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Mining clickthrough data for collaborative web search

Published: 23 May 2006 Publication History

Abstract

This paper is to investigate the group behavior patterns of search activities based on Web search history data, i.e., clickthrough data, to boost search performance. We propose a Collaborative Web Search (CWS) framework based on the probabilistic modeling of the co-occurrence relationship among the heterogeneous web objects: users, queries, and Web pages. The CWS framework consists of two steps: (1) a cube-clustering approach is put forward to estimate the semantic cluster structures of the Web objects; (2) Web search activities are conducted by leveraging the probabilistic relations among the estimated cluster structures. Experiments on a real-world clickthrough data set validate the effectiveness of our CWS approach.

References

[1]
J. S. Breese, D. Heckerman, and C. Kadie. Empirical analysis of predictive algorithms for collaborative filtering. In UAI, pages 43--52. Morgan Kaufman, 1998.
[2]
I. S. Dhillon, S. Mallela, and D. S. Modha. Information-theoretic co-clustering. In SIGKDD, pages 89--98, 2003.
[3]
B. Smyth, E. Balfe, O. Boydell, K. Bradley, P. Briggs, M. Coyle, and J. Freyne. A live-user evaluation of collaborative web search. In IJCAI, pages 1419--1424, 2005.

Cited By

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  • (2012)Enhancing group awareness on the webInteracting with Computers10.1016/j.intcom.2012.05.00624:5(398-408)Online publication date: 1-Sep-2012
  • (2011)Group Crumb: Sharing Web Navigation by Visualizing Group Traces on the WebECSCW 2011: Proceedings of the 12th European Conference on Computer Supported Cooperative Work, 24-28 September 2011, Aarhus Denmark10.1007/978-0-85729-913-0_19(353-372)Online publication date: 4-Jul-2011
  • (2010)Personalization of tagging systemsInformation Processing and Management: an International Journal10.1016/j.ipm.2009.06.00246:1(58-70)Online publication date: 1-Jan-2010

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cover image ACM Conferences
WWW '06: Proceedings of the 15th international conference on World Wide Web
May 2006
1102 pages
ISBN:1595933239
DOI:10.1145/1135777
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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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 23 May 2006

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Author Tags

  1. clickthrough data
  2. collaborative web search
  3. cube-clustering

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Overall Acceptance Rate 1,899 of 8,196 submissions, 23%

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Cited By

View all
  • (2012)Enhancing group awareness on the webInteracting with Computers10.1016/j.intcom.2012.05.00624:5(398-408)Online publication date: 1-Sep-2012
  • (2011)Group Crumb: Sharing Web Navigation by Visualizing Group Traces on the WebECSCW 2011: Proceedings of the 12th European Conference on Computer Supported Cooperative Work, 24-28 September 2011, Aarhus Denmark10.1007/978-0-85729-913-0_19(353-372)Online publication date: 4-Jul-2011
  • (2010)Personalization of tagging systemsInformation Processing and Management: an International Journal10.1016/j.ipm.2009.06.00246:1(58-70)Online publication date: 1-Jan-2010

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