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Advances in Web Mining and Web Usage Analysis

6th International Workshop on Knowledge Discovery on the Web, WebKDD 2004, Seattle, WA, USA, August 22-25, 2004, Revised Selected Papers

  • Bamshad Mobasher
  • Olfa Nasraoui
  • Bing Liu
  • Brij Masand
Conference proceedings WebKDD 2004

Part of the Lecture Notes in Computer Science book series (LNCS, volume 3932)

Also part of the Lecture Notes in Artificial Intelligence book sub series (LNAI, volume 3932)

Table of contents

  1. Front Matter
  2. Web Usage Analysis and User Modeling

  3. Web Personalization and Recommender Systems

  4. Search Personalization

    1. Lin Deng, Wilfred Ng, Xiaoyong Chai, Dik-Lun Lee
      Pages 87-103
    2. Mehmet S. Aktas, Mehmet A. Nacar, Filippo Menczer
      Pages 104-115
  5. Semantic Web Mining

    1. Rosa Meo, Pier Luca Lanzi, Maristella Matera, Roberto Esposito
      Pages 135-148
    2. Stephan Bloehdorn, Andreas Hotho
      Pages 149-166
  6. Back Matter

About these proceedings

Introduction

TheWebisaliveenvironmentthatmanagesanddrivesawidespectrumofapp- cations in which a user may interact with a company, a governmental authority, a non-governmental organization or other non-pro?t institution or other users. User preferences and expectations, together with usage patterns, form the basis for personalized, user-friendly and business-optimal services. Key Web business metrics enabled by proper data capture and processing are essential to run an e?ective business or service. Enabling technologies include data mining, sc- able warehousing and preprocessing, sequence discovery, real time processing, document classi?cation, user modeling and quality evaluation models for them. Recipient technologies required for user pro?ling and usage patterns include recommendation systems, Web analytics applications, and application servers, coupled with content management systems and fraud detectors. Furthermore, the inherent and increasing heterogeneity of the Web has - quired Web-based applications to more e?ectively integrate a variety of types of data across multiple channels and from di?erent sources. The development and application of Web mining techniques in the context of Web content, Web usage, and Web structure data has already resulted in dramatic improvements in a variety of Web applications, from search engines, Web agents, and content management systems, to Web analytics and personalization services. A focus on techniques and architectures for more e?ective integration and mining of c- tent, usage,and structure data from di?erent sourcesis likely to leadto the next generation of more useful and more intelligent applications.

Keywords

data mining knowledge knowledge discovery modeling recommender system semantic web web mining

Editors and affiliations

  • Bamshad Mobasher
    • 1
  • Olfa Nasraoui
    • 2
  • Bing Liu
    • 3
  • Brij Masand
    • 4
  1. 1.Center for Web Intelligence School of ComputingDePaul UniversityChicagoUSA
  2. 2.Speed School of Engineering, Department of Computer Engineering & Computer ScienceUniversity of LouisvilleLouisvilleUSA
  3. 3.College of Architecture and Urban PlanningTongji UniversityShanghaiP.R. China
  4. 4.Data Miners Inc.BostonUSA

Bibliographic information

  • DOI https://doi.org/10.1007/11899402
  • Copyright Information Springer-Verlag Berlin Heidelberg 2006
  • Publisher Name Springer, Berlin, Heidelberg
  • eBook Packages Computer Science
  • Print ISBN 978-3-540-47127-1
  • Online ISBN 978-3-540-47128-8
  • Series Print ISSN 0302-9743
  • Series Online ISSN 1611-3349
  • Buy this book on publisher's site