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Poster
Track: Posters
Paper Title:
Using Subspace Analysis for Event Detection from Web Click-through Data
Authors:
- Ling Chen(L3S Research Center)
- Yiqun Hu(Nanyang Technological University)
- Wolfgang Nejdl(L3S Research Center)
Abstract:
Although most of existing research usually detects events by
analyzing the content or structural information of Web documents,
a recent direction is to study the usage data. In this paper, we
focus on detecting events from Web textit{click-through data}
generated by Web search engines. We propose a novel approach which
effectively detects events from click-through data based on robust
subspace analysis. We first transform click-through data to the
$2D$ polar space. Next, an algorithm based on Generalized
Principal Component Analysis (GPCA) is used to estimate subspaces
of transformed data such that each subspace contains query
sessions of similar topics. Then, we prune uninteresting subspaces
which do not contain query sessions corresponding to real events
by considering both the semantic certainty and the temporal
certainty of query sessions in each subspace. Finally, various
events are detected from interesting subspaces by utilizing a
nonparametric clustering technique. Compared with existing
approaches, our experimental results based on real-life
click-through data have shown that the proposed approach is more
accurate in detecting real events and more effective in
determining the number of events.
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