Personalizing atypical web search sessions

  • Authors:
  • Carsten Eickhoff;Kevyn Collins-Thompson;Paul N. Bennett;Susan Dumais

  • Affiliations:
  • Delft University of Technology, Delft, Netherlands;Microsoft Research, Redmond, WA, USA;Microsoft Research, Redmond, WA, USA;Microsoft Research, Redmond, WA, USA

  • Venue:
  • Proceedings of the sixth ACM international conference on Web search and data mining
  • Year:
  • 2013

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Abstract

Most research in Web search personalization models 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 for a significant number of search sessions, users diverge from their regular search profiles in order to satisfy atypical, limited-duration information needs. In this work, we conduct a large-scale inspection of real-life search sessions to further understand this scenario. 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 proposed method is evaluated based on Web-scale search session data spanning several months of user activity.