Personalized Mobile App Recommendation

Recent years have witnessed a rapid adoption of mobile devices and a dramatic proliferation of mobile applications (Apps for brevity). However, the large number of mobile Apps makes it difficult for users to locate relevant Apps. Therefore, recommending Apps becomes an urgent task. Traditional recommendation approaches focus on learning the interest of a user and the functionality of an item (e.g., an App) from a set of user-item ratings, and they recommend an item to a user if the item's functionality well matches the user's interest. However, Apps could have privileges to access a user's sensitive resources ( e.g., contact, message, and location). As a result, a user chooses an App not only because of its functionality, but also because it respects the user's privacy preference. To the best of our knowledge, this paper presents the first systematic study on incorporating both interest-functionality interactions and users' privacy preferences to perform personalized App recommendations. Specifically, we first construct a new model to capture the trade-off between functionality and user privacy preference. Then we crawled a real-world dataset (16,344 users, 6,157 Apps, and 263,054 ratings) from Google Play and use it to comprehensively evaluate our model and previous methods. We find that our method consistently and substantially outperforms the state-of-the-art approaches, which implies the importance of user privacy preference on personalized App recommendations. Moreover, we explore the impact of different levels of privacy information on the performances of our method, which gives us insights on what resources are more likely to be treated as private by users and influence users' behaviors at selecting Apps.