Tailoring click models to user goals

Abstract

Click models provide a principled way of understanding user interaction with web search results in a query session and a statistical tool for leveraging search engine click logs to analyze and improve user experience. An important component in all existing click models is the user behavior assumption -- how users scan, examine and click web documents listed in the result page. Usually the average user behavior pattern is summarized in a small set of global parameters. Can we fit multiple models with different user behavior parameters on a click data set? A previous study showed that the mixture modeling approach did not lead to better performance despite extra computational cost.