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Voting techniques for expert search

Authors:

Craig Macdonald,

Iadh OunisAuthors Info & Claims

Knowledge and Information Systems, Volume 16, Issue 3

Pages 259 - 280

Published: 01 September 2008 Publication History

Abstract

In an expert search task, the users' need is to identify people who have relevant expertise to a topic of interest. An expert search system predicts and ranks the expertise of a set of candidate persons with respect to the users' query. In this paper, we propose a novel approach for predicting and ranking candidate expertise with respect to a query, called the Voting Model for Expert Search. In the Voting Model, we see the problem of ranking experts as a voting problem. We model the voting problem using 12 various voting techniques, which are inspired from the data fusion field. We investigate the effectiveness of the Voting Model and the associated voting techniques across a range of document weighting models, in the context of the TREC 2005 and TREC 2006 Enterprise tracks. The evaluation results show that the voting paradigm is very effective, without using any query or collection-specific heuristics. Moreover, we show that improving the quality of the underlying document representation can significantly improve the retrieval performance of the voting techniques on an expert search task. In particular, we demonstrate that applying field-based weighting models improves the ranking of candidates. Finally, we demonstrate that the relative performance of the voting techniques for the proposed approach is stable on a given task regardless of the used weighting models, suggesting that some of the proposed voting techniques will always perform better than other voting techniques.

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Cited By

Liu DChen YKao WWang H(2019)Integrating expert profile, reputation and link analysis for expert finding in question-answering websitesInformation Processing and Management: an International Journal10.1016/j.ipm.2012.07.00249:1(312-329)Online publication date: 22-Nov-2019
https://dl.acm.org/doi/10.1016/j.ipm.2012.07.002
Moreira CCalado PMartins B(2018)Learning to rank academic experts in the DBLP datasetExpert Systems: The Journal of Knowledge Engineering10.1111/exsy.1206232:4(477-493)Online publication date: 12-Dec-2018
https://dl.acm.org/doi/10.1111/exsy.12062
Jafari Navimipour N(2018)A formal approach for the specification and verification of a Trustworthy Human Resource Discovery mechanism in the Expert CloudExpert Systems with Applications: An International Journal10.1016/j.eswa.2015.03.03542:15(6112-6131)Online publication date: 29-Dec-2018
https://dl.acm.org/doi/10.1016/j.eswa.2015.03.035
Show More Cited By

Index Terms

Voting techniques for expert search
1. Computing methodologies
  1. Artificial intelligence
    1. Search methodologies
      1. Heuristic function construction
2. Information systems
  1. Information retrieval
    1. Evaluation of retrieval results
  2. World Wide Web
    1. Web applications
    2. Web services

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cover image Knowledge and Information Systems

Knowledge and Information Systems Volume 16, Issue 3

September 2008

130 pages

ISSN:0219-1377

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Copyright © Copyright © 2008 Springer-Verlag London Limited.

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Springer-Verlag

Berlin, Heidelberg

Publication History

Published: 01 September 2008

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Cited By

Liu DChen YKao WWang H(2019)Integrating expert profile, reputation and link analysis for expert finding in question-answering websitesInformation Processing and Management: an International Journal10.1016/j.ipm.2012.07.00249:1(312-329)Online publication date: 22-Nov-2019
https://dl.acm.org/doi/10.1016/j.ipm.2012.07.002
Moreira CCalado PMartins B(2018)Learning to rank academic experts in the DBLP datasetExpert Systems: The Journal of Knowledge Engineering10.1111/exsy.1206232:4(477-493)Online publication date: 12-Dec-2018
https://dl.acm.org/doi/10.1111/exsy.12062
Jafari Navimipour N(2018)A formal approach for the specification and verification of a Trustworthy Human Resource Discovery mechanism in the Expert CloudExpert Systems with Applications: An International Journal10.1016/j.eswa.2015.03.03542:15(6112-6131)Online publication date: 29-Dec-2018
https://dl.acm.org/doi/10.1016/j.eswa.2015.03.035
Neshati MHiemstra DAsgari EBeigy H(2018)Integration of scientific and social networksWorld Wide Web10.1007/s11280-013-0229-117:5(1051-1079)Online publication date: 25-Dec-2018
https://dl.acm.org/doi/10.1007/s11280-013-0229-1
Ma DChen YDu XHao Y(2018)Interpreting Fine-Grained Categories from Natural Language Queries of Entity SearchDatabase Systems for Advanced Applications10.1007/978-3-319-91452-7_55(861-877)Online publication date: 21-May-2018
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Lin W(2017)On image search result aggregationPattern Analysis & Applications10.1007/s10044-017-0596-920:3(865-870)Online publication date: 1-Aug-2017
https://dl.acm.org/doi/10.1007/s10044-017-0596-9
Xu WSun JMa JDu W(2016)A personalized information recommendation system for R&D project opportunity finding in big data contextsJournal of Network and Computer Applications10.1016/j.jnca.2015.01.00359:C(362-369)Online publication date: 1-Jan-2016
https://dl.acm.org/doi/10.1016/j.jnca.2015.01.003
Said Lhadj LBoughanem MAmrouche K(2016)Enhancing information retrieval through concept-based language modeling and semantic smoothingJournal of the Association for Information Science and Technology10.1002/asi.2355367:12(2909-2927)Online publication date: 1-Dec-2016
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Hammache ABoughanem MAhmed-Ouamer R(2014)Combining compound and single terms under language model frameworkKnowledge and Information Systems10.1007/s10115-013-0618-x39:2(329-349)Online publication date: 1-May-2014
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Albaham ASalim NCulpepper SZuccon GSitbon L(2013)Quality biased thread retrieval using the voting modelProceedings of the 18th Australasian Document Computing Symposium10.1145/2537734.2537752(97-100)Online publication date: 5-Dec-2013
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