Matching task profiles and user needs in personalized web search

  • Authors:

  • Julia Luxenburger;Shady Elbassuoni;Gerhard Weikum

  • Affiliations:

  • Max-Planck Institut für Informatik, Saarbrücken, Germany;Max-Planck Institut für Informatik, Saarbrücken, Germany;Max-Planck Institut für Informatik, Saarbrücken, Germany

  • Venue:
  • Proceedings of the 17th ACM conference on Information and knowledge management
  • Year:
  • 2008

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Abstract

Personalization has been deemed one of the major challenges in information retrieval with a significant potential for providing better search experience to individual users. Especially, the need for enhanced user models better capturing elements such as users' goals, tasks, and contexts has been identified. In this paper, we introduce a statistical language model for user tasks representing different granularity levels of a user profile, ranging from very specific search goals to broad topics. We propose a personalization framework that selectively matches the actual user information need with relevant past user tasks, and allows to dynamically switch the course of personalization from re-finding very precise information to biasing results to general user interests. In the extreme, our model is able to detect when the user's search and browse history is not appropriate for aiding the user in satisfying her current information quest. Instead of blindly applying personalization to all user queries, our approach refrains from undue actions in these cases, accounting for the user's desire of discovering new topics, and changing interests over time. The effectiveness of our method is demonstrated by an empirical user study.