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Track: Posters

Paper Title:
Using Subspace Analysis for Event Detection from Web Click-through Data


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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