Abstract

In this paper we propose a novel methodology for analyzing Web user behavior based on session simulation by using an Ant Colony Optimization algorithm which incorporates usage, structure and content data originating from a real website. In the first place, artificial ants learn from a clustered Web user session set through the modification of a text preference vector. Then, trained ants are released through a web graph and the generated artificial sessions are compared with real usage. The main result is that the proposed model explains approximately 80% of real usage in terms of a predefined similarity measure. © 2011 IEEE.