by explicitly including into the model the probability of transition between result pages. We show that our new click model can significantly better capture user behavior on the second and later result pages while giving the same performance on the first result page.