Article

A community-aware search engine

Authors:

Rodrigo B. Almeida,

Virgilio A. F. AlmeidaAuthors Info & Claims

WWW '04: Proceedings of the 13th international conference on World Wide Web

Pages 413 - 421

https://doi.org/10.1145/988672.988728

Published: 17 May 2004 Publication History

Abstract

Current search technologies work in a "one size fits all" fashion. Therefore, the answer to a query is independent of specific user information need. In this paper we describe a novel ranking technique for personalized search servicesthat combines content-based and community-based evidences. The community-based information is used in order to provide context for queries andis influenced by the current interaction of the user with the service. Ouralgorithm is evaluated using data derived from an actual service available on the Web an online bookstore. We show that the quality of content-based ranking strategies can be improved by the use of communityinformation as another evidential source of relevance. In our experiments the improvements reach up to 48% in terms of average precision.

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Cited By

Han SHe D(2018)Network-Based Social SearchSocial Information Access10.1007/978-3-319-90092-6_8(277-309)Online publication date: 3-May-2018
https://doi.org/10.1007/978-3-319-90092-6_8
Brusilovsky PSmyth BShapira B(2018)Social SearchSocial Information Access10.1007/978-3-319-90092-6_7(213-276)Online publication date: 3-May-2018
https://doi.org/10.1007/978-3-319-90092-6_7
Joachims TGranka LPan BHembrooke HGay G(2017)Accurately Interpreting Clickthrough Data as Implicit FeedbackACM SIGIR Forum10.1145/3130332.313033451:1(4-11)Online publication date: 2-Aug-2017
https://dl.acm.org/doi/10.1145/3130332.3130334
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Index Terms

A community-aware search engine
1. Human-centered computing
  1. Human computer interaction (HCI)
2. Information systems
  1. Information retrieval
    1. Retrieval models and ranking
  2. World Wide Web
    1. Web applications
    2. Web services

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Information & Contributors

Information

Published In

cover image ACM Conferences

WWW '04: Proceedings of the 13th international conference on World Wide Web

May 2004

754 pages

ISBN:158113844X

DOI:10.1145/988672

Conference Chairs:
Stuart Feldman
IBM Research
,
Mike Uretsky
New York University
,
Program Chairs:
Marc Najork
Microsoft Research
,
Craig Wills
Worcester Polytechnic Institute

Copyright © 2004 ACM.

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Publication History

Published: 17 May 2004

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WWW04: The 2004 World Wide Web Conference ( in conjunction with ACM Conference on Electronic Commerce [EC'04])

May 17 - 20, 2004

NY, New York, USA

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

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Cited By

Han SHe D(2018)Network-Based Social SearchSocial Information Access10.1007/978-3-319-90092-6_8(277-309)Online publication date: 3-May-2018
https://doi.org/10.1007/978-3-319-90092-6_8
Brusilovsky PSmyth BShapira B(2018)Social SearchSocial Information Access10.1007/978-3-319-90092-6_7(213-276)Online publication date: 3-May-2018
https://doi.org/10.1007/978-3-319-90092-6_7
Joachims TGranka LPan BHembrooke HGay G(2017)Accurately Interpreting Clickthrough Data as Implicit FeedbackACM SIGIR Forum10.1145/3130332.313033451:1(4-11)Online publication date: 2-Aug-2017
https://dl.acm.org/doi/10.1145/3130332.3130334
Xie HLi XWang TLau RWong TChen LWang FLi Q(2016)Incorporating sentiment into tag-based user profiles and resource profiles for personalized search in folksonomyInformation Processing and Management: an International Journal10.1016/j.ipm.2015.03.00152:1(61-72)Online publication date: 1-Jan-2016
https://dl.acm.org/doi/10.1016/j.ipm.2015.03.001
Shafiq OAlhajj RRokne J(2015)On personalizing Web search using social network analysisInformation Sciences: an International Journal10.1016/j.ins.2015.02.029314:C(55-76)Online publication date: 1-Sep-2015
https://dl.acm.org/doi/10.1016/j.ins.2015.02.029
Yan JChu WWhite RGeva STrotman ABruza PClarke CJärvelin K(2014)Cohort modeling for enhanced personalized searchProceedings of the 37th international ACM SIGIR conference on Research & development in information retrieval10.1145/2600428.2609617(505-514)Online publication date: 3-Jul-2014
https://dl.acm.org/doi/10.1145/2600428.2609617
Xie HLi QMao XLi XCai YRao Y(2014)2014 Special IssueNeural Networks10.1016/j.neunet.2014.05.00958(111-121)Online publication date: 1-Oct-2014
https://dl.acm.org/doi/10.1016/j.neunet.2014.05.009
White RChu WHassan AHe XSong YWang HSchwabe DAlmeida VGlaser HBaeza-Yates RMoon S(2013)Enhancing personalized search by mining and modeling task behaviorProceedings of the 22nd international conference on World Wide Web10.1145/2488388.2488511(1411-1420)Online publication date: 13-May-2013
https://dl.acm.org/doi/10.1145/2488388.2488511
Liu XTurtle H(2013)Real‐time user interest modeling for real‐time rankingJournal of the American Society for Information Science and Technology10.1002/asi.2286264:8(1557-1576)Online publication date: 13-Jun-2013
https://doi.org/10.1002/asi.22862
Peng XNiu ZHuang SZhao Y(2012)Personalized Web Search Using Clickthrough Data and Web Page RatingJournal of Computers10.4304/jcp.7.10.2578-25847:10Online publication date: 1-Oct-2012
https://doi.org/10.4304/jcp.7.10.2578-2584
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