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Exploring Social Annotations with the Application to Web Page Recommendation

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Abstract

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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Authors and Affiliations

  1. Key Laboratory of Complex Systems and Intelligence Science, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China

    Hui-Qian Li, Fen Xia, Daniel Zeng (Senior Member, IEEE), Fei-Yue Wang (Senior Member, CCF, Fellow, IEEE) & Wen-Ji Mao (Senior Member, CCF, Member, ACM)

  2. Department of Management Information Systems, University of Arizona, Tucson, AZ, 85721, U.S.A.

    Daniel Zeng (Senior Member, IEEE)

Authors
  1. Hui-Qian Li
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  2. Fen Xia
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  3. Daniel Zeng
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  4. Fei-Yue Wang
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  5. Wen-Ji Mao
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Corresponding author

Correspondence to Hui-Qian Li.

Additional information

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.

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Li, HQ., Xia, F., Zeng, D. et al. Exploring Social Annotations with the Application to Web Page Recommendation. J. Comput. Sci. Technol. 24, 1028–1034 (2009). https://doi.org/10.1007/s11390-009-9292-6

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  • DOI: https://doi.org/10.1007/s11390-009-9292-6

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