? A Survey on Expert Recommendation in Community Question Answering
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Journal of Computer Science and Technology 2018, Vol. 33 Issue (4) :625-653    DOI: 10.1007/s11390-018-1845-0
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A Survey on Expert Recommendation in Community Question Answering
Xianzhi Wang1, Member, ACM, IEEE, Chaoran Huang2, Student Member, ACM, IEEE Lina Yao2, Member, ACM, IEEE, Boualem Benatallah2, Member, IEEE, Manqing Dong2, Student Member, ACM, IEEE
1 School of Software, University of Technology Sydney, Sydney, NSW 2007, Australia;
2 School of Computer Science and Engineering, University of New South Wales, Sydney, NSW 2052, Australia

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Abstract Community question answering (CQA) represents the type of Web applications where people can exchange knowledge via asking and answering questions. One significant challenge of most real-world CQA systems is the lack of effective matching between questions and the potential good answerers, which adversely affects the efficient knowledge acquisition and circulation. On the one hand, a requester might experience many low-quality answers without receiving a quality response in a brief time; on the other hand, an answerer might face numerous new questions without being able to identify the questions of interest quickly. Under this situation, expert recommendation emerges as a promising technique to address the above issues. Instead of passively waiting for users to browse and find their questions of interest, an expert recommendation method raises the attention of users to the appropriate questions actively and promptly. The past few years have witnessed considerable efforts that address the expert recommendation problem from different perspectives. These methods all have their issues that need to be resolved before the advantages of expert recommendation can be fully embraced. In this survey, we first present an overview of the research efforts and state-of-the-art techniques for the expert recommendation in CQA. We next summarize and compare the existing methods concerning their advantages and shortcomings, followed by discussing the open issues and future research directions.
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Keywordscommunity question answering   expert recommendation   challenge   solution   future direction     
Received 2018-01-21;
About author: Xianzhi Wang is a lecturer with School of Software, University of Technology Sydney, Sydney. He received his B.E. degree from Xi'an Jiaotong University, Xi'an, M.E. and Ph.D. degrees from Harbin Institute of Technology, Harbin, all in computer science, in 2007, 2009, and 2014 respectively. His research interests include Internet of Things, data management, machine learning, and services computing. He received ARC Discovery Early Career Researcher Award (DECRA) in 2017 and IBM Ph.D. Fellowship Award in 2013.
Cite this article:   
Xianzhi Wang, Chaoran Huang, Lina Yao, Boualem Benatallah, Manqing Dong.A Survey on Expert Recommendation in Community Question Answering[J]  Journal of Computer Science and Technology, 2018,V33(4): 625-653
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