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Task-aware Search Personalization

MPG-Autoren
http://pubman.mpdl.mpg.de/cone/persons/resource/persons44955

Luxenburger,  Julia
Databases and Information Systems, MPI for Informatics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons44375

Elbassuoni,  Shady
Databases and Information Systems, MPI for Informatics, Max Planck Society;
International Max Planck Research School, MPI for Informatics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons45720

Weikum,  Gerhard
Databases and Information Systems, MPI for Informatics, Max Planck Society;

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Zitation

Luxenburger, J., Elbassuoni, S., & Weikum, G. (2008). Task-aware Search Personalization. In S.-H. Myaeng, D. W. Oard, F. Sebastiani, T.-S. Chua, & M.-K. Leong (Eds.), ACM SIGIR 2008: Thirty-First Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (pp. 721-722). New York, NY: ACM.


Zitierlink: http://hdl.handle.net/11858/00-001M-0000-000F-1D1E-5
Zusammenfassung
Search personalization has been pursued in many ways, in order to provide better result rankings and better overall search experience to individual users. However, blindly applying personalization to all user queries, for example, by a background model derived from the user's long-term query-and-click history, is not always appropriate for aiding the user in accomplishing her actual task. User interests change over time, a user sometimes works on very different categories of tasks within a short timespan, and history-based personalization may impede a user's desire of discovering new topics. In this paper we propose a personalization framework that is selective in a twofold sense. First, it selectively employs personalization techniques for queries that are expected to benefit from prior history information, while refraining from undue actions otherwise. Second, we introduce the notion of tasks representing different granularity levels of a user profile, ranging from very specific search goals to broad topics, and base our reasoning selectively on query-relevant user tasks. These considerations are cast into a statistical language model for tasks, queries, and documents, supporting both judicious query expansion and result re-ranking. The effectiveness of our method is demonstrated by an empirical user study.