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Context-aware search personalization with concept preference

Published: 24 October 2011 Publication History

Abstract

As the size of the web is growing rapidly, a well-recognized challenge for developing web search engines is to optimize the search result towards each user's preference. In this paper, we propose and develop a new personalization framework that captures the user's preference in the form of concepts obtained by mining web search contexts. The search context consists of both the user's clickthroughs and query reformulations that satisfy some specific information need, which is able to provide more information than each individual query in a search session. We also propose a method that discovers search contexts by one-pass of raw search query log. Using the information of the search context, we develop eight strategies that derive conceptual preference judgment. A learning-to-rank approach is employed to combine the derived preference judgments and then a Context-Aware User Profile (CAUP) is created. We further employ CAUP to adapt a personalized ranking function. Experimental results demonstrate that our approach captures accurate and comprehensive user's preference and, in terms of Top-N results quality, outperforms those existing concept-based personalization approaches without using search contexts.

References

[1]
Daniel Gayo-Avello, A survey on session detection methods in query logs and a proposal for future evaluation, Information Sciences 179 (2009).
[2]
A. Herdagdelen and et al., Generalized syntactic and semantic models of query reformulation, Proc. of the SIGIR Conference, 2010.
[3]
J. Huang and E. N. Efthimiadis, Analyzing and evaluating query reformulation strategies in web search logs, Proc. of the CIKM Conference, 2009.
[4]
T. Joachims, Optimizing search engines using clickthrough data, Proc. of the SIGKDD Conference, 2002.
[5]
T. Joachims and et al., Evaluating the accuracy of implicit feedback from clicks and query reformulations in web search, ACM TOIS 25 (2007).
[6]
R. Jones and K. L. Klinkner, Beyond the session timeout: automatic hierarchical segmentation of search topics in query logs, Proc. of the CIKM Conference, 2008.
[7]
Y. Ke, L. Deng, W. Ng, and D. L. Lee, Web dynamics and their ramifications for the development of web search engines, Computer Networks 50 (2006), no. 10.
[8]
K. W. T Leung and D. L. Lee, Deriving concept-based user profiles from search engine logs, IEEE TKDE 22 (2010).
[9]
K. W. T. Leung, W. Ng, and D. L. Lee, Personalized concept-based clustering of search engine queries, IEEE TKDE 20 (2008).
[10]
J. Luxenburger, S. Elbassuoni, and G. Weikum, Matching task profiles and user needs in personalized web search, Proc. of the CIKM Conference, 2008.
[11]
F. Radlinski and T. Joachims, Query chains: learning to rank from implicit feedback, Proc. of the SIGKDD Conference, 2005.
[12]
X. Shen, B. Tan, and C. X. Zhai, Context-sensitive information retrieval using implicit feedback, Proc. of the SIGIR Conference, 2005.
[13]
Jaime Teevan, Meredith Ringel Morris, and Steve Bush, Discovering and using groups to improve personalized search, Proc. of the ACM WSDM Conference, 2009.
[14]
E. Voorhees and D. Harman, Trec experiment and evaluation in information retrieval, MIT Press, Cambridge, MA, 2005.
[15]
B. Xiang, D. Jiang, J. Pei, X. Sun, E. Chen, and H. Li, Context-aware ranking in web search, Proc. of the SIGIR Conference, 2010.

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    cover image ACM Conferences
    CIKM '11: Proceedings of the 20th ACM international conference on Information and knowledge management
    October 2011
    2712 pages
    ISBN:9781450307178
    DOI:10.1145/2063576
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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    Publication History

    Published: 24 October 2011

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    Author Tags

    1. clickthrough
    2. query reformulation
    3. search personalization

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    • (2020)A Quantum Interior Point Method for LPs and SDPsACM Transactions on Quantum Computing10.1145/34063061:1(1-32)Online publication date: 2-Oct-2020
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