[1]胡 云,张 舒,佘侃侃,等.基于重叠社区发现的社会网络推荐算法研究[J].南京师范大学学报(自然科学版),2018,41(03):35.[doi:10.3969/j.issn.1001-4616.2018.03.006]
　Hu Yun,Zhang Shu,She Kankan,et al.Research of Social Recommendation Method Based onOverlapping Community Detection[J].Journal of Nanjing Normal University(Natural Science Edition),2018,41(03):35.[doi:10.3969/j.issn.1001-4616.2018.03.006]
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基于重叠社区发现的社会网络推荐算法研究()
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《南京师范大学学报》（自然科学版）[ISSN:1001-4616/CN:32-1239/N]

卷:
第41卷
期数:
2018年03期
页码:
35
栏目:
·人工智能算法与应用专栏·
出版日期:
2018-09-30

文章信息/Info

Title:
Research of Social Recommendation Method Based onOverlapping Community Detection
文章编号:
1001-4616(2018)03-0035-07
作者:
胡 云1; 张 舒2; 佘侃侃1; 李 慧3; 4; 施 珺3
(1.南京中医药大学信息技术学院,江苏 南京 210023)(2.淮海工学院商学院,江苏 连云港 222005)(3.淮海工学院计算机工程学院,江苏 连云港 222005)(4.江苏省海洋资源开发研究院,江苏 连云港 222005)
Author(s):
Hu Yun1; Zhang Shu2; She Kankan1; Li Hui3; 4; Shi Jun3
(1.College of Information Technology,Nanjing University of Chinese Medicine,Nanjing 210023,China)(2.Business School,Huaihai Institute of Technology,Lianyungang 222005,China)(3.Department of Computer Science,Huaihai Institute of Technology,Lianyungang 222005,China)(4.Marine Resources Development Institute of Jiangsu,Lianyungang 222005,China)
关键词:
社会网络; 推荐; 重叠; 社区; 检测
Keywords:
social network; recommendation; overlapping; community; detection
分类号:
TP391
DOI:
10.3969/j.issn.1001-4616.2018.03.006
文献标志码:
A
摘要:
协同过滤算法已成为用来为用户提供个性化服务以处理海量信息最常用的方法之一. 本文提出一种基于重叠社区发现的社会网络推荐算法,该算法同时考虑了群组用户的兴趣以及他们复杂的内部关系,通过将重叠社区发现算法和基于模型的社会推荐算法进行创新融合,以实现重叠社区的发现、建立,和基于社区的智能推荐. 基于开放数据集,本文设计了一系列相关实验以验证算法的有效性和准确性. 实验结果表明本文提出的算法可以实现高效且准确的社会网络推荐.
Abstract:
Collaborative filtering algorithms have become one of the most popular approaches to provide personalized services for users to deal with large amounts of information. This paper proposes a new social recommendation methods based on overlapping community discovery. The algorithm considers both the interest of the group users and their complex internal relations. In order to achieve the detection,establishment coalition of overlapping community and intelligent recommendation based on community,it innovates and integrates overlapping community discovery algorithm and social recommendation algorithm based on the model. Based on the open data set,this paper designs a series of the related experiments to validate the accuracy and effectiveness of the algorithm. Experimental results show that the proposed algorithm can achieve highly efficient and accurate social network recommendation.

参考文献/References:

[1] ZHAO W X,LI S,CHANG E Y,et al. Connecting social media to e-commerce:cold-start product recommendation using microblogging information[J]. IEEE transactions on knowledge and data engineering,2016,28(5):1147-1159.
[2]HE C,LI H,FEI X,et al. A topic community-based method for friend recommendation in large-scale online social networks[J]. Concurrency and computation practice and experience,2017,29(6):1-10.
[3]HONG M,JUNG J J,PICCIALLI F,et al. Social recommendation service for cultural heritage[J]. Personal and ubiquitous computing,2017,21(2):191-201.
[4]LU Y,TSAPARAS P,NTOULAS A,et al. Exploiting Social Context for Review Quality Prediction[C]//www2010,ACM. New York,USA,2010:691-700.
[5]MA H,ZHOU D Y,LIU C,et al. Recommender Systems with Social Regularization[C]//WSDM,ACM. New York,USA,2011:287-295.
[6]LIU F,LEE H J. Use of social network information to enhance collaborative filtering performance[J]. Expert system application,2010,37(7):4772-4778.
[7]NOEL J,SANNER S,TRAN K N,et al. New objective functions for social collaborative filtering[C]//WWW2012. Lyon,France,2012:1-10.
[8]YANG S H,LONG B,SMOLA A J,et al. Like alike:joint friendship and interest propagation in social networks[C]//WWW2011,ACM. New York,USA,WWW,2011:537-546.
[9]XIAO R L,LI Y N,CHEN H T,et al. SRSP-PMF:a novel probabilistic matrix factorization recommendation algorithm using social reliable similarity propagation[J]. Lecture notes in computer science,2015:80-91.
[10]JI K,SUN R Y,LI X,et al. Improving matrix approximation for recommendation via a clustering-based reconstructive method[J]. Neurocomputing,2016(173):912-920.
[11]SALAKHUTDINOV R,MNIH A. Probabilistic matrix factorization[C]//Proc of the 21st Annual Conf on Neural Information Processing Systems. New York:Curran Associates Inc,2008:1257-1264.
[12]LEE D D,SEUNG H S. Learning the parts of objects by non-negative matrix factorization[J]. Nature,1999,401(6755):788-791.

备注/Memo

备注/Memo:
收稿日期:2018-04-16.
基金项目:国家自然科学基金项目(81674099)、国家重点研发计划项目(2017YFC1703501、2017YFC1703503、2017YFC1703506)、连云港市科技计划项目(JC1608)、江苏高校“青蓝工程”资助项目、连云港市“521工程”科研资助、淮海工学院自然基金项目(Z2017012、 Z2015012)、淮海工学院教学改革项目(XJG2017-2-5)、教育部协同育人项目(201702134005、201701028110).
通讯联系人:张舒,讲师,研究方向:智能信息处理. E-mail:shufanzs@126.com
更新日期/Last Update: 2018-11-19