Siriporn ChimphleeNaomie SalimMohd Salihin Bin NgadimanWitcha Chimphlee2025-03-102025-03-102006Advances in Systems, Computing Sciences and Software Engineering - Proceedings of SCSS 20051402052626; 978-140205262-010.1007/1-4020-5263-4-582-s2.0-84862209764https://repository.dusit.ac.th//handle/123456789/5075Predicting the next request of a user as visits Web pages has gained importance as Web-based activity increases. A large amount of research has been done on trying to predict correctly the pages a user will request. This task requires the development of models that can predicts a user's next request to a web server. In this paper, we propose a method for constructing first-order and second-order Markov models of Web site access prediction based on past visitor behavior and compare it association rules technique. In these approaches, sequences of user requests are collected by the session identification technique, which distinguishes the requests for the same web page in different browses. We report experimental studies using real server log for comparison between methods and show that degree of precision. © 2006 Springer.Association rulesMarkov modelPredictionConference paperScopus