期刊文献+

SNS网站用户关系挖掘的设计与实现 被引量:6

Design and Implementation of User Relationship Mining in Social Networking Services Website
下载PDF
导出
摘要 针对目前网络社交网站存在的交友形式单薄、好友关系淡化、用户流失等问题,设计基于"寝室"组织形式的社交网站系统,嵌入用户挖掘模块,通过权值更新和潜在关系更新算法帮助用户寻找潜在好友,以达到拓展社交圈的目的。实验结果表明,该系统的好友推荐准确度较高。 With the wide application of Internet and computer technology, online social networking service becomes one of the best means to keep contacts with old friends and to make new friends. However, there are still many shortages in Social Networking Services(SNS), such as user relationship deterioration and user loss. In order to solve these problems, this paper proposes a SNS website system based on "dormitory". A module of user relationship mining is embedded to help users to get their potential friends in advance by weight and potential relationship updating. Experimental results show that this system achieves a good recommending accuracy.
出处 《计算机工程》 CAS CSCD 北大核心 2011年第3期61-63,共3页 Computer Engineering
关键词 社交网络服务 六度分隔 小世界效应 用户关系挖掘 Social Networking Services(SNS) six degrees of separation small world phenomenon user relationship mining
  • 相关文献

参考文献5

  • 1Kumar R,Novak J,Tomkins A.Structure and Evolution of Online Social Networks[C]//Proc.of International Conference on Knowledge Discovery and Data Mining.Philadelphia,USA:[s.n.],2006.
  • 2Boyd D M,Ellison N B.Social Network Sites:Definition,History,and Scholarship[J].Journal of Computer-Mediated Communication,2007,13(1):210-230.
  • 3Mislove A,Krislma M M,Gummadi P,et al.Measurement and Analysis of Online Social NetwoAs[C]//Proc.of Intemet Measurement Conference.San Diego,California,USA:[s.n.],2007:29-42.
  • 4Aim Y Y,Han S,Kwak H,et al.Analysis of Topological Characteristics of Huge Online Social Networking Services[C]//Proc.of International World Wide Web Conference.AIbcrta,Canada:[s.n.],2007:835-845.
  • 5金晶,陈清华,罗恒.改进的E-learning社区自组织算法[J].计算机工程,2008,34(19):266-268. 被引量:1

二级参考文献6

  • 1Belkin N J, Croft W B. Information Filtering and Information Retrieval: Two Sides of the Same Coin[J]. Communications of the ACM, 1992, 35(2): 29-38.
  • 2Sarwar B, Karypis G; Konstan J, et al. Item-based Collaborative Filtering Recommendation Algorithms[C]//Proceedings of the 10th International Conference on World Wide Web. [S.l.]: IEEE Press, 2001: 285-295.
  • 3Yang Fan, Shen Ruimin, Han Peng. A Dynamic Self-organizing E-learner[C]//Proc. of the 16th Australian Joint Conference on Artificial Intelligence. Perth, Australia: [s. n.], 2003: 590-600.
  • 4Hebb D O. The Organization of Behavior[M]. New York, USA: Wiley, 1949.
  • 5Cohen E. Search and Replication in Unstructured Peer-toPpeer Networks[C]//Proc. of the 16th International Conference on Super Computing. New York, USA: ACM Press, 2002: 84-95.
  • 6Xu Linhao, Dai Chenyun, Cai Wenyuan, et al. Towards Adaptive Probabilistic Search in Unstructured P2P Systems[C]//Proc. of the 6th Asia Pacific Web Conference. Hangzhou, China: [s. n.], 2004: 258-268.

同被引文献93

引证文献6

二级引证文献34

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部