A Naive Bayes Learning Based Website Reconfiguration System

Li, Jia and Li, Huiqing and Jia, Xiumei (2004) A Naive Bayes Learning Based Website Reconfiguration System. In: Proceedings of the 2004 International Conference on Machine Learning and Applications, DEC 16-18, 2004, Louisville, KY. (The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided)

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he continuous and sharp growth of web sites in terms of size and complexity has made improving the website organization to facilitate users' navigation something of an emergency. To address this problem, in this paper we propose a website reconfiguration system using the machine learning approach. First, a Naive Bayes Classifier is trained and then applied to identify each page in a web site as important oil unimportant in terms fulfilling visitors' information needs. For those important pages, we check the reason ableness of their locations, which is measured by the average number of hops needed to reach them during visitor sessions. Those important but difficult reach pages are considered for reconfiguration, which is done by either automatically moving them to some level closer to the visitors' starting point, making it easier for users to access them, or presenting webmasters with a list of suggestions. We also propose a formula to evaluate the "global structure" of a web site, and use it to examine the effect of our system on improving website design.

Item Type: Conference or workshop item (UNSPECIFIED)
Subjects: Q Science > QA Mathematics (inc Computing science) > QA 76 Software, computer programming,
Divisions: Faculties > Science Technology and Medical Studies > School of Computing > Theoretical Computing Group
Depositing User: Mark Wheadon
Date Deposited: 24 Nov 2008 18:01
Last Modified: 11 Jul 2009 12:35
Resource URI: https://kar.kent.ac.uk/id/eprint/14048 (The current URI for this page, for reference purposes)
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