In the past few years, there has been increased research interest in detecting previously unidentified events from Web resources. Our focus in this paper is to detect events from the click-through data generated by Web search engines. Existing event detection algorithms, which mainly study the news archive data, cannot be employed directly because of the following two unique features of click-through data: 1) the information provided by click-through data is quite limited; 2) not every query issued to a Web search engine corresponds to an event in the real world. In this paper, we address this problem by proposing an effective algorithm which Detects Events from ClicK-through data DECK. We firstly transform click-through data to the 2D polar space by considering the semantic dimension and temporal dimension of queries. Robust subspace estimation is performed to detect subspaces such that each subspace consists of queries of similar semantics. Next, we prune uninteresting subspaces which do not contain queries corresponding to real events by simultaneously considering the respective distribution of queries along the semantic dimension and the temporal dimension in each subspace. Finally, events are detected from interesting subspaces using a nonparametric clustering technique. Compared with an existing approach, our experimental results based on real-life data have shown that the proposed approach is more accurate and effective in detecting real events from click-through data.
['Ling Chen', 'Yiqun Hu', 'Wolfgang Nejdl']
DECK: Detecting Events from Web Click-Through Data