A Web Browsing Cognitive Model
Web usage have been studied from the point of view of machine learning. Although web usage prediction are mostly restricted to an static web site structure, hence this results to be a hard restriction to accomplish in the practice. We propose a decision-making model that allow predicting web users’ navigation choices even in dynamics web sites. We propose a neurophysiological theory of web browsing decision making, which is based on the Leaky Competing Accumulator (LCA). The model is stochastic and has been studied in the context of Psychology for many years. Choices are performed to follow hyperlink according to user text preferences. This process is repeated until the web user decide to leave the web site. Model’s parameters are required to be fitted in order to perform Monte Carlo simulations. It has been observed that nearly 73% of the real distribution is recovered by this method.
KeywordsNeurocomputing Web User Behavior LCA Neurophysiology Stochastic Equation Stochastic Simulation Text Preferences Web Session Curse of Dimensionality Markov processes
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