摘要
The recommendation system can efficiently solve the information overload in mobile Internet. Thus, how to effectively utilize context information to improve the accuracy of recommendation becomes the research focus in the field. This article puts forward a novel approach to realize the context-aware recommendation in mobile environments. It first gets users’ interest resonance with a hash-based interest resonance mining algorithm. Then, it calculates the association degree between the user and the item and then predicts the user’s rating on the item. Finally, it comprehensively figures out the recommending index. Moreover, this article also designs a personal recommendation model for the users and provides relevant decision-making coefficients. Experiments have demonstrated that our approach is superior to the traditional ones （RMP, RSTE, MD and BBBs） in both performance and efficiency.
The recommendation system can efficiently solve the information overload in mobile Internet. Thus, how to effectively utilize context information to improve the accuracy of recommendation becomes the research focus in the field. This article puts forward a novel approach to realize the context-aware recommendation in mobile environments. It first gets users’ interest resonance with a hash-based interest resonance mining algorithm. Then, it calculates the association degree between the user and the item and then predicts the user’s rating on the item. Finally, it comprehensively figures out the recommending index. Moreover, this article also designs a personal recommendation model for the users and provides relevant decision-making coefficients. Experiments have demonstrated that our approach is superior to the traditional ones （RMP, RSTE, MD and BBBs） in both performance and efficiency.
基金
Supported by the School-Enterprise Project of Nokia Research Center(Beijing)
