Towards Implicit User Modeling Based on Artificial Intelligence, Cognitive Styles and Web Interaction Data

Abstract

A key challenge of adaptive interactive systems is to provide a positive user experience by extracting implicitly the users' unique characteristics through their interactions with the system, and dynamically adapting and personalizing the system's content presentation and functionality. Among the different dimensions of individual differences that could be considered, this work utilizes the cognitive styles of users as determinant factors for personalization. The overarching goal of this paper is to increase our understanding about the effect of cognitive styles of users on their navigation behavior and content representation preference. We propose a Web-based tool, utilizing Artificial Intelligence techniques, to implicitly capture and find any possible relations between the cognitive styles of users and their characteristics in navigation behavior and content representation preference by using their Web interaction data. The proposed tool has been evaluated with a user study revealing that cognitive styles of users have an effect on their navigation behavior and content representation preference. Research works like the reported one are useful for improving implicit and intelligent user modeling in engineering adaptive interactive systems.