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

Osmar R. Zaiane

ISBN 10: 3540203044 / ISBN 13: 9783540203049

Editore: Springer Berlin Heidelberg, Springer Berlin Heidelberg Okt 2003, 2003

Riguardo questo articolo

Neuware -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.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 196 pp. Englisch.

Codice articolo 9783540203049

Dati bibliografici

• Titolo: WEBKDD 2002 - Mining Web Data for Discovering Usage Patterns and Profiles
• Editore: Springer Berlin Heidelberg, Springer Berlin Heidelberg Okt 2003
• Data di pubblicazione: 2003
• Lingua: Inglese
• ISBN 10: 3540203044
• ISBN 13: 9783540203049
• Rilegatura: Taschenbuch
• Condizione: Neu
• Dimensioni: 235 L x 155 A x 11 P (millimetri) 235 millimetri di larghezza per 155 millimetri di altezza per 11 millimetri di profondità
• Peso: 306 grammi

Descrizione articolo

Riassunto

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.

Contenuti

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.