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Personalized app recommendation based on app permissions


With the development of science and technology, the popularity of smart phones has made exponential growth in mobile phone application market. 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 apps’ permissions, functionalities and users’ interests altogether. 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 devise a recommendation method based on both permissions and functionalities. 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. Furthermore, we propose a novel matrix factorization algorithm MFPF based on users’ interests, apps’ permissions and functionalities to handle personalized app recommendation. We compare our work with some of the state-of-the-art recommendation algorithms, and the results indicate that our work can improve the recommendation accuracy remarkably.

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This work is supported by the National Science Foundation of China (NSFC, No.61472291 and No.41472288).

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Correspondence to Gang Tian.

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This article belongs to the Topical Collection: Special Issue on Security and Privacy of IoT

Guest Editors: Tarik Taleb, Zonghua Zhang, and Hua Wang

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Peng, M., Zeng, G., Sun, Z. et al. Personalized app recommendation based on app permissions. World Wide Web 21, 89–104 (2018).

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