Context-Awareness Based Multidimensional Personalized Recommendation

Abstract

This paper proposes the CaPeR which is the context-awareness based architecture for personalized recommendation. The CaPeR provides the personalized recommender engine and the peer-to-peer context management framework. With a hybrid approach, the personalized recommender engine combines those contexts into the decisions on recommendations to get more comprehensive recommendation effectiveness. Owing to the distribution and limit of resources of the mobile terminals, the CaPeR constructs a context management framework based on the mechanism of Registration query. Through designing an efficient mechanism of message intercommunications, the CaPeR builds up the architecture for sharing and fusing of contexts to satisfy the requirements of recommendations. At the end of this paper, the Flowing Desktop is given a description as the prototype of the CaPeR.