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Exploit the tripartite network of social tagging for web clustering
Conference proceeding

Exploit the tripartite network of social tagging for web clustering

Caimei Lu, Xin Chen, E Park and Xiaoli Chen
Proceedings of the 18th ACM conference on information and knowledge management, pp 1545-1548
02 Nov 2009
DOI: https://doi.org/10.1145/1645953.1646167

Abstract

folksonomy social tagging tag clustering tripartite network
In this poster, we investigate how to enhance web clustering by leveraging the tripartite network of social tagging systems. We propose a clustering method, called "Tripartite Clustering", which cluster the three types of nodes (resources, users and tags) simultaneously based on the links in the social tagging network. The proposed method is experimented on a real-world social tagging dataset sampled from del.icio.us. We also compare the proposed clustering approach with K-means. All the clustering results are evaluated against a human-maintained web directory. The experimental results show that Tripartite Clustering significantly outperforms the content-based K-means approach and achieves performance close to that of social annotation-based K-means whereas generating much more useful information.

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32 citations in Scopus

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