A personalized recommendation model based on the user-state awareness

Major recommendation systems don´t consider the user´s current composite state in time. And also they do not consider the affect on the recommendation results when the user updates the context. In the process of analyzing the user´s interests and preferences, it is not enough to determine the user state only based on the browsing records about the user, so more context information from the user´s current activity is need excavated. In this paper, a personalized recommendation model based on the user-state awareness is proposed. Through the complement of the user-state perception, the recommendation system is more intelligent and has a higher service quality.