? 社区问答中的专家推荐研究综述
Journal of Computer Science and Technology
Quick Search in JCST
 Advanced Search 
      Home | PrePrint | SiteMap | Contact Us | Help
 
Indexed by   SCIE, EI ...
Bimonthly    Since 1986
Journal of Computer Science and Technology 2018, Vol. 33 Issue (4) :625-653    DOI: 10.1007/s11390-018-1845-0
Special Issue on Software Engineering for High-Confidence Systems << Previous Articles | Next Articles >>
社区问答中的专家推荐研究综述
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
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

摘要
参考文献
相关文章
Download: [PDF 472KB]  
摘要 社区问答是一种以提问和回答问题的方式支持人们知识交流的网络应用。现实中的多数社区问答系统面临一个重要难题,即:缺乏对于问题与优秀回答者的有效匹配。这一缺乏严重影响了问答系统中知识的高效获取与传播:一方面,提问者可能收到很多答案但却无法在短时间内从中找到高质量的答案;另一方面,回答者可能看到很多问题却无法很快定位到自己想要回答的问题。专家推荐是解决以上问题的一种有效技术。不同于被动地等待人们浏览和回答问题,专家推荐方法主动并即时提醒用户那些适合他们回答的问题。虽然近年来一些学者从不同角度对专家推荐方法展开了研究,然而现有方法都存在其各自的问题使得专家推荐的优势无法充分发挥。本文首先回顾了社区问答领域对专家推荐方法的现有研究,随后对已有方法进行总结并比较它们的优缺点,最后指出仍待解决的难题及有价值的研究方向。
关键词社区问答   专家推荐   挑战   解决方案   未来方向     
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.
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.
引用本文:   
Xianzhi Wang, Chaoran Huang, Lina Yao, Boualem Benatallah, Manqing Dong.社区问答中的专家推荐研究综述[J]  Journal of Computer Science and Technology , 2018,V33(4): 625-653
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
链接本文:  
http://jcst.ict.ac.cn:8080/jcst/CN/10.1007/s11390-018-1845-0
Copyright 2010 by Journal of Computer Science and Technology