Reorganizing web sites based on user access patterns

Authors:
Yongjian Fu;Mario Creado;Chunhua Ju
Affiliations:
University of Missouri-Rolla, Rolla, MO;University of Missouri-Rolla, Rolla, MO;University of Missouri-Rolla, Rolla, MO
Venue:
Proceedings of the tenth international conference on Information and knowledge management
Year:
2001

Citing 2
Cited 5

Knowledge discovery from users Web-page navigation

RIDE '97 Proceedings of the 7th International Workshop on Research Issues in Data Engineering (RIDE '97) High Performance Database Management for Large-Scale Applications
Adaptive web sites: an AI challenge

IJCAI'97 Proceedings of the 15th international joint conference on Artifical intelligence - Volume 1

Data mining of web access logs from an academic web site

Design and application of hybrid intelligent systems
Mining interesting knowledge from weblogs: a survey

Data & Knowledge Engineering
Mining globally distributed frequent subgraphs in a single labeled graph

Data & Knowledge Engineering
New Techniques for Data Preprocessing Based on Usage Logs for Efficient Web User Profiling at Client Side

WI-IAT '09 Proceedings of the 2009 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology - Volume 03
Finding unexpected navigation behaviour in clickstream data for website design improvement

Journal of Web Engineering

Quantified Score

Hi-index	0.00

Visualization

Abstract

In this paper, an approach for reorganizing Web sites based on user access patterns is proposed. The approach consists of three steps: preprocessing, page classification, and site reorganization. In preprocessing, pages on a Web site are processed to create an internal representation of the site, and page access information of its users is extracted from its server log. In page classification, the Web pages on the site are classified into two categories, index pages and content pages, based on the page access information. After the pages are classified, in site reorganization, the Web site is examined to find better ways to organize and arrange the pages on the site. Our experiments on a large real data set show that the approach is efficient and practical for adaptive Web sites.