Convolutional neural networks for expert recommendation in community question answering

作     者：Jian WANG Jiqing SUN Hongfei LIN Hualei DONG Shaowu ZHANG 

作者机构：School of Computer Science and Technology Dalian University of Technology 

出 版 物：《Science China(Information Sciences)》 (中国科学:信息科学(英文版))

年 卷 期：2017年第60卷第11期

页      面：19-27页

核心收录：

学科分类：12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 081104[工学-模式识别与智能系统] 081203[工学-计算机应用技术] 08[工学] 0835[工学-软件工程] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：supported by National Natural Science Foundation of China (Grant Nos. 61572098, 61632011, 61562080) National Key Research Development Program of China (Grant No. 2016YFB1001103) Major Projects of Science and Technology Innovation in Liaoning Province (Grant No. 2015106021) 

主　　题：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.