基于用户-标签异构网络的社区问答专家发现方法

doi:10.19678/j.issn.1000-3428.0053734

摘要/Abstract

摘要： 在Stack Overflow、Quora等社区问答网站中，日益增长的用户数使新问题数量急剧增加，传统的专家发现方法通常根据历史回答记录建立用户文档，再从中提取用户文本特征，难以及时寻找到合适的专家进行回答。针对该问题，提出一种社区问答中基于用户-标签异构网络的专家发现方法。根据用户历史回答记录和问题的附带标签构建用户-标签网络，以此得到用户的向量表示。在此基础上，使用全连接神经网络提取用户特征和问题文本特征，通过比较两者的余弦相似度得到候选专家列表。基于StackExchange的真实世界数据集进行测试，实验结果表明，与LDA、STM、RankingSVM和QR-DSSM方法相比，该方法的MRR指标值较高，能够准确寻找到可提供正确答案的专家。

关键词: 社区问答, 专家发现, 问题路由, 深度学习, 网络嵌入

Abstract: In Community Question Answering(CQA) websites such as Stack Overflow and Quora,the growing number of users leads to a sharp increase in the number of new questions.Traditional expert discovery methods usually establish user documents based on historical answer records and extract user text features from them,making it difficult to find appropriate experts to answer questions in time.To address this problem,an expert discovery method based on user-tag heterogeneous network is proposed.This method builds a user-tag network based on the historical answer records of users and tags attached to the questions,so as to obtain the vector representation of users.On this basis,fully connected neural network is used to extract user features and text features of questions,and their cosine similarity is compared to obtain the list of candidate experts.Experimental results on the real world dataset of stackExchange show that compared with LDA,STM,RankingSVM and QR-DSSM methods,this method has a higher index value,and can accurately find experts that can provide correct answers.

Key words: Community Question Answering(CQA), expert discovery, question routing, deep learning, network embedding

中图分类号:

TP39

黄辉, 刘永坚, 解庆. 基于用户-标签异构网络的社区问答专家发现方法[J]. 计算机工程, 2020, 46(2): 53-58.

HUANG Hui, LIU Yongjian, XIE Qing. Expert Discovery Method Based on User-Tag Heterogeneous Network for Community Question Answering[J]. Computer Engineering, 2020, 46(2): 53-58.

http://www.ecice06.com/CN/Y2020/V46/I2/53

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参考文献

[1] LIN Hongfei,WANG Jian,XIONG Daping,et al.Category participation-based approach to find experts for community question answer services[J].Computer Engineering and Design,2014,35(1):333-338.(in Chinese) 林鸿飞,王健,熊大平,等.基于类别参与度的社区问答专家发现方法[J].计算机工程与设计,2014,35(1):333-338.
[2] LI Kelin.Discovering domain experts in online Q&A communities by combining user topics and behaviors[J].Computer and Modernization,2018(9):72-79.(in Chinese) 李科霖.一种融合话题和行为的在线问答社区领域专家发现方法[J].计算机与现代化,2018(9):72-79.
[3] RIAHI F,ZOLAKTAF Z,SHAFIEI M,et al. Finding expert users in community question answering[C]//Proceedings of the 21st International Conference on World Wide Web.New York,USA:ACM Press,2012:791-798.
[4] ROSEN-ZVI M,GRIFFITHS T,STEYVERS T,et al.The author-topic model for authors and documents[C]//Proceedings of the 20th Conference on Uncertainty in Artificial Intelligence.Banff,Canada:AUAI Press,2004:487-494.
[5] CHENG Xiang,ZHU Shuguang,CHEN Gang,et al.Exploiting user feedback for expert finding in community question answering[C]//Proceedings of 2015 IEEE International Conference on Data Mining Workshop.Washington D.C.,USA:IEEE Computer Society,2015:295-302.
[6] CHENG Xiang,ZHU Shuguang,SU Sen,et al.A multi-objective optimization approach for question routing in community question answering services[J].IEEE Transactions on Knowledge and Data Engineering,2017,29(9):1779-1792.
[7] ZHANG Junyang,WANG Huili,GUO Yang,et al.Review of deep learning[J].Application Research of Computers,2018,35(7):1921-1928.(in Chinese) 张军阳,王慧丽,郭阳,等.深度学习相关研究综述[J].计算机应用研究,2018,35(7):1921-1928.
[8] DENG Xuming.Research on tourism service recommendation system based on deep learning[D].Xi’an:Northwestern University,2018.(in Chinese) 邓旭明.基于深度学习的旅游服务推荐系统研究[D].西安:西北大学,2018.
[9] HUANG Liwei,JIANG Bitao,LÜ Shouye,et al.Survey on deep learning based recommender systems[J].Chinese Journal of Computers,2018,41(7):191-219.(in Chinese) 黄立威,江碧涛,吕守业,等.基于深度学习的推荐系统研究综述[J].计算机学报,2018,41(7):191-219.
[10] AZZAM A,TAZI N,HOSSNY A.A question routing technique using deep neural network for communities of question answering[C]//Proceedings of International Conference on Database Systems for Advanced Applications.Berlin,Germany:Springer,2017:35-49.
[11] HUANG Posen,HE Xiaodong,GAO Jianfeng,et al.Learning deep structured semantic models for Web search using clickthrough data[C]//Proceedings of the 22nd ACM International Conference on Information and Knowledge Management.New York,USA:ACM Press,2013:2333-2338.
[12] WANG Jian,SUN Jiqing,LIN Hongfei,et al.Convolutional neural networks for expert recommendation in community question answering[J].Science China (Information Sciences),2017,60(11):19-27.
[13] JI Zongcheng,WANG Bin.Learning to rank for question routing in community question answering[C]//Proceedings of the 22nd ACM International Conference on Information and Knowledge Management.New York,USA:ACM Press,2013:2363-2368.
[14] JURCZYK P,AGICHTEIN E.Discovering authorities in question answer communities by using link analysis[C]//Proceedings of the 16th ACM Conference on Information and Knowledge Management.New York,USA:ACM Press,2007:919-922.
[15] SHEN Jie,SHEN Wen,FAN Xin.Recommending experts in Q&A communities by weighted hits algorithm[C]//Proceedings of International Forum on Information Technology and Applications.Guangzhou,China:[s.n.],2009:151-154.
[16] ZHAO Zhou,YANG Qifan,CAI Deng,et al.Expert finding for community-based question answering via ranking metric network learning[C]//Proceedings of the 25th International Joint Conference on Artificial Intelligence.New York,USA:AAAI Press,2016:3000-3006.
[17] PEROZZI B,AL-RFOU R,SKIENA S.Deepwalk:online learning of social representations[C]//Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.New York,USA:ACM Press,2014:701-710.
[18] LIU Zhiqiang,ZHANG Yan.Structures or texts? a dynamic gating method for expert finding in CQA services[C]//Proceedings of Conference on Database Systems for Advanced Applications.Berlin,Germany:Springer,2018:201-208.
[19] SOCHER R,CHEN D,MANNING C D,et al.Reasoning with neural tensor networks for knowledge base completion[C]//Proceedings of NIPS’13.Berlin,Germany:Springer,2013:926-934.
[20] TANG Jian,QU Meng,WANG Mingzhe,et al.LINE:large-scale information network embedding[C]//Proceedings of the 24th International Conference on World Wide Web.[S.l.]:International World Wide Web Conferences Steering Committee,2015:1067-1077.

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