Incorporating contextual information and collaborative filtering methods for multimedia recommendation in a mobile environment

Recommender systems have been developed in different application services. In addition to using recommendation techniques, it is helpful to employ contextual information in determining the relevance of an item to a users’s needs. To enhance recommendation performance, we present in this study two approaches that, in a direct way, integrate different types of contextual information and user ratings in computational methods. To verify the proposed approaches in making collaborative recommendations, we conduct a series of experiments to evaluate performance. The results show that the proposed context-aware methods outperform other conventional approaches. Moreover, we implement a mobile multimedia recommendation system on a cloud platform to demonstrate how our approaches can be used to develop a real-world application.