Using normalize time spent on a Web page for Web personalization

The continuous growth of the information on the Internet makes it necessary for users to be provided with a convenient and yet accurate tool to capture the information needed. Web usage mining has gained more popularity among researchers in discovering the users browsing behavior. In this paper, we developed a usage model for predictions based on association rule and similarity measures. We used the normalized lime spent on each page for weighting the pages instead of binary. Two evaluation metrics is applied to evaluate the accuracy of the recommendations, namely precision and coverage. The result shows that the time spent on a Web page is essential in determining the importance of that page before it is recommended to the user.