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WEBKDD 2002 - Mining Web Data for Discovering Usage Patterns and Profiles

4th International Workshop, Edmonton, Canada, July 23, 2002, Revised Papers

  • Conference proceedings
  • © 2003

Overview

Editors:
  1. Osmar R. Zaïane

    1. University of Alberta, Canada

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  2. Jaideep Srivastava

    1. University of Minnesota, Minneapolis, USA

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  3. Myra Spiliopoulou

    1. Faculty of Computer Science, Otto-von-Guericke-University Magdeburg, Germany

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    You can also search for this editor in PubMed   Google Scholar

  4. Brij Masand

    1. Data Miners Inc., Boston, USA

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Part of the book series: Lecture Notes in Computer Science (LNCS, volume 2703)

Part of the book sub series: Lecture Notes in Artificial Intelligence (LNAI)

Included in the following conference series:

Conference proceedings info: WebKDD 2002.

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Table of contents (10 papers)

  1. Front Matter

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  2. LumberJack: Intelligent Discovery and Analysis of Web User Traffic Composition

    • Ed H. Chi, Adam Rosien, Jeffrey Heer
    Pages 1-16

  3. Mining eBay: Bidding Strategies and Shill Detection

    • Harshit S. Shah, Neeraj R. Joshi, Ashish Sureka, Peter R. Wurman
    Pages 17-34

  4. Automatic Categorization of Web Pages and User Clustering with Mixtures of Hidden Markov Models

    • Alexander Ypma, Tom Heskes
    Pages 35-49

  5. Web Usage Mining by Means of Multidimensional Sequence Alignment Methods

    • Birgit Hay, Geert Wets, Koen Vanhoof
    Pages 50-65

  6. A Customizable Behavior Model for Temporal Prediction of Web User Sequences

    • Enrique Frías-Martínez, Vijay Karamcheti
    Pages 66-85

  7. Coping with Sparsity in a Recommender System

    • André Bergholz
    Pages 86-99

  8. On the Use of Constrained Associations for Web Log Mining

    • Hui Yang, Srinivasan Parthasarathy
    Pages 100-118

  9. Mining WWW Access Sequence by Matrix Clustering

    • Shigeru Oyanagi, Kazuto Kubota, Akihiko Nakase
    Pages 119-136

  10. Comparing Two Recommender Algorithms with the Help of Recommendations by Peers

    • Andreas Geyer-Schulz, Michael Hahsler
    Pages 137-158

  11. The Impact of Site Structure and User Environment on Session Reconstruction in Web Usage Analysis

    • Bettina Berendt, Bamshad Mobasher, Miki Nakagawa, Myra Spiliopoulou
    Pages 159-179

  12. Back Matter

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  1. WEBKDD 2002 - Mining Web Data for Discovering Usage Patterns and Profiles

Keywords

About this book

1 WorkshopTheme Data mining as a discipline aims to relate the analysis of large amounts of user data to shed light on key business questions. Web usage mining in particular, a relatively young discipline, investigates methodologies and techniques that - dress the unique challenges of discovering insights from Web usage data, aiming toevaluateWebusability,understandtheinterestsandexpectationsofusersand assess the e?ectiveness of content delivery. The maturing and expanding Web presents a key driving force in the rapid growth of electronic commerce and a new channel for content providers. Customized o?ers and content, made possible by discovered knowledge about the customer, are fundamental for the establi- ment of viable e-commerce solutions and sustained and e?ective content delivery in noncommercial domains. Rich Web logs provide companies with data about their online visitors and prospective customers, allowing microsegmentation and personalized interactions. While Web mining as a domain is several years old, the challenges that characterize data analysis in this area continue to be formidable. Though p- processing data routinely takes up a major part of the e?ort in data mining, Web usage data presents further challenges based on the di?culties of assigning data streams to unique users and tracking them over time. New innovations are required to reliably reconstruct sessions, to ascertain similarity and di?erences between sessions, and to be able to segment online users into relevant groups.

Editors and Affiliations

  • University of Alberta, Canada

    Osmar R. Zaïane

  • University of Minnesota, Minneapolis, USA

    Jaideep Srivastava

  • Faculty of Computer Science, Otto-von-Guericke-University Magdeburg, Germany

    Myra Spiliopoulou

  • Data Miners Inc., Boston, USA

    Brij Masand

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