A Category Aware Non-negative Matrix Factorization Approach for App Permission Recommendation

Abstract

The permission mechanism in Android imposes additional requirements on app developers, since developers have to learn not only the APIs to be used, but also the permissions to be declared. Recommending permissions for apps becomes necessary and meaningful to help developers determine suitable permissions to be declared in apps. Previous studies suffer from the cold-start problem and do not consider the fact that categories of APIs invoked by apps may influence permissions required by apps, since APIs with similar usage may request same permissions. To address these issues, this paper proposes a Category aware Non-negative Matrix Factorization (CNMF) framework to recommend app permissions. The framework firstly calculates semantic similarities among APIs based on word embeddings and clusters similar APIs into the same category, and then computes the probabilities of apps using APIs in each category and integrates the app-category information into the non-negative matrix factorization. Experimental results on a real-world dataset show that our framework can achieve better performance than the state-of-the-art approaches.