Expertise retrieval in bibliographic network: A topic dominance learning approach

Seyyed Hadi Hashemi, Mahmood Neshati, Hamid Beigy

Research output: Chapter in Book/Report/Conference proceedingConference contribution

18 Citations (Scopus)

Abstract

Expert finding in bibliographic networks has received increased interests in recent years. This task concerns with finding relevant researchers for a given topic. Motivated by the observation that rarely do all coauthors contribute to a paper equally, in this paper, we propose a discriminative method to realize leading authors contributing in a scientific publication. Specifically, we cast the problem of expert finding in a bibliographic network to find leading experts in a research group, which is easier to solve. According to some observations, we recognize three feature groups that can discriminate relevant and irrelevant experts. Experimental results on a real dataset, and an automatically generated one that is gathered from Microsoft academic search show that the proposed model significantly improves the performance of expert finding in terms of all common Information Retrieval evaluation metrics. Copyright is held by the owner/author(s).

Original languageEnglish
Title of host publicationInternational Conference on Information and Knowledge Management, Proceedings
Pages1117-1126
Number of pages10
DOIs
Publication statusPublished - 11 Dec 2013
Event22nd ACM International Conference on Information and Knowledge Management, CIKM 2013 - San Francisco, CA, United States
Duration: 27 Oct 20131 Nov 2013

Other

Other22nd ACM International Conference on Information and Knowledge Management, CIKM 2013
CountryUnited States
CitySan Francisco, CA
Period27/10/131/11/13

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Keywords

  • Discriminative models
  • Expertise retrieval
  • Learning to rank

ASJC Scopus subject areas

  • Business, Management and Accounting(all)
  • Decision Sciences(all)

Cite this

Hashemi, S. H., Neshati, M., & Beigy, H. (2013). Expertise retrieval in bibliographic network: A topic dominance learning approach. In International Conference on Information and Knowledge Management, Proceedings (pp. 1117-1126) https://doi.org/10.1145/2505515.2505697