Personalized Multimedia Recommendations for Cloud-Integrated Cyber-Physical Systems

Portable smart devices have paved the way for accessing and capturing different types of multimedia contents with human interactions, leading to the emergence of cyber-physical systems (CPSs). Although the massive data collected from these physical terminals can contribute to the improvement of their quality of lives by building smart communities, CPSs intensify the information overload problem. Therefore, plenty of research efforts have been paid to develop multimedia recommender systems. However, most existing research activities neglect its time-varying features due to system dynamics, i.e., not only the amount of input data constantly grows, but also the change of user behaviors and system operating environment. In order to sustain the high accuracy of recommendations, the system in a CPS has to be updated regularly. However, the more often the update proceeds, the more the cost of other computational resources. To this end, in this paper, we propose an adaptive recommender system by using feedback control frameworks in CPSs. The proposed solution continuously monitors its changes and estimates the loss of performance (in terms of accuracy) to overcome the data aging problem and justify if the current "revisiting ratio" between the new and old items can still accurately reflect current user behavior. Theoretical analysis and extensive results by using a real data set in a cloud setting are supplemented to show the advantages of the proposed system.