Personalized real-time location-tagged contents recommender system based on mobile social networks

This paper proposes a real-time location-tagged contents recommender system which is based on mobile social network. The system locates a user via global positioning system, and then applies distance and preference filtering methods. We confirmed that the system is highly effective and applicable to convergence by a location data and content recommender through an implementation and preference prediction experiments.