收稿日期: 2016-09-19
修回日期: 2016-12-21
网络出版日期: 2017-01-05
基金资助
本文系国家社会科学基金重大项目“基于多维度聚合的网络资源知识发现研究”（项目编号：13&ZD183）和国家社会科学基金项目“基于关联数据的数字图书馆多粒度集成知识服务研究”（项目编号：14CTQ003）研究成果之一。
Friend Recommendation Based on Strength of Relationships and Interests
Received date: 2016-09-19
Revised date: 2016-12-21
Online published: 2017-01-05
夏立新 , 李重阳 , 王忠义 . 融合关系强度和兴趣的好友推荐方法研究[J]. 图书情报工作, 2017 , 61(1) : 64 -71 . DOI: 10.13266/j.issn.0252-3116.2017.01.008
[Purpose/significance] Aiming at improving the efficiency of friend recommendation algorithm,this paper expands the connecting relations between social network users based on the theory of three-degree influence.[Method/process] Firstly, the strength of friend relationships between users could be calculated, which would be used to filter out user set. Secondly, this paper calculates the similarity of interests based on the content of common concern of users. Thirdly, it achieved to recommend friends to social network users by fusing the strength of relationships and the similarity of interests.[Result/conclusion] The experiment results on douban data show that the proposed method is a better recommendation method. It can be helpful for the friend recommendation and complements the theory of social recommendation.
[1] CNNIC.2015年中国社交应用用户行为研究报告.[EB/OL].[2016-05-31].http://www.cnnic.net.cn/hlwfzyj/hlwxzbg/sqbg/201604/P020160408334860042447.pdf.
[2] 克里斯塔基斯.大连接[M].简学,译.北京:中国人民大学出版社, 2013:37-44.
[3] LI W, HE W. An improved collaborative filtering approach based on user ranking and item clustering[C]//PATHAN M, WEI G Y, FORTINO G. Internet and distributed computing systems. Berlin:Springer, 2013:134-144.
[4] THOMAS R, LEON M, STEFANO D A, et al. Improving change recommendation using aggregated association rules[C]//KIM M, ROBBES R, BIRD C. Proceedings of the 13th international conference on mining software repositories. New York:ACM, 2016:73-84.
[5] CASTRO J, RORRIGUEZ R M, BARRANCO M J. Weighting of features in content-based filtering with entropy and dependence measures[J]. International journal of computational intelligence systems, 2014, 7(1):80-89.
[6] LEI K, ZHANG K, XIANG Y C, et al. A hybrid recommendation model based on the label propagation and VSM clustering[C]//YONG J M, XI B. Proceedings of the 7th international conference on computer science & education. Piscataway:IEEE, 2012:911-915.
[7] 唐晓波, 祝黎, 谢力. 基于主题的微博二级好友推荐模型研究[J]. 图书情报工作, 2014, 58(9):105-113.
[8] WU Z, HUANG I, ZHENG X, et al. A test-bed for facebook friend-list recommendations[C]//CHOUNTA I A. Proceedings of the 7th ACM SIGCHI Symposium on Engineering Interactive Computing Systems. New York:ACM, 2015:222-225.
[9] WU B X, XIAO J. Potential friend recommendation based on user tagging[J]. Journal of computer applications, 2015, 35(6):1663-1667.
[10] ZHANG Z, LIU Y, DING W, et al. Proposing a new friend recommendation method, FRUTAI, to enhance social media providers' performance[J]. Decision support systems, 2015, 79(C):46-54.
[11] YU Z, WANG C, BU J, et al. Friend recommendation with content spread enhancement in social networks[J]. Information sciences, 2015, 309(10):102-118.
[12] HUANG S, ZHAN J, WANG L, et al. Social friend recommendation based on multiple network correlation[J]. IEEE transactions on multimedia, 2016, 18(2):287-299.
[13] 施少怀. 一种基于用户倾向的微博好友推荐算法[D]. 哈尔滨:哈尔滨工业大学, 2012.
[14] 黄鹤. 关于我国婚恋网站网络交友的研究[D]. 武汉:华中师范大学, 2011.
[15] NARUCHITPARAMES J, GUNES M H, LOUIS S J. Friend recommendations in social networks using genetic algorithms and network topology[C]//CORTEZ P. Proceedings of the IEEE congress on evolutionary computation. Piscataway:IEEE, 2011:2207-2214.
[16] JERE W A, GEORG S, GRANOVETTER M. Triadic closure[M]. Toronto:Equ Press, 2012.
[17] 项亮. 推荐系统实践[M]. 北京:人民邮电出版社, 2012.
[18] 吴昊, 刘东苏. 社交网络中的好友推荐方法研究[J]. 现代图书情报技术, 2015(1):59-65.
[19] 唐晓波, 房小可. 基于隐含狄利克雷分配的微博推荐模型研究[J]. 情报科学, 2015(2):3-8.
[20] 叶春蕾, 邢燕丽. 基于LDA和社会网络中心度的研究生个性化检索推荐模型研究[J]. 图书情报工作, 2015,59(13):104-110.
[21] 胡吉明, 陈果. 基于动态LDA主题模型的内容主题挖掘与演化[J]. 图书情报工作, 2014, 58(2):138-142.
[22] XU C Y, LI D. Friend recommendation method based on micro-blogs and network structural information[J]. Computer engineering and applications, 2016, 52(1):55-60.
[23] MISLOVE A, MARCON M, GUMMADI K P, et al. Measurement and analysis of online social networks[C]//KONSTANTINA P, ZHANG Z L. Proceedings of the 7th ACM SIGCOMM Conference on Internet Measurement. New York:ACM, 2007:1815-1816.
[24] CHEN J L, GSYER W, DUGAN C, et al. Make new friends, but keep the old:recommending people on social networking sites[C]//COCKTON G, KORHONEN P. Proceedings of the SIGCHI conference on human factors in computing systems. New York:ACM, 2009:201-210.
[25] 王名扬, 贾冲冲, 杨东辉. 基于三度影响力的社交好友推荐机制[J]. 计算机应用, 2015, 35(7):1984-1987.
[26] 张霁. 论豆瓣网的小众聚合功能[D].成都:四川省社会科学院,2012.
[27] 199IT.豆瓣注册用户超7500万[EB/OL].[2016-06-13].http://www.199it.com/Archives/169873.html.
/
| 〈 |
|
〉 |
