Multi-Store Metadata-Based Supervised Mobile App Classification

The mass adoption of smartphone and tablet devices has boosted the growth of the mobile applications market. Confronted with a huge number of choices, users may encounter difficulties in locating the applications that meet their needs. Sorting applications into a user-defined classification scheme would help the app discovery process. Systems for automatically classifying apps into such a classification scheme are thus sorely needed. Methods for automated app classification have been proposed that rely on tracking how the app is actually used on users' mobile devices; however, this approach can lead to privacy issues. We present a system for classifying mobile apps into user-defined classification schemes which instead leverages information publicly available from the online stores where the apps are marketed. We present experimental results obtained on a dataset of 5,993 apps manually classified under a classification scheme consisting of 50 classes. Our results indicate that automated app classification can be performed with good accuracy, at the same time preserving users' privacy.