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Clustering-Based Learning Approach for Ant Colony Optimization Model to Simulate Web User Behavior

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3 Author(s)
Loyola, P. ; Dept. of Ind. Eng., Univ. de Chile, Santiago, Chile ; Roman, P.E. ; Velasquez, J.D.

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.

Published in:

Web Intelligence and Intelligent Agent Technology (WI-IAT), 2011 IEEE/WIC/ACM International Conference on  (Volume:1 )

Date of Conference:

22-27 Aug. 2011