›› 2009, Vol. 24 ›› Issue (6): 1028-1035.

• Special Section on International Partnership Programs Supported by CAS • Previous Articles     Next Articles

Exploring Social Annotations with the Application to Web Page Recommendation

Hui-Qian Li1 (李慧倩), Fen Xia1(夏粉), Daniel Zeng1,2 (曾大军), Senior Member, IEEE, Fei-Yue Wang1 (王飞跃), Senior Member, CCF, Fellow, IEEE, and Wen-Ji Mao1 (毛文吉), Senior Member, CCF, Member, ACM   

  1. 1Key Laboratory of Complex Systems and Intelligence Science, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
    2Department of Management Information Systems, University of Arizona, Tucson AZ 85721, U.S.A.
  • Received:2009-06-09 Revised:2009-07-17 Online:2009-11-05 Published:2009-11-05
  • About author:
    Hui-Qian Li is a research scientist in the Key Lab of Complex Systems and Intelligent Science at Institute of Automation, Chinese Academy of Sciences. Her research interests include complex networks, recommendation systems, statistical machine learning.
    Fen Xia is an assistant professor in the Key Lab of Complex Systems and Intelligent Science at Institute of Automation, Chinese Academy of Sciences. His research interests include statistical machine learning, ranking, regularization methods, efficient algorithms, and information retrieval.
    Daniel Zeng received his Ph.D. degree in industrial administration from Carnegie Mellon University in 1998. He is a research professor at the Institute of Automation, Chinese Academy of Sciences. He is also affiliated with the University of Arizona as an associate professor and the director of the Intelligent Systems and Decisions Laboratory. Prof. Zeng is a senior member of IEEE. His research interests include software agents and multi-agent systems, intelligence and security informatics, social computing and recommender systems.
    Fei-Yue Wang received his Ph.D. degree in computer and systems engineering from Rensselaer Polytechnic Institute in 1990. He is the director of the Key Laboratory of Complex Systems and Intelligence Science at the Chinese Academy of Sciences. He is also a professor in the University of Arizona's Systems & Industrial Engineering Department and the director of the university's Program in Advanced Research of Complex Systems. Prof. Wang is a fellow of IEEE, INCOSE, IFAC, ASME, and AAAS. His current research interests include social computing, Web and services science, modeling, analysis and control of complex systems, especially social and cyber-physical systems.
    Wen-Ji Mao received her Ph.D. degree in computer science from the University of Southern California in 2006. She is an associate professor at the Institute of Automation, Chinese Academy of Sciences. Prof. Mao is a member of ACM and AAAI, and a senior member of the China Computer Federation. Her research interests include artificial intelligence, multi-agent systems and social modeling.
    Fen Xia is an assistant professor in the Key Lab of Complex System and Intelligent Science at Institute of Automation China Academy of Sciences. He received his BSC degree in Automation at the University of Science and Technology of China (USTC) in 2003 and PhD degree from the Institute of Automation, China Academy of Sciences in 2008. His research interests include statistical machine learning, ranking, regularization methods, efficient algorithms, and information retrieval.
  • Supported by:

    This work is supported in part by the National Natural Science Foundation of China under Grant Nos. 60621001, 60875028, 60875049, and 70890084, the Chinese Ministry of Science and Technology under Grant No. 2006AA010106, and the Chinese Academy of Sciences under Grant Nos. 2F05N01, 2F08N03 and 2F07C01.

Collaborative social annotation systems allow users to record and share their original keywords or tag attachments to Web resources such as Web pages, photos, or videos. These annotations are a method for organizing and labeling information. They have the potential to help users navigate the Web and locate the needed resources. However, since annotations are posted by users under no central control, there exist problems such as spam and synonymous annotations. To efficiently use annotation information to facilitate knowledge discovery from the Web, it is advantageous if we organize social annotations from semantic perspective and embed them into algorithms for knowledge discovery. This inspires the Web page recommendation with annotations, in which users and Web pages are clustered so that semantically similar items can be related. In this paper we propose four graphic models which cluster users, Web pages and annotations and recommend Web pages for given users by assigning items to the right cluster first. The algorithms are then compared to the classical collaborative filtering recommendation method on a real-world data set. Our result indicates that the graphic models provide better recommendation performance and are robust to fit for the real applications.

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