Adaptive personalized recommendation based on adaptive learning

Collaborative filtering has been widely applied in many fields in recent years due to the increase in web-based activities such as e-commerce and online content distribution. Current collaborative filtering techniques such as correlation-based, SVD-based and supervised learning-based approaches provide good accuracy, but are computationally very expensive and can only be deployed in static off-line settings, where the known rating information does not change with time. However, a number of practical scenarios require dynamic adaptive collaborative filtering that can allow new users, items and ratings to enter the system at a rapid rate. In this paper, we consider a novel adaptive personalized recommendation based on adaptive learning. Fast adaptive learning runs through all the aspects of the proposed approach, including training, prediction and updating. Empirical evaluation of our approach on Movielens dataset demonstrates that it is possible to obtain accuracy comparable to that of the correlation-based, SVD-based and supervised learning-based approaches at a much lower computational cost.