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2009 Ninth IEEE International Conference on Data Mining (2009)
Miami, Florida
Dec. 6, 2009 to Dec. 9, 2009
ISSN: 1550-4786
ISBN: 978-0-7695-3895-2
pp: 728-733
Collaborative tagging systems with user generated content have become a fundamental element of websites such as Delicious, Flickr or CiteULike. By sharing common knowledge, massively linked semantic data sets are generated that provide new challenges for data mining. In this paper, we reduce the data complexity in these systems by finding meaningful topics that serve to group similar users and serve to recommend tags or resources to users. We propose a well-founded probabilistic approach that can model every aspect of a collaborative tagging system. By integrating both user information and tag information into the well-known Latent Dirichlet Allocation framework, the developed models can be used to solve a number of important information extraction and retrieval tasks.
collaborative tagging, LDA, user modeling

H. Kriegel, M. Dejori, M. Bundschus, V. Tresp, S. Yu and A. Rettinger, "Hierarchical Bayesian Models for Collaborative Tagging Systems," 2009 Ninth IEEE International Conference on Data Mining(ICDM), Miami, Florida, 2009, pp. 728-733.
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