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A social tag clustering method based on common co-occurrence group similarity

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Abstract

Social tagging systems are widely applied in Web 2.0. Many users use these systems to create, organize, manage, and share Internet resources freely. However, many ambiguous and uncontrolled tags produced by social tagging systems not only worsen users’ experience, but also restrict resources’ retrieval efficiency. Tag clustering can aggregate tags with similar semantics together, and help mitigate the above problems. In this paper, we first present a common co-occurrence group similarity based approach, which employs the ternary relation among users, resources, and tags to measure the semantic relevance between tags. Then we propose a spectral clustering method to address the high dimensionality and sparsity of the annotating data. Finally, experimental results show that the proposed method is useful and efficient.

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Authors and Affiliations

  1. School of Computer and Information, Hefei University of Technology, Hefei, 230009, China

    Hui-zong Li, Xue-gang Hu, Wei He & Jian-han Pan

  2. School of Economics and Management, Anhui University of Science and Technology, Huainan, 232001, China

    Hui-zong Li

  3. School of Computer, Minnan Normal University, Zhangzhou, 363000, China

    Yao-jin Lin

Authors
  1. Hui-zong Li
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  2. Xue-gang Hu
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  3. Yao-jin Lin
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  4. Wei He
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  5. Jian-han Pan
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Correspondence to Hui-zong Li.

Additional information

Project supported by the National Natural Science Foundation of China (Nos. 61273292, 61303131, 51474007, and 51374114) and the MOE Humanities and Social Science Research on Youth Foundation of China (No. 13YJCZH077)

ORCID: Hui-zong LI, http://orcid.org/0000-0002-1459-989X

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Li, Hz., Hu, Xg., Lin, Yj. et al. A social tag clustering method based on common co-occurrence group similarity. Frontiers Inf Technol Electronic Eng 17, 122–134 (2016). https://doi.org/10.1631/FITEE.1500187

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