A Naive Bayes Learning Based Website Reconfiguration System

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.