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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

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

Buch | Softcover

IX, 183 Seiten

2003 | 2003
Springer Berlin (Verlag)
978-3-540-20304-9 (ISBN)

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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.

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.

Erscheint lt. Verlag	13.10.2003
Reihe/Serie	Lecture Notes in Artificial Intelligence
Reihe/Serie	Lecture Notes in Computer Science
Zusatzinfo	IX, 183 p.
Verlagsort	Berlin
Sprache	englisch
Maße	155 x 233 mm
Gewicht	286 g
Themenwelt	Sachbuch/Ratgeber ► Natur / Technik ► Naturwissenschaft
Themenwelt	Mathematik / Informatik ► Informatik ► Theorie / Studium
Schlagworte	algorithms • Alignment • Association Rule Mining • Clustering • Data Mining • Hardcover, Softcover / Informatik, EDV/Informatik • HC/Informatik, EDV/Informatik • Knowledge Discovery • navigation analysis • Recommender System • Recommender Systems • session • usage analysis • usage pattern discovery • User Profiling • web access sequence mining • Web Data Mining • Web traffic mining • WWW
ISBN-10	3-540-20304-4 / 3540203044
ISBN-13	978-3-540-20304-9 / 9783540203049
Zustand	Neuware