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Ranking user authority with relevant knowledge categories for expert finding

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Abstract

The problem of expert finding targets on identifying experts with special skills or knowledge for some particular knowledge categories, i.e. knowledge domains, by ranking user authority. In recent years, this problem has become increasingly important with the popularity of knowledge sharing social networks. While many previous studies have examined authority ranking for expert finding, they have a focus on leveraging only the information in the target category for expert finding. It is not clear how to exploit the information in the relevant categories of a target category for improving the quality of authority ranking. To that end, in this paper, we propose an expert finding framework based on the authority information in the target category as well as the relevant categories. Along this line, we develop a scalable method for measuring the relevancies between categories through topic models, which takes consideration of both content and user interaction based category similarities. Also, we provide a topical link analysis approach, which is multiple-category-sensitive, for ranking user authority by considering the information in both the target category and the relevant categories. Finally, in terms of validation, we evaluate the proposed expert finding framework in two large-scale real-world data sets collected from two major commercial Question Answering (Q&A) web sites. The results show that the proposed method outperforms the baseline methods with a significant margin.

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Authors and Affiliations

  1. School of Computer Science and Technology, University of Science and Technology of China, Hefei, Anhui, 230026, China

    Hengshu Zhu & Enhong Chen

  2. Nokia, Beijing, 100010, China

    Huanhuan Cao & Jilei Tian

  3. Management Science and Information Systems Department, Rutgers Business School, Rutgers University, Newark, NJ, 07102, USA

    Hui Xiong

Authors
  1. Hengshu Zhu
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  2. Enhong Chen
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  3. Hui Xiong
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  4. Huanhuan Cao
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  5. Jilei Tian
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Corresponding author

Correspondence to Enhong Chen.

Additional information

This is a substantially extended and revised version of [35], which appears in Proceedings of the 20th ACM Conference on Information and Knowledge Management (CIKM2011).

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Zhu, H., Chen, E., Xiong, H. et al. Ranking user authority with relevant knowledge categories for expert finding. World Wide Web 17, 1081–1107 (2014). https://doi.org/10.1007/s11280-013-0217-5

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  • DOI: https://doi.org/10.1007/s11280-013-0217-5

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