Category-Aware App Permission Recommendation based on Sparse Linear Model

Android has recently become one of the leading operating systems for mobile app development. The permission- based mechanism in Android forces app developers to determine permissions required by apps besides implementing the functionality, which increases the burden on developers. App permission recommendation becomes necessary and meaningful to assist developers determine appropriate needed permissions. Existing approaches for app permission recommendation have various limitations, such as suffering from the cold-start problem, needing to learn both of the app and permission embedding matrices. To address these issues, we define a sparse matrix factorization model, in which API categories are utilized as latent factors, app-API calls are applied for app representation, and only one sparse matrix is to be learned for permission representation. We further present an efficient approach by utilizing the Alternating Direction Method of Multipliers to solve the optimization problem. We conduct a comprehensive set of experiments on a real-world dataset, which show that our approach outperforms the state-of-the-art approaches in terms of four well-known metrics.