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In this paper, we present a complete framework and findings in mining web usage patterns from Web log files of a real website that has all the challenging aspects of real life web usage mining, including evolving user profiles and external data describing an ontology of the web content. Even though the website under study is part of a non-profit organization that does not "sell" any products, it was crucial to understand "who" the users were, "what" they looked at, and "how their interests changed with time", all of which are important questions in Customer Relationship Management (CRM). Hence, we present an approach to discover and track evolving user profiles. We also describe how to enrich the discovered user profiles with explicit information need that is inferred from search queries extracted from Web log data. Profiles are also enriched with other domain specific information facets that give a panoramic view of the discovered mass usage modes. An objective validation strategy is also used to assess the quality of the mined profiles, and in particular, their adaptability in the face of evolving user behavior.
User profiles and alert services, Data mining, Web mining, Personalization
Esin Saka, Richard Germain, Olfa Nasraoui, Antonio Badia, Maha Soliman, "A Web Usage Mining Framework for Mining Evolving User Profiles in Dynamic Web Sites", IEEE Transactions on Knowledge & Data Engineering, vol. 20, no. , pp. 202-215, February 2008, doi:10.1109/TKDE.2007.190667
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