Journal of Guangxi Normal University(Natural Science Edition) ›› 2018, Vol. 36 ›› Issue (4): 51-58.doi: 10.16088/j.issn.1001-6600.2018.04.007

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Domain Experts Recommendation in Social Network Basedon the LDA Theme Model of Trust

LIU Dianting1*, WU Lina2   

  1. 1. College of Mechanical and Control Engineering,Guilin University of Technology,Guilin Guangxi 541004,China;
    2. College of Information Science and Engineering,Guilin University of Technology,Guilin Guangxi 541004,China
  • Received:2017-06-26 Published:2018-10-20

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Abstract: With the development of Web 2.0 technology, the social network has provided a new and convenient platform to communicate and collaborate for people. Faced with the problem of network information overloaded, it is very difficult to find the experts who are interested in the topic and are reliable to make decisions based on experts’ opinions. In this paper, domain experts recommendation in social network based on the LDA theme model of trust method is proposed. The personalized recommendation of domain experts for the user trust is implemented. The method is based on the topic model (LDA), the social network structure, the users’ trust relationship and social influence. It makes up the deficiency of the traditional experts recommendation methods that only involve expert’s characteristics which lead to low recommendation accuracy and the problem of the pattern of recommended results. Finally, the feasibility and effectiveness of the method are verified by experiments.

Key words: social network, information overload, theme model, personalized recommendation, user trust

CLC Number: 

  • TP393.4
[1] 李江,李东,冯培桦,等.基于专长吻合度、学术影响力与社会关联值的专家推荐模型研究[J].情报学报,2017, 36(4):338-345.DOI:10.3772/j.issn.1000-0135.2017.04.002.
[2] 瞿辉,王菲菲.基于知识关联网络的作者学术影响力评价研究[J].情报杂志,2016,35(7):190-195. D0I:10.3969/j.issn.1002-1965.2016.07.032.
[3] XIE Xiaoqin,LI Yijia,ZHANG Zhiqiang,et al.A topic-specific contextual expert finding method in social network[C]//LI Feifei,SHIM K,ZHANG Kai,et al.Web Technologies and Applications:Lecture Notes in Computer Science vol 9931.Cham,Switzerland:Springer International Publishing AG,2016:292-303.DOI: 10.1007/978-3-319-45814-4_24.
[4] LIN Shuyi,HONG Wenxing,WANG Dingding,et al.A survey on expert finding techniques[J].Journal of Intelligent Information Systems,2017,49(2):255-279.DOI:10.1007/s10844-016-0440-5.
[5] 龚凯乐,成颖.基于“问题-用户”的网络问答社区专家发现方法研究[J].图书情报工作, 2016, 60(24):115-121. DOI:10.13266/j.issn.0252-3116.2016.24.016.
[6] YENITERZI R,CALLAN J.Constructing effective and efficient topic-specific authority networks for expert finding in social media[C]//Proceedings of the first international workshop on Social media retrieval and analysis.New York:ACM Press,2014:45-50.DOI:10.1145/2632188.2632208.
[7] 杨文显.项目评审专家协同推荐方法的研究及应用[D].杭州:杭州电子科技大学,2016.
[8] 王书海.协同过滤推荐算法研究及MapReduce实现[D].成都:四川师范大学,2016.
[9] SHROT T,ROSENFELD A,GOLBECK J, et al.Crisp: an interruption management algorithm based on collaborative filtering[C]//Proceedings of the SIGCHI Conference on Human Factors in Computing Systems. New York:ACM Press,2014:3035-3044.DOI:10.1145/2556288.2557109.
[10] 翟伯荫.社交网络中领域专家的识别研究[D].上海:华东师范大学,2015.
[11] 杜静.社会网络中基于信任的推荐方法设计与实现[D].哈尔滨:黑龙江大学,2015.
[12] DING Junmei,CHEN Yan,LI Xin,et al.Unsupervised expert finding in social network for personalized recommendation[C]//CUI Bin,ZHANG Nan,XU Jianliang,et al.Web-Age Information Management:Lecture Notes in Computer Science vol 9658.Cham,Switzerland:Springer International Publishing AG,2016:257-271.DOI: 10.1007/978-3-319-39937-9_20.
[13] VERGNE M,SUSI A.Expert finding using Markov networks in open source communities[C]//JARKE M, MYLOPOULOS J,QUIX C,et al.Advanced Information Systems Engineering:Lecture Notes in Computer Science vol 8484.Cham,Switzerland:Springer International Publishing AG,2014:196-210.DOI:10.1007/978-3-319-07881-6_14.
[14] 朱丽中,徐秀娟,刘宇.基于项目和信任的协同过滤推荐算法[J].计算机工程,2013,39(1):58-62,66.DOI:10. 3969/j.issn.1000-3428.2013.01.012.
[15] 汪俊,岳峰,王刚,等.科研社交网络中基于链接预测的专家推荐研究[J].情报杂志,2015,34(6):151-157. DOI:10.3969/j.issn.1002-1965.2015.06.027.
[16] LIU Jian,JIA Bei,XU Hao,et al.A topic rank based document priors model for expert finding[C]//FEI Minrui,MA Shiwei,LI Xin,et al.Advanced Computational Methods in Life System Modeling and Simulation: Communications in Computer and Information Science vol 761.Singapore:Springer,2017:334-341.DOI: 10.1007/978-981-10-6370-1_33.
[17] 姜文君.在线社会网络中个性化信任评价基础与应用研究[D].长沙:中南大学,2014.
[18] DONG Yuxin,ZHAO Chunhui,CHENG Weijie,et al.A personalized recommendation algorithm with user trust in social network[C]//CHE Wanxiang,HAN Qilong,WANG Hongzhi,et al.Social Computing:Proceedings of Second International Conference of Young Computer Scientists,Engineers and Educators.Singapore: Springer,2016:63-76.DOI: 10.1007/978-981-10-2053-7_7.
[19] LI Yanyan,MA Shaoqian,HUANG Ronghuai.Social context analysis for topic-specific expert finding in online learning communities[M]//CHANG Maiga,LI Yanyan.Smart Learning Environments:Lecture Notes in Educational Technology.Berlin:Springer,2015:57-74.DOI:10.1007/978-3-662-44447-4_4.
[20] 邸亮,杜永萍.LDA模型在微博用户推荐中的应用[J].计算机工程,2014,40(5):1-6,11.DOI:10.3969/j.issn. 1000-3428.2014.05.001.
[21] 仇丽青,陈卓艳,丁长青,等.基于改进LDA主题模型的社会网络话题发现算法iMLDA[J].情报科学,2016,34(9): 115-118,133.DOI:10.13833/j.cnki.is.2016.09.023.
[22] ZHANG Zhijun,XU Gongwen,ZHANG Pengfei,et al.Personalized recommendation algorithm for social networks based on comprehensive trust[J].Applied Intelligence,2017,47(3):659-669.DOI:10.1007/s10489-017-0928-x.
[23] 李德新,钟俊.一种改进用户相似度的协同过滤推荐算法[J].数字技术与应用,2017(2):158-159.DOI:10.3969/j.issn.1007-9416.2017.02.099.
[24] 关鹏,王曰芬,傅柱.不同语料下基于LDA主题模型的科学文献主题抽取效果分析[J].图书情报工作, 2016,60(2):112-121.DOI:10.13266/j.issn.0252-3116.2016.02.018.
[25] 孙志滨.LDA模型的研究及其在推荐系统中的应用[D].杭州:浙江大学,2016.
[26] 李湛.基于社会信任网络的协同过滤推荐方法研究[D].大连:大连理工大学,2013.
[27] 郭文健,高仲合,段婷婷.基于信任网络的个性化推荐算法[J].电子技术,2016(12):65-67.DOI:10.3969/j.issn.1000-0755.2016.12.020.
[28] 靳健,杨海慈,李凝,等.基于主题契合度的专家推荐模型研究[J].数字图书馆论坛,2017(4):47-55.DOI:10.3772/j.issn.1673-2286.2017.04.007.
[29] 赵千,耿骞,靳健,等.一种面向主题覆盖度与权威度的评审专家推荐模型研究[J].图书情报工作,2017,61(1): 80-88.DOI:10.13266/j.issn.0252-3116.2017.01.010.
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