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Expertise finding in bibliographic network: Topic dominance learning approach

Neshati, M ; Sharif University of Technology

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  1. Type of Document: Article
  2. DOI: 10.1109/TCYB.2014.2312614
  3. Abstract:
  4. Expert finding problem in bibliographic networks has received increased interest in recent years. This problem concerns 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 two discriminative methods for realizing 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. We recognize three feature groups that can discriminate relevant experts from other authors of a document. Experimental results on a real dataset, and a synthetic one that is gathered from a Microsoft academic search engine, show that the proposed model significantly improves the performance of expert finding in terms of all common information retrieval evaluation metrics
  5. Keywords:
  6. DBLP ; pointwise learning ; Bibliographies ; Expert finding ; Learning to rank ; Pairwise learning ; Point wise ; Search engines
  7. Source: IEEE Transactions on Cybernetics ; Vol. 44, issue. 12 , 2014 , pp. 2646-2657 ; ISSN: 21682267
  8. URL: http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6837494