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Lyon
Aug. 22, 2011 to Aug. 27, 2011
ISBN: 978-1-4577-1373-6
pp: 457-464
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 web site. 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.
INDEX TERMS
Ant Colony Optimization, Web User Behavior, Web Usage Mining, Multia-gent Simulation, Text Preferences
CITATION
Pablo Loyola, Pablo E. Rom´n, Juan D. Vel´squez, "Clustering-Based Learning Approach for Ant Colony Optimization Model to Simulate Web User Behavior", WI-IAT, 2011, 2011 IEEE/WIC/ACM International Joint Conferences on Web Intelligence (WI) and Intelligent Agent Technologies, 2011 IEEE/WIC/ACM International Joint Conferences on Web Intelligence (WI) and Intelligent Agent Technologies 2011, pp. 457-464, doi:10.1109/WI-IAT.2011.116
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