Abstract
A website community refers to a set of websites that concentrate on the same or similar topics. There are two major challenges in website community mining task. First, the websites in the same topic may not have direct links among them because of competition concerns. Second, one website may contain information about several topics. Accordingly, the website community mining method should be able to capture such phenomena and assigns such website into different communities. In this paper, we propose a method to automatically mine website communities by exploiting the query log data in Web search. Query log data can be regarded as a comprehensive summarization of the real Web. The queries that result in a particular website clicked can be regarded as the summarization of that website content. The websites in the same topic are indirectly connected by the queries that convey information need in this topic. This observation can help us overcome the first challenge. The proposed two-phase method can tackle the second challenge. In the first phase, we cluster the queries of the same host to obtain different content aspects of the host. In the second phase, we further cluster the obtained content aspects from different hosts. Because of the two-phase clustering, one host may appear in more than one website communities.
The work described in this paper is also supported by grants from the Research Grant Council of the Hong Kong Special Administrative Region, China (Project Code: CUHK413510).
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Bing, L., Lam, W., Jameel, S., Lu, C. (2014). Website Community Mining from Query Logs with Two-Phase Clustering. In: Gelbukh, A. (eds) Computational Linguistics and Intelligent Text Processing. CICLing 2014. Lecture Notes in Computer Science, vol 8404. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-54903-8_17
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DOI: https://doi.org/10.1007/978-3-642-54903-8_17
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