Advances in Web Mining and Web Usage Analysis : 7th International Workshop on Knowledge Discovery on the Web, WebKDD 2005, Chicago, IL, USA, August 21, 2005. Revised Papers / edited by Olfa Nasraoui, Osmar Zaïane, Myra Spiliopoulou, Bamshad Mobasher, Brij Masand, Philip S. Yu.

Springer eBooks

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.

This book constitutes the thoroughly refereed post-proceedings of the 7th International Workshop on Mining Web Data, WEBKDD 2005, held in Chicago, IL, USA in August 2005 in conjunction with the 11th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2005. The 9 revised full papers presented together with a detailed preface went through two rounds of reviewing and improvement and were carfully selected for inclusion in the book. The enhanced papers show that Web mining techniques and applications have to more effectively integrate a variety of types of data across multiple channels and from different sources in addition to usage, such as content, structure, and semantics. Thus a next generation of intelligent applications is stimulated for more effective exploitation and mining of multi-faceted data. The papers express also the need to study and design robust recommender systems that can resist various malicious manipulations.

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