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Leverage RAF to find domain experts on research social network services: A big data analytics methodology with MapReduce framework

Author

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  • Sun, Jianshan
  • Xu, Wei
  • Ma, Jian
  • Sun, Jiasen

Abstract

With the rapid proliferation of information technology, the increasing amount of information available has posted significant challenges on relevant information discovery for users. An alternative way is to find an expert with specific expertise. Expert recommendation is important in variety of contexts ranging from industry to academia. Information retrieval methods or graph-based methods have been proposed to approach this problem in previous research while some important contextual factors are ignored. In this paper, considering the factors of topic relevance, expert quality, and researcher connectivity, we propose a novel researcher modeling approach to recommend experts in scientific communities. The proposed recommendation method is well evaluated and compared with some commonly used recommendation models. Furthermore, the proposed method has been implemented in ScholarMate (www.scholarmate.com), an online research social network platform. The experimental results exhibit that the proposed method is more effective than baseline methods, and it is a potential recommendation method to find domain experts on research social network services.

Suggested Citation

  • Sun, Jianshan & Xu, Wei & Ma, Jian & Sun, Jiasen, 2015. "Leverage RAF to find domain experts on research social network services: A big data analytics methodology with MapReduce framework," International Journal of Production Economics, Elsevier, vol. 165(C), pages 185-193.
    • Handle: RePEc:eee:proeco:v:165:y:2015:i:c:p:185-193
    DOI: 10.1016/j.ijpe.2014.12.038
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    References listed on IDEAS

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    1. Heather Piwowar, 2013. "Value all research products," Nature, Nature, vol. 493(7431), pages 159-159, January.
    2. Katja Hofmann & Krisztian Balog & Toine Bogers & Maarten de Rijke, 2010. "Contextual factors for finding similar experts," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 61(5), pages 994-1014, May.
    3. Katja Hofmann & Krisztian Balog & Toine Bogers & Maarten de Rijke, 2010. "Contextual factors for finding similar experts," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 61(5), pages 994-1014, May.
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    Cited by:

    1. Kozjek, Dominik & Vrabič, Rok & Eržen, Gregor & Butala, Peter, 2018. "Identifying the business and social networks in the domain of production by merging the data from heterogeneous internet sources," International Journal of Production Economics, Elsevier, vol. 200(C), pages 181-191.
    2. Wei Du & Xusen Cheng & Chen Yang & Jianshan Sun & Jian Ma, 2017. "Establishing interoperability among knowledge organization systems for research management: a social network approach," Scientometrics, Springer;Akadémiai Kiadó, vol. 112(3), pages 1489-1506, September.
    3. Chung, Jaemin & Ko, Namuk & Kim, Hyeonsu & Yoon, Janghyeok, 2021. "Inventor profile mining approach for prospective human resource scouting," Journal of Informetrics, Elsevier, vol. 15(1).

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