Research On Personalized APP Recommendation Algorithm Based On Permissions And Functionalities

With the development of science and technology,the popularity of smart phones has made exponential growth.How to help users to select applications they prefer has become a hot topic in recommendation algorithm.As traditional recommendation algorithms are based on popularity and download,they inadvertently fail to recommend the desirable applications.At the same time,many users tend to pay more attention to permissions of those applications,because of some privacy and security reasons.There are few recommendation algorithms which take account of both apps'permissions,functionalities and users' interests.Some of them only consider permissions while neglecting the users' interests,others just perform linear combination of apps' permissions,functionalities and users' interests to implement top-N recommendation.In this paper,we propose a novel matrix factorization algorithm MFPF based on users' interests,apps' permissions and functionalities to handle personalized app recommendation.The main contributions of our work includes as follow.(1)Combining with the experimental data,we analyze the apps' permissions,and we analyze the number of general app's required permissions,the types of permissions as well as the required permissions between the general app and malicious app.(2)After demonstrating the correlation of apps' permissions and users' interests,we design an app risk score calculating method ARSM based on app-permission bipartite graph model.(3)We construct a matrix factorization algorithm to perform recommendation.This algorithm recommends apps by integrating users' interests on apps'functionalities,as well as the apps' permissions.(4)We evaluate the proportion and influence of the permissions in the users'ratings,further analysis on the relationship between users' ratings and apps'permissions.The experimental data contain app and the corresponding user comments score data in this paper comes from the An Zhi market.The experimental results show that compared with the traditional recommendation method,our algorithm has better effect,and the accuracy of this algorithm is higher than that of the newest app recommendation system with the same privileges and functions.