Mobile Applications Recommendation Based on User Ratings and Permissions

Nowadays a large number of portable android applications are coming in the market. So, it has become a very difficult task for the user to ensure the security of the mobile applications that he wants to install. So, to simplify this, we propose a mobile App recommender system with popularity and security awareness. The design aspect is to recommend the mobile applications by evaluating the security risks of mobile apps and popularity based on user ratings. We use a web crawler which indexes the applications and store it in a database. Then the applications are clustered based on its popularity and user ratings. Whenever a query executes the proposed android application lists out apps from Google Play Store with its security rating. The security risk of the applications mainly depends on permissions that the application uses and its user popularity. The objective of this paper is to provide an effective recommendation system without compromising security aspects and popularity.