OSCAR: an Online Scalable Adaptive Recommender for improving the recommendation effectiveness of entertainment video webshop

A recommender system is beneficial for the sales of e-commerce, so many kinds of recommendation approaches have been proposed for various situations. However, each recommendation approach can deal well with some kinds of categories and users´ behaviors only. Accordingly, how to provide users with the personalized recommendation with higher fidelity is an important issue. Therefore, in this paper, an Online SCalable Adaptive Recommendation scheme, called OSCAR, has been proposed in order to take advantages of various recommendation approaches and then efficiently coordinate them to adaptively meet the users´ preferences according to the various contents´ characteristics and users´ behaviors. Besides, the experimental results show that OSCAR´s recommendation effectiveness is better and more stable than existing approaches.