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A survey on expert finding techniques

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Abstract

Finding experts in specified areas is an important task and has attracted much attention in the information retrieval community. Research on this topic has made significant progress in the past few decades and various techniques have been proposed. In this survey, we review the state-of-the-art methods in expert finding and summarize these methods into different categories based on their underlying algorithms and models. We also introduce the most widely used data collection for evaluating expert finding systems, and discuss future research directions.

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Notes

  1. http://trec.nist.gov/

  2. http://vcresearch.berkeley.edu/faculty-expertise

  3. http://www.5501e.com

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Acknowledgments

This work is partially supported by the National Natural Science Foundation of China (No.61303081 and No. 91646116) and by Scientific and Technological Support Project (Society) of Jiangsu Province (No. BE2016776).

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Authors and Affiliations

  1. Department of Automation, Shanghai Jiao Tong University, Shanghai, China

    Shuyi Lin

  2. Key Laboratory of System Control and Information Processing, Ministry of Education, Shanghai Jiao Tong University, Shanghai, China

    Shuyi Lin

  3. Department of Automation, Xiamen University, Xiamen, China

    Wenxing Hong

  4. Department of CEECS, Florida Atlantic University, Boca Raton, FL, USA

    Dingding Wang

  5. School of Computer Science, Florida International University, Miami, FL, USA

    Tao Li

  6. School of Computer Science, Nanjing University of Posts and Telecommunications, Nanjing, China

    Tao Li

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  1. Shuyi Lin
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Correspondence to Tao Li.

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Lin, S., Hong, W., Wang, D. et al. A survey on expert finding techniques. J Intell Inf Syst 49, 255–279 (2017). https://doi.org/10.1007/s10844-016-0440-5

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