Joint Modeling of Users' Interests and Mobility Patterns for Point-of-Interest Recommendation

Point-of-Interest (POI) recommendation has become an important means to help people discover interesting places, especially when users travel out of town. However, extreme sparsity of user-POI matrix creates a severe challenge. To cope with this challenge, we propose a unified probabilistic generative model, Topic-Region Model (TRM), to simultaneously discover the semantic, temporal and spatial patterns of users' check-in activities, and to model their joint effect on users' decision-making for POIs. We conduct extensive experiments to evaluate the performance of our TRM on two real large-scale datasets, and the experimental results clearly demonstrate that TRM outperforms the state-of-art methods.