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Convolutional neural networks for expert recommendation in community question answering


Community Question Answering (CQA) is becoming an increasingly important web service for people to search for expertise and to share their own. With lots of questions being solved, CQA have built a massive, freely accessible knowledge repository, which can provide valuable information for the broader society rather than just satisfy the question askers. It is critically important for CQA services to get high quality answers in order to maximize the benefit of this process. However, people are considered as experts only in their own specialized areas. This paper is concerned with the problem of expert recommendation for a newly posed question, which will reduce the questioner’s waiting time and improve the quality of the answer, so as to improve the satisfaction of the whole community. We propose an approach based on convolutional neural networks (CNN) to resolve this issue. Experimental analysis over a large real-world dataset from Stack Overflow demonstrates that our approach achieves a significant improvement over several baseline methods.

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This work was supported by National Natural Science Foundation of China (Grant Nos. 61572098, 61632011, 61562080), National Key Research Development Program of China (Grant No. 2016YF-B1001103), and Major Projects of Science and Technology Innovation in Liaoning Province (Grant No. 20151060-21).

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Correspondence to Jian Wang or Hongfei Lin.

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Wang, J., Sun, J., Lin, H. et al. Convolutional neural networks for expert recommendation in community question answering. Sci. China Inf. Sci. 60, 110102 (2017).

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  • community question answering
  • expert recommendation
  • convolutional neural networks
  • classification-based method
  • expert modeling