State-of-the-art web search personalization treats users as static or slowly evolving entities with a given set of preferences defined by their past behavior. However, recent publications as well as empirical evidence suggest that there is a significant number of search sessions in which users diverge from their regular search profiles in order to satisfy atypical, non-recurring information needs. In this work, we conduct a large-scale inspection of real life search sessions to further the understanding of this problem. Subsequently, we design an automatic means of detecting and supporting such atypical sessions. We demonstrate significant improvements over state-of-the-art web search personalization techniques by accounting for the typicality of search sessions. The merit of the proposed method is evaluated based on web-scale search session data spanning several months of user activity.
This work together with Kevyn Collins-Thompson, Paul Bennett and Susan Dumais has been accepted for full oral presentation at the ACM International Conference on Web Search and Data Mining (WSDM) in Rome, Italy.