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Hierarchical Bayesian Models for Collaborative Tagging Systems

Hierarchical Bayesian Models for Collaborative Tagging Systems

Published: 2009 Dezember

Buchtitel: Ninth IEEE International Conference on Data Mining (ICDM 09)
Seiten: 728-733
Verlag: IEEE Computer Society

Referierte Veröffentlichung


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.

ISSN: 1550-4786
Download: Media:Bundschuss icdm09 HBMfCTS.pdf
DOI Link: 10.1109/ICDM.2009.121