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The Web presents a key driving force for a large spectrum of applications in which a user interacts with a company, a governmental authority, a non-governmental organization or other non-profit institution or other users. User preferences and expectations, together with usage patterns, form the basis for personalized, user-friendly and optimal services. Key metrics enabled by proper data capture and processing are essential to run an effective business or service. Enabling technologies include data mining, scalable warehousing and preprocessing, sequence discovery, real time processing, document classification, user modeling and quality evaluation models for them. Recipient technologies that demand for user profiling and usage patterns include recommendation systems, Web analytics applications, application servers coupled with content management systems and fraud detectors.
Furthermore, the inherent and increasing heterogeneity of the Web has required Web-based applications to more effectively integrate a variety of types of data across multiple channels and from different sources such as content, structure, and more recently, semantics. A focus on techniques and architectures for more effective exploitation and mining of such multi-faceted data is likely to lead to the next generation of more useful and more intelligent applications. WEBKDD’2006 is interested in techniques that enhance Web usage mining through the use of other knowledge channels and sources. These considerations can help answer questions such as “Can a web usage mining system reason about the discovered (usage) patterns or user models ?” And “Can recommender systems explain their recommendation to users?”
The WEBKDD'2006 workshop aims to bring together practitioners and researchers with a specific focus on the emerging trends and industry needs, as well as all areas of Web mining and Semantic Web mining, with an emphasis on a seven years' update: What are the lessons learned on algorithms, semantics, data preparation, data integration and applications of the Web? How are new technologies, like adaptive mining methods, stream mining algorithms and techniques for the Grid apply to Web mining? What new challenges are posed by new forms of data, especially flat texts, documents, pictures and streams? Which lessons have we learned about usability, e-commerce applications, personalization, and recommendation engines?
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