WEBKDD 2002 - Mining Web Data for Discovering Usage Patterns and Profiles: 4th International Workshop, Edmonton, Canada, July 23, 2002, Revised Papers

by Osmar R. Zaiane (Editor), Jaideep Srivastava (Editor), Myra Spiliopoulou (Editor), Brij Masand (Editor)

Overview

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 to evaluate Web usability, understand the interests and expectations of users and assess the effectiveness 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 offers and content, made possible by discovered knowledge about the customer, are fundamental for the establi- ment of viable e-commerce solutions and sustained and effective 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 effort in data mining, Web usage data presents further challenges based on the difficulties of assigning data streams to unique users and tracking them over time. New innovations are required to reliably reconstruct sessions, to ascertain similarity and differences between sessions, and to be able to segment online users into relevant groups.

Product Details

ISBN-13:	9783540203049
Publisher:	Springer Berlin Heidelberg
Publication date:	12/05/2003
Series:	Lecture Notes in Computer Science , #2703
Edition description:	2003
Pages:	183
Product dimensions:	6.10(w) x 9.17(h) x 0.36(d)

Table of Contents

LumberJack: Intelligent Discovery and Analysis of Web User Traffic Composition.- Mining eBay: Bidding Strategies and Shill Detection.- Automatic Categorization of Web Pages and User Clustering with Mixtures of Hidden Markov Models.- Web Usage Mining by Means of Multidimensional Sequence Alignment Methods.- A Customizable Behavior Model for Temporal Prediction of Web User Sequences.- Coping with Sparsity in a Recommender System.- On the Use of Constrained Associations for Web Log Mining.- Mining WWW Access Sequence by Matrix Clustering.- Comparing Two Recommender Algorithms with the Help of Recommendations by Peers.- The Impact of Site Structure and User Environment on Session Reconstruction in Web Usage Analysis.