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  Task-aware Search Personalization

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.

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Item Permalink: https://hdl.handle.net/11858/00-001M-0000-000F-1D1E-5 Version Permalink: https://hdl.handle.net/11858/00-001M-0000-000F-1D1F-3
Genre: Conference Paper

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 Creators:
Luxenburger, Julia1, Author           
Elbassuoni, Shady1, 2, Author           
Weikum, Gerhard1, Author           
Affiliations:
1Databases and Information Systems, MPI for Informatics, Max Planck Society, ou_24018              
2International Max Planck Research School, MPI for Informatics, Max Planck Society, ou_1116551              

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 Abstract: 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.

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Language(s): eng - English
 Dates: 2009-03-252008
 Publication Status: Issued
 Pages: -
 Publishing info: New York, NY : ACM
 Table of Contents: -
 Rev. Type: -
 Identifiers: eDoc: 428153
URI: http://doi.acm.org/10.1145/1390334.1390469
Other: Local-ID: C125756E0038A185-95FBECD09D7B613BC1257424005384EC-LuxSigir2008
 Degree: -

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Title: Untitled Event
Place of Event: Singapore
Start-/End Date: 2008-07-20 - 2008-07-24

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Title: ACM SIGIR 2008 : Thirty-First Annual International ACM SIGIR Conference on Research and Development in Information Retrieval
Source Genre: Proceedings
 Creator(s):
Myaeng, Sung-Hyon, Editor
Oard, Douglas W., Editor
Sebastiani, Fabrizio, Editor
Chua, Tat-Seng, Editor
Leong, Mun-Kew, Editor
Affiliations:
-
Publ. Info: New York, NY : ACM
Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 721 - 722 Identifier: ISBN: 978-1-60558-164-4

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Title: ACM SIGIR Forum
Source Genre: Series
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Pages: - Volume / Issue: - Sequence Number: - Start / End Page: - Identifier: -