Using learned browsing behavior models to recommend relevant web pages

We introduce our research on learning browsing behavior models for inferring a user's information need (corresponding to a set of words) based on the actions he has taken during his current web session. This information is then used to find relevant pages, from essentially anywhere on the web. The models, learned from over one hundred users during a fiveweek user study, are session-specific but independent of both the user and website. Our empirical results suggest that these models can identify and satisfy the current information needs of users, even if they browse previously unseen pages containing unfamiliar words.