Abstract
In this paper, we propose an algorithm to detect real world events from the click-through data. Our approach differs from the existing work as we: (i) consider the click-through data as collaborative query sessions instead of mere web logs proposed by many others (ii) integrate the semantics, structure, and content of queries and pages, and (iii) aim to achieve the overall objective via query clustering. The problem of event detection is transformed into query clustering by generating clusters using hybrid cover graphs where each hybrid cover graph corresponds to a real-world event. The evolutionary pattern for the co-occurrence of query-page pairs in a hybrid cover graph is imposed over a moving window period. Finally, we experimentally evaluated our proposed approach using a commercial search engine’s data collected over 3 months with about 20 million web queries and page clicks from 650,000 users. Our method outperforms the most recent event detection work proposed using complex methods in terms of metrics such as number of events detected, F-measures, entropy, recall etc.
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Angajala, P.K., Madria, S.K., Linderman, M. (2012). ECO: Event Detection from Click-through Data via Query Clustering. In: Meersman, R., et al. On the Move to Meaningful Internet Systems: OTM 2012. OTM 2012. Lecture Notes in Computer Science, vol 7565. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33606-5_20
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DOI: https://doi.org/10.1007/978-3-642-33606-5_20
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