中国邮电高校学报(英文) ›› 2013, Vol. 20 ›› Issue (1): 122-128.doi: 10.1016/S1005-8885(13)60018-7

• Others • 上一篇    

BPR-UserRec: a personalized user recommendation method in social tagging systems

杨谈1,崔毅东2,金跃辉3   

  1. State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing 100876, China
  • 收稿日期:2012-09-18 修回日期:2012-12-10 出版日期:2013-02-28 发布日期:2013-02-28
  • 通讯作者: 杨谈 E-mail:atomoto@gmail.com
  • 基金资助:

    This work was supported by the National Basic Research Program of China (2009CB320505), the Hi-Tech Research and Development Program of China (20011AA 01A102), the Nuclear High-Based Project of China (2012ZX01039004-008), the National Nature Science Foundation of China (61002011), and the Electronic Information Industry Development Fund Program ‘The Development and Industrialization of Key Supporting Software in Cloud Computing (Cloud Storage Service)’

BPR-UserRec: a personalized user recommendation method in social tagging systems

  1. State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing 100876, China
  • Received:2012-09-18 Revised:2012-12-10 Online:2013-02-28 Published:2013-02-28
  • Supported by:

    This work was supported by the National Basic Research Program of China (2009CB320505), the Hi-Tech Research and Development Program of China (20011AA 01A102), the Nuclear High-Based Project of China (2012ZX01039004-008), the National Nature Science Foundation of China (61002011), and the Electronic Information Industry Development Fund Program ‘The Development and Industrialization of Key Supporting Software in Cloud Computing (Cloud Storage Service)’

摘要:

Social tagging is one of the most important characteristics of Web 2.0 services, and social tagging systems (STS) are becoming more and more popular for users to annotate, organize and share items on the Web. Moreover, online social network has been incorporated into social tagging systems. As more and more users tend to interact with real friends on the Web, personalized user recommendation service provided in social tagging systems is very appealing. In this paper, we propose a personalized user recommendation method, and our method handles not only the users’ interest networks, but also the social network information. We empirically show that our method outperforms a state-of-the-art method on real dataset from Last.fm dataset and Douban.

关键词:

social tagging systems, user recommendation, tensor factorization

Abstract:

Social tagging is one of the most important characteristics of Web 2.0 services, and social tagging systems (STS) are becoming more and more popular for users to annotate, organize and share items on the Web. Moreover, online social network has been incorporated into social tagging systems. As more and more users tend to interact with real friends on the Web, personalized user recommendation service provided in social tagging systems is very appealing. In this paper, we propose a personalized user recommendation method, and our method handles not only the users’ interest networks, but also the social network information. We empirically show that our method outperforms a state-of-the-art method on real dataset from Last.fm dataset and Douban.

Key words:

social tagging systems, user recommendation, tensor factorization