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Long Beach, CA, USA
Mar. 1, 2010 to Mar. 6, 2010
ISBN: 978-1-4244-5445-7
pp: 780-783
Junjie Yao , Department of Computer Science and Technology, Key Laboratory of High Confidence Software Technologies (Ministry of Education), Peking University, China
Bin Cui , Department of Computer Science and Technology, Key Laboratory of High Confidence Software Technologies (Ministry of Education), Peking University, China
Yuxin Huang , Department of Computer Science and Technology, Key Laboratory of High Confidence Software Technologies (Ministry of Education), Peking University, China
Yanhong Zhou , Department of Computer Science and Technology, Key Laboratory of High Confidence Software Technologies (Ministry of Education), Peking University, China
ABSTRACT
Collaborative tagging systems have emerged as an ubiquitous way to annotate and organize online resources. The users' tagging actions over time reflect the changing of their interests. In this paper, we propose to detect bursty tagging event, which captures the relations among a group of correlated tags where the tags are either bursty or associated with bursty tag co-occurrence. We exploit the sliding time intervals to extract bursty features from large tag corpora as the first step, and then adopt graph clustering techniques to group bursty features into meaningful bursty events. An experimental study demonstrates the superiority of our approach.
CITATION
Junjie Yao, Bin Cui, Yuxin Huang, Yanhong Zhou, "Detecting bursty events in collaborative tagging systems", ICDE, 2010, 2013 IEEE 29th International Conference on Data Engineering (ICDE), 2013 IEEE 29th International Conference on Data Engineering (ICDE) 2010, pp. 780-783, doi:10.1109/ICDE.2010.5447922
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