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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.

The introduction of the paper is well presented. The state of the art section is well done, indicating the recent research in the area and following chronological order. In the presentation of the methodology the authors begin by describing the notation used to represent the model of social tagging system, as well as the status of co-occurrences between tags (co-occur for the same resource tags; for a single user, or for a same user-feature combination). The authors used examples to explain this part. In analyzing the results, the authors used two more geared metrics for clustering (Silhouette coefficient and Dunn index), according to the authors, rather than precision and recall. The results were compared with other four approaches adopted in state of the art. The algorithm was implemented in Matlab, and based on the metric previously proposed. The results obtained are satisfactory.

基于共同共现群体相似度的社会化标签聚类方法

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