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Expert finding in community question answering: a review

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Abstract

The rapid development of Community Question Answering (CQA) satisfies users’ quest for professional and personal knowledge about anything. In CQA, one central issue is to find users with expertise and willingness to answer the given questions. Expert finding in CQA often exhibits very different challenges compared to traditional methods. The new features of CQA (such as huge volume, sparse data and crowdsourcing) violate fundamental assumptions of traditional recommendation systems. This paper focuses on reviewing and categorizing the current progress on expert finding in CQA. We classify the recent solutions into four different categories: matrix factorization based models (MF-based models), gradient boosting tree based models (GBT-based models), deep learning based models (DL-based models) and ranking based models (R-based models). We find that MF-based models outperform other categories of models in the crowdsourcing situation. Moreover, we use innovative diagrams to clarify several important concepts of ensemble learning, and find that ensemble models with several specific single models can further boost the performance. Further, we compare the performance of different models on different types of matching tasks, including textvs.text, graphvs.text, audiovs.text and videovs.text. The results will help the model selection of expert finding in practice. Finally, we explore some potential future issues in expert finding research in CQA.

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Notes

  1. https://www.quora.com/What-percentage-of-questions-on-Quora-have-no-answers.

  2. https://biendata.com/competition/bytecup2016/.

  3. http://answers.google.com.

  4. https://www.quora.com/.

  5. https://answers.yahoo.com/.

  6. https://stackoverflow.com/.

  7. http://www.answers.com/.

  8. https://www.zhihu.com/.

  9. https://www.wukong.com/.

  10. https://zhidao.baidu.com/.

  11. https://wenwen.sogou.com/.

  12. More details of experiment results will be clarified in Sect. 10.

  13. https://grouplens.org/datasets/movielens/1m/.

  14. https://biendata.com/competition/luckydata/.

  15. https://www.kaggle.com/c/data-science-bowl-2017.

  16. https://www.kaggle.com/c/mlsp-2013-birds.

  17. https://www.kaggle.com/c/youtube8m.

  18. http://ms-multimedia-challenge.com/2016/challenge.

  19. Its accuracy is larger than 0.5.

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Acknowledgements

This work is supported by the NSFC for Distinguished Young Scholar (61825602), National Natural Science Foundation of China (61806111), and the National High Technology Research and Development Program of China (863 Program) (2015AA124102).

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Authors and Affiliations

  1. Knowledge Engineering Lab, Department of Computer Science and Technology, Tsinghua University, Beijing, China

    Sha Yuan & Jie Tang

  2. Institute of Medical Information, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, China

    Yu Zhang

  3. Computer Science Department, University of Valencia, Valencia, Spain

    Juan Bautista Cabotà

  4. Electronics and Computer Science, University of Southampton, Southampton, United Kingdom

    Wendy Hall

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  1. Sha Yuan
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  2. Yu Zhang
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  3. Jie Tang
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Correspondence to Jie Tang.

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Yuan, S., Zhang, Y., Tang, J. et al. Expert finding in community question answering: a review. Artif Intell Rev 53, 843–874 (2020). https://doi.org/10.1007/s10462-018-09680-6

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