Abstract
As the increasing quantity of mobile applications brings all kinds of benefits to smartphone users, people are more difficult to pick a new suitable mobile application (also known as app) out of hundreds in an app store. Thus, predicting which app will be installed by a specific user can help both users and app store operators. Existing works have focused on this problem and tried to use various features and algorithms to help recommend apps to users. However, some of them suffer from privacy and security issues, i.e. the system requires too much personal information about the user, such as detailed location series, social network information or even age, gender and other personality traits. And most of the content-based filtering methods only take the apps that have similar topics or functions to the already-used ones into consideration but ignore the demand saturation situation and the facts that users may explore new topics according to their personality. In this paper, we put forward a novel method, which uses limited user information to recommend new apps to individuals. It protects user privacy and achieves high accuracy at the same time. Experiments show that the proposed model achieve 23.5% precision and 19.3% recall in top-5 (out of 577 apps) prediction result.
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Wang, C., Chu, J. (2018). Privacy-Preserved Prediction for Mobile Application Adoption. In: Sun, X., Pan, Z., Bertino, E. (eds) Cloud Computing and Security. ICCCS 2018. Lecture Notes in Computer Science(), vol 11065. Springer, Cham. https://doi.org/10.1007/978-3-030-00012-7_17
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DOI: https://doi.org/10.1007/978-3-030-00012-7_17
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