Spatio-Temporal Topic Modeling in Mobile Social Media for Location Recommendation

Mobile networks enable users to post on social media services (e.g., Twitter) from anywhere and anytime. This new phenomenon led to the emergence of a new line of work of mining the behavior of mobile users taking into account the spatio-temporal aspects of their engagement with online social media. In this paper, we address the problem of recommending the right locations to users at the right time. We claim to propose the first comprehensive model, called STT (Spatio-Temporal Topic), to capture the spatio-temporal aspects of user check-ins in a single probabilistic model for location recommendation. Our proposed generative model does not only captures spatio-temporal aspects of check-ins, but also profiles users. We conduct experiments on real life data sets from Twitter, Go Walla, and Bright kite. We evaluate the effectiveness of STT by evaluating the accuracy of location recommendation. The experimental results show that STT achieves better performance than the state-of-the-art models in the areas of recommender systems as well as topic modeling.