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ZhihuRank: A Topic-Sensitive Expert Finding Algorithm in Community Question Answering Websites

  • Xuebo Liu
  • Shuang Ye
  • Xin Li
  • Yonghao Luo
  • Yanghui RaoEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9412)

Abstract

Expert finding is important to the development of community question answering websites and e-learning. In this study, we propose a topic-sensitive probabilistic model to estimate the user authority ranking for each question, which is based on the link analysis technique and topical similarities between users and questions. Most of the existing approaches focus on the user relationship only. Compared to the existing approaches, our method is more effective because we consider the link structure and the topical similarity simultaneously. We use the real-world data set from Zhihu (a famous CQA website in China) to conduct experiments. Experimental results show that our algorithm outperforms other algorithms in the user authority ranking.

Keywords

Community question answering Expert finding PageRank Latent topic modeling 

Notes

Acknowledgements

The authors are thankful to the anonymous reviewers for their constructive comments and suggestions on an earlier version of this paper. The research described in this paper has been supported by the National Natural Science Foundation of China (Grant No. 61502545), and “the Fundamental Research Funds for the Central Universities” (Grant No. 46000-31121401).

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Xuebo Liu
    • 1
  • Shuang Ye
    • 1
  • Xin Li
    • 1
  • Yonghao Luo
    • 1
  • Yanghui Rao
    • 1
    Email author
  1. 1.School of Mobile Information EngineeringSun Yat-sen UniversityZhuhaiChina

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