Personalized online video recommendations by using adaptive feedback control frameworks

Recommender systems have changed the way people originally find products, information, and even their social circles. However, most existing research activities neglect its time-varying feature, i.e., the growing input data, the change of user behaviors. In order to sustain the high accuracy of recommendations, systems have to be updated regularly. However, the more often the update proceeds, the more cost of time and other computational resources. Thus, it is critical to strike the balance between accuracy and cost. In this paper, we propose an adaptive recommender system by using feedback control frameworks. The proposed solution continuously monitors its changes and estimates the loss of performance (in terms of accuracy) from two perspectives: data problem(data aging and data deficient) in training set, and changes of user behavior by "revisiting ratio". When the benefit of performing an update exceeds the cost of resources, the system update itself. Theoretical analysis and extensive results by using a real data set are supplemented to show the advantages of the proposed system.