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TagClus: a random walk-based method for tag clustering

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Abstract

Tagging behavior on the Internet has seen dramatic increase in recent years, and social tagging has become a popular way to organize and share resources. However, ambiguity and large quantities of tags restrict its effective use for resource searching and classifying. Tag clustering can group tags with similar semantics together, thus helping alleviate these problems. In this paper, we introduce a random walk-based method to measure relevance between tags by exploiting the relationship between tags and resources. Based on this, we also develop a novel clustering method, TagClus, which can address several challenges in tag clustering. Experimental results on a real dataset show that our methods achieve good accuracy and acceptable performance for tag clustering.

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Authors and Affiliations

  1. Key Labs of Data Engineering and Knowledge Engineering, Ministry of Education, Beijing, China

    Jianwei Cui, Jun He, Pei Li, Xiaoyong Du & Puwei Wang

  2. School of Information, Renmin University of China, 100872, Beijing, China

    Jianwei Cui, Jun He, Pei Li, Xiaoyong Du & Puwei Wang

  3. School of Economics and Management, Tsinghua University, 100084, Beijing, China

    Hongyan Liu

Authors
  1. Jianwei Cui
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  2. Hongyan Liu
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  3. Jun He
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  4. Pei Li
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  5. Xiaoyong Du
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  6. Puwei Wang
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Correspondence to Hongyan Liu or Jun He.

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Cui, J., Liu, H., He, J. et al. TagClus: a random walk-based method for tag clustering. Knowl Inf Syst 27, 193–225 (2011). https://doi.org/10.1007/s10115-010-0307-y

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  • DOI: https://doi.org/10.1007/s10115-010-0307-y

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