Spatio-Temporal Modeling of Users' Check-ins in Location-Based Social Networks
This work addresses the problem of user behavior prediction for social network applications, but it is incremental as it builds on existing probabilistic modeling approaches.
The paper tackled the problem of predicting user check-ins in location-based social networks by modeling spatio-temporal behavior with periodic patterns and social influence, resulting in a model that outperforms alternatives in predicting time and location of check-ins.
Social networks are getting closer to our real physical world. People share the exact location and time of their check-ins and are influenced by their friends. Modeling the spatio-temporal behavior of users in social networks is of great importance for predicting the future behavior of users, controlling the users' movements, and finding the latent influence network. It is observed that users have periodic patterns in their movements. Also, they are influenced by the locations that their close friends recently visited. Leveraging these two observations, we propose a probabilistic model based on a doubly stochastic point process with a periodic decaying kernel for the time of check-ins and a time-varying multinomial distribution for the location of check-ins of users in the location-based social networks. We learn the model parameters using an efficient EM algorithm, which distributes over the users. Experiments on synthetic and real data gathered from Foursquare show that the proposed inference algorithm learns the parameters efficiently and our model outperforms the other alternatives in the prediction of time and location of check-ins.