Abstract: The quest to develop applications to fully assist humans in our daily activities through the acquisition and utilization of contextual information has been the ultimate concern of researchers in pervasive computing. Many different implementation mechanisms have been adopted over the years. Currently, researchers are focusing on artificial neural network to develop context-aware recommender systems. This paper examines the various architectures and learning algorithms employed in these systems in order to deduce the general trend of implementation whilst assessing their pros and cons through literature survey. The paper goes ahead to recommend robust mechanism for future applications that can be used in mobile environment. The work revealed that the general trend of implementation is through threshold scoring mechanisms, reliance on the internet, complicated learning algorithms and architectures with the view to achieving higher prediction accuracy. These mechanisms ignore the limited capabilities of mobile devices in achieving pervasive computing. The work therefore recommended that there is the need to use simple but adaptive neural network architectures and learning algorithms so that these systems can be implemented on mobile devices without any negative impact on them
Keywords: Artificial Neural Network, Context-aware Application, Intelligent Systems, Mobile device, Pervasive Computing, Recommender Systems.
Title: A Robust Mechanism for Artificial Neural Network Context-Aware Recommender Systems (ANN CARS) in Mobile Environment
Author: Samuel King Opoku, Dr. D. Subba Rao
International Journal of Computer Science and Information Technology Research
ISSN 2348-1196 (print), ISSN 2348-120X (online)
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