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2011 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology
Clustering-Based Learning Approach for Ant Colony Optimization Model to Simulate Web User Behavior
Lyon, France
August 22-August 27
ISBN: 978-0-7695-4513-4
| ASCII Text | x | ||
| Pablo Loyola, Pablo E. Rom´n, Juan D. Vel´squez, "Clustering-Based Learning Approach for Ant Colony Optimization Model to Simulate Web User Behavior," Web Intelligence and Intelligent Agent Technology, IEEE/WIC/ACM International Conference on, vol. 1, pp. 457-464, 2011 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology, 2011. | |||
| BibTex | x | ||
| @article{ 10.1109/WI-IAT.2011.116, author = {Pablo Loyola and Pablo E. Rom´n and Juan D. Vel´squez}, title = {Clustering-Based Learning Approach for Ant Colony Optimization Model to Simulate Web User Behavior}, journal ={Web Intelligence and Intelligent Agent Technology, IEEE/WIC/ACM International Conference on}, volume = {1}, year = {2011}, isbn = {978-0-7695-4513-4}, pages = {457-464}, doi = {http://doi.ieeecomputersociety.org/10.1109/WI-IAT.2011.116}, publisher = {IEEE Computer Society}, address = {Los Alamitos, CA, USA}, } | |||
| RefWorks Procite/RefMan/Endnote | x | ||
| TY - CONF JO - Web Intelligence and Intelligent Agent Technology, IEEE/WIC/ACM International Conference on TI - Clustering-Based Learning Approach for Ant Colony Optimization Model to Simulate Web User Behavior SN - 978-0-7695-4513-4 SP457 EP464 A1 - Pablo Loyola, A1 - Pablo E. Rom´n, A1 - Juan D. Vel´squez, PY - 2011 KW - Ant Colony Optimization KW - Web User Behavior KW - Web Usage Mining KW - Multia-gent Simulation KW - Text Preferences VL - 1 JA - Web Intelligence and Intelligent Agent Technology, IEEE/WIC/ACM International Conference on ER - | |||
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, vol. 1, pp.457-464, 2011 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology, 2011
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