A Time-Context-Dependent Resource Diffusion Algorithm Based on User Splitting

Abstract

Abstract Personalized recommendation for smartphone applications is studied, and a time-context-dependent resource diffusion algorithm based on user splitting is proposed. The algorithm introduces time context information. According to this idea, the algorithm can make full use of user preference data information, cluster mobile applications to get more user preference categories and optimize recommendation results. In the personalized recommendation process of mobile applications, because users and projects are dynamic changes, this paper analyses the dynamic change process of the algorithm in detail, and enhances the applicability of the algorithm. Experimental results show that the proposed algorithm improves the accuracy of recommendation results compared with the previous resource diffusion algorithm.