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论文类型：期刊论文

发表时间：2017-11-01

发表刊物：SCIENCE CHINA-INFORMATION SCIENCES

收录刊物：SCIE、EI、Scopus

卷号：60

期号：11

ISSN号：1674-733X

关键字：community question answering; expert recommendation; convolutional neural networks; classification-based method; expert modeling

摘要：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.