Advances in Web Mining and Web Usage Analysis (eBook, PDF)

7th International Workshop on Knowledge Discovery on the Web, WEBKDD 2005, Chicago, IL, USA, August 21, 2005, Revised Papers
Redaktion: Nasraoui, Olfa; Yu, Philip; Masand, Brij; Mobasher, Manshad; Spiliopoulou, Myra; Zaiane, Osmar

• Format: PDF

Produktbeschreibung

Thisbookcontainsthepostworkshopproceedingsofthe7thInternationalWo- shop on Knowledge Discovery from the Web, WEBKDD 2005. The WEBKDD workshop series takes place as part of the ACM SIGKDD International Conf- ence on Knowledge Discovery and Data Mining (KDD) since 1999. The discipline of data mining delivers methodologies and tools for the an- ysis of large data volumes and the extraction of comprehensible and non-trivial insights from them. Web mining, a much younger discipline, concentrates on the analysisofdata pertinentto theWeb.Web mining methods areappliedonusage data and Web site content; they strive to improve our understanding of how the Web is used, to enhance usability and to promote mutual satisfaction between e-business venues and their potential customers. In the last years, the interest for the Web as medium for communication, interaction and business has led to new challenges and to intensive, dedicated research. Many of the infancy problems in Web mining have now been solved but the tremendous potential for new and improved uses, as well as misuses, of the Web are leading to new challenges.

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Produktdetails

• Verlag: Springer Berlin Heidelberg
• Seitenzahl: 182
• Erscheinungstermin: 20. Oktober 2006
• Englisch
• ISBN-13: 9783540463481
• Artikelnr.: 44599222

Autorenporträt

Olfa Nasraoui, University of Louisville, KY, USA / Osmar Zaiane, University of Alberta, AB, Canada / Myra Spiliopoulou, Otto-von-Guericke-University, Magdeburg, Germany / Manshad Mobasher, DePaul University, Chicago, IL, USA / Brij Masand, Data Miners, Inc., Boston, MA, USA / Philip Yu, IBM T.J. Watson, Yorktown Heights, NY, USA

Inhaltsangabe

Mining Significant Usage Patterns from Clickstream Data.- Using and Learning Semantics in Frequent Subgraph Mining.- Overcoming Incomplete User Models in Recommendation Systems Via an Ontology.- Data Sparsity Issues in the Collaborative Filtering Framework.- USER: User-Sensitive Expert Recommendations for Knowledge-Dense Environments.- Analysis and Detection of Segment-Focused Attacks Against Collaborative Recommendation.- Adaptive Web Usage Profiling.- On Clustering Techniques for Change Diagnosis in Data Streams.- Personalized Search Results with User Interest Hierarchies Learnt from Bookmarks.