Check in or Not? A Stochastic Game for Privacy Preserving in Point-of-Interest Recommendation System

With the growing popularity of mobile social networks, point-of-interest (POI) recommendation, which utilizes users' check-in data to suggest interesting places for users, has attracted much attention in recent years. The check-in data, containing time and location information, are closely related to the user's personal life. Due to privacy concerns, users are reluctant to share check-in data with the service provider (SP), which causes a negative effect on recommendations. It is important for the user to find a balance between privacy and recommendation quality. In this paper, we consider a POI recommendation scenario where an adversary can access the data that a user reports to the SP. The user sequentially decides whether to check in for the POI he has visited. A stochastic game model is proposed to analyze the interaction between the user and the adversary. To find a good policy for the user, two value iteration algorithms are applied. The proposed game has a large state set, which makes it difficult for policy learning. To deal with this problem, we use some tricks when implementing the minimax Q-learning algorithm, and a set of neural networks are trained to approximate the Q-functions. To evaluate the performance of the learning algorithms, we conduct a series of simulations by using real-world check-in data. Simulation results show that the proposed learning algorithms can help the user to make good decisions, in the sense that the user can get a high long-term return.