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On Modelling Cognitive Styles of Users in Adaptive Interactive Systems using Artificial Neural Networks

User modelling in interactive Web systems is an essential quality to optimally filter, personalise and adapt their content and functionality to serve the intrinsic needs of individual users. The mechanism for obtaining the user model needs to be intelligent, adaptive and transparent to the user, in the sense that user experience should not be disrupted or compromised. Human factors are extensively employed lately for enriching user models by capturing more intrinsic perceptual characteristics of the users. Accordingly, this paper proposes the use of Artificial Neural Networks (ANNs) for attaining cognitive styles of users in adaptive interactive systems. One of the main benefits is the automatic prediction of cognitive typologies of users by avoiding psychometric tests, which are among the typical ways of constructing user profiles and are particularly timeconsuming. Furthermore, ANNs can efficiently model the relationship between cognitive styles and user interaction. The experimental setup and the results obtained show that ANNs are suitable for predicting the cognitive styles ratio of users in respect to their actual cognitive style ratio value.