From Web to Social Web: Discovering and Deploying User and Content Profiles
The World Wide Web is a rich source of information about human behavior. It containslarge amount of data organizedvia interconnected Web pages,traces of information search, user feedback on items of interest, etc. In addition to large data volumes, one of
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Subseries of Lecture Notes in Computer Science
4737
Bettina Berendt Andreas Hotho Dunja Mladeniˇc Giovanni Semeraro (Eds.)
From Web to Social Web: Discovering and Deploying User and Content Profiles Workshop on Web Mining, WebMine 2006 Berlin, Germany, September 18, 2006 Revised Selected and Invited Papers
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Series Editors Jaime G. Carbonell, Carnegie Mellon University, Pittsburgh, PA, USA Jörg Siekmann, University of Saarland, Saarbrücken, Germany Volume Editors Bettina Berendt Institute of Information Systems Humboldt University Berlin, Germany E-mail: [email protected] Andreas Hotho KDE Group at the University of Kassel, Germany E-mail: [email protected] Dunja Mladeniˇc J. Stefan Institute, Ljubliana, Slovenia E-mail: [email protected] Giovanni Semeraro Department of Informatics University of Bari, Italy E-mail: [email protected]
Library of Congress Control Number: 2007934911
CR Subject Classification (1998): H.2.8, H.3-4 LNCS Sublibrary: SL 7 – Artificial Intelligence ISSN ISBN-10 ISBN-13
0302-9743 3-540-74950-0 Springer Berlin Heidelberg New York 978-3-540-74950-9 Springer Berlin Heidelberg New York
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Preface
The World Wide Web is a rich source of information about human behavior. It contains large amount of data organized via interconnected Web pages, traces of information search, user feedback on items of interest, etc. In addition to large data volumes, one of the important characteristics of the Web is its dynamics, where content, structure and usage are changing over time. This shows up in the rise of related research areas like communities of practice, knowledge management, Web communities, and peer-to-peer. In particular the notion of collaborative work and thus the need of its systematic analysis become more and more important. For instance, to develop effective Web applications, it is essential to analyze patterns hidden in the usage of Web resources, their contents and their interconnections. Machine learning and data mining methods have been used extensively to find patterns in usage of the network by exploiting both contents and link structures. We have investigated these topics in a series of workshops on Semantic W
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