Reorganizing web sites based on user access patterns

In this paper, an approach for reorganizing Web sites based on user access patterns is proposed. Our goal is to build adaptive Web sites by evolving site structure to facilitate user access. 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. Page access information of its users is extracted from the Web 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. An algorithm for reorganizing Web sites has been developed. Our experiments on a large real data set show that the approach is efficient and practical for adaptive Web sites. Copyright © 2002 John Wiley & Sons, Ltd.

[1]  Jaideep Srivastava,et al.  Data Preparation for Mining World Wide Web Browsing Patterns , 1999, Knowledge and Information Systems.

[2]  Jaideep Srivastava,et al.  Web usage mining: discovery and application of interesting patterns from web data , 2000 .

[3]  Georgios Paliouras,et al.  Clustering the Users of Large Web Sites into Communities , 2000, ICML.

[4]  K. Ueda Launching the new era : The fifth generation project : personal perspectives , 1993 .

[5]  Mark Levene,et al.  Mining Association Rules in Hypertext Databases , 1998, KDD.

[6]  Oren Etzioni,et al.  Adaptive Web Sites: Automatically Synthesizing Web Pages , 1998, AAAI/IAAI.

[7]  Oren Etzioni,et al.  The World-Wide Web: quagmire or gold mine? , 1996, CACM.

[8]  Myra Spiliopoulou,et al.  Web Usage Analysis and User Profiling: International WEBKDD'99 Workshop San Diego, CA, USA, August 15, 1999 Revised Papers , 2000 .

[9]  Jaideep Srivastava,et al.  Creating adaptive Web sites through usage-based clustering of URLs , 1999, Proceedings 1999 Workshop on Knowledge and Data Engineering Exchange (KDEX'99) (Cat. No.PR00453).

[10]  Cyrus Shahabi,et al.  Knowledge discovery from users Web-page navigation , 1997, Proceedings Seventh International Workshop on Research Issues in Data Engineering. High Performance Database Management for Large-Scale Applications.

[11]  Oren Etzioni,et al.  Adaptive Web Sites: an AI Challenge , 1997, IJCAI.

[12]  Maurice D. Mulvenna,et al.  Discovering Internet marketing intelligence through online analytical web usage mining , 1998, SGMD.

[13]  Anthony Scime WebSifter: an ontology-based personalizable search agent for the Web , 2000, Proceedings 2000 Kyoto International Conference on Digital Libraries: Research and Practice.

[14]  Sourav S. Bhowmick,et al.  Research Issues in Web Data Mining , 1999, DaWaK.

[15]  Jian Pei,et al.  Mining Access Patterns Efficiently from Web Logs , 2000, PAKDD.

[16]  Phillip M. Hallam-Baker,et al.  Extended Log File Format , 1996, World Wide Web J..

[17]  Bamshad Mobasher,et al.  Discovery of Aggregate Usage Profiles for Web Personalization , 2000 .

[18]  Jiawei Han,et al.  Discovering Web access patterns and trends by applying OLAP and data mining technology on Web logs , 1998, Proceedings IEEE International Forum on Research and Technology Advances in Digital Libraries -ADL'98-.

[19]  Jaideep Srivastava,et al.  Web mining: information and pattern discovery on the World Wide Web , 1997, Proceedings Ninth IEEE International Conference on Tools with Artificial Intelligence.

[20]  Myra Spiliopoulou,et al.  WUM - A Tool for WWW Ulitization Analysis , 1998, WebDB.