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A model for expert finding in social networks

Published: 24 July 2011 Publication History

Abstract

Expert finding is a task of finding knowledgeable people on a given topic. State-of-the-art expertise retrieval algorithms identify matching experts based on analysis of textual content of documents experts are associated with. While powerful, these models ignore social structure that might be available. In this paper, we develop a Bayesian hierarchical model for expert finding that accounts for both social relationships and content. The model assumes that social links are determined by expertise similarity between candidates. We demonstrate the improved retrieval performance of our model over the baseline on a realistic data set.

References

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K. Balog, T. Bogers, L. Azzopardi, M. de Rijke, and A. van den Bosch. Broad expertise retrieval in sparse data environments. In SIGIR '07, pages 551--558. ACM, 2007.
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J. Chang and D. Blei. Hierarchical relational models for document networks. Annals of Applied Statistics, 4 (1): 124--150, 2010.
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D. Horowitz and S. D. Kamvar. The anatomy of a large-scale social search engine. In WWW '10, pages 431--440. ACM, 2010.
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M. Karimzadehgan, R. W. White, and M. Richardson. Enhancing expert finding using organizational hierarchies. In ECIR'09, pages 177--188, 2009.
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Cited By

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  • (2021)Intern retrieval from community question answering websites: A new variation of expert finding problemExpert Systems with Applications10.1016/j.eswa.2021.115044181(115044)Online publication date: Nov-2021
  • (2019)Misinformation-oriented expert finding in social networksWorld Wide Web10.1007/s11280-019-00717-623:2(693-714)Online publication date: 23-Aug-2019
  • (2019)Crowdsourcing High-Quality Structured DataInformation Management and Big Data10.1007/978-3-030-11680-4_29(304-319)Online publication date: 8-Feb-2019
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    Published In

    cover image ACM Conferences
    SIGIR '11: Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
    July 2011
    1374 pages
    ISBN:9781450307574
    DOI:10.1145/2009916

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    Association for Computing Machinery

    New York, NY, United States

    Publication History

    Published: 24 July 2011

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    Author Tags

    1. expert finding
    2. social network
    3. topic model

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    Overall Acceptance Rate 792 of 3,983 submissions, 20%

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    View all
    • (2021)Intern retrieval from community question answering websites: A new variation of expert finding problemExpert Systems with Applications10.1016/j.eswa.2021.115044181(115044)Online publication date: Nov-2021
    • (2019)Misinformation-oriented expert finding in social networksWorld Wide Web10.1007/s11280-019-00717-623:2(693-714)Online publication date: 23-Aug-2019
    • (2019)Crowdsourcing High-Quality Structured DataInformation Management and Big Data10.1007/978-3-030-11680-4_29(304-319)Online publication date: 8-Feb-2019
    • (2018)Analysis and Prediction of Endorsement-Based Skill Assessment in LinkedIn2018 IEEE 42nd Annual Computer Software and Applications Conference (COMPSAC)10.1109/COMPSAC.2018.00071(461-470)Online publication date: Jul-2018
    • (2017)PINProceedings of the 14th EAI International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services10.1145/3144457.3144458(186-195)Online publication date: 7-Nov-2017
    • (2017)Expert finding by the Dempster‐Shafer theory for evidence combinationExpert Systems10.1111/exsy.1223135:1Online publication date: 19-Oct-2017
    • (2016)Learning to Find Topic Experts in Twitter via Different RelationsIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2016.253916628:7(1764-1778)Online publication date: 1-Jul-2016
    • (2016)A survey on participant recruitment in crowdsensing systems2016 6th International Conference on Computer and Knowledge Engineering (ICCKE)10.1109/ICCKE.2016.7802154(286-291)Online publication date: Oct-2016
    • (2013)Friend recommendation based on the Luscher color theory: Twitter use case2013 IEEE 11th Malaysia International Conference on Communications (MICC)10.1109/MICC.2013.6805828(218-221)Online publication date: Nov-2013
    • (2013)A Trust-Based Recruitment Framework for Multi-hop Social Participatory SensingProceedings of the 2013 IEEE International Conference on Distributed Computing in Sensor Systems10.1109/DCOSS.2013.29(266-273)Online publication date: 20-May-2013
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