Next Check-in Location Prediction via Footprints and Friendship on Location-Based Social Networks

Abstract

With the thriving of location-based social networks, a large number of user check-in data have been accumulated. Tasks such as the prediction of the next check-in location can be addressed through the usage of LBSN data. Previous work mainly uses the historical trajectories of users to analyze users’ check-in behavior, while the social information of users was rarely used. In this paper, we propose a unified location prediction framework to integrate the effect of history check-in and the influence of social circles. We first employ the most frequent check-in model (MFC) and the user-based collaborative filtering model (UCF) to capture users’ historical trajectories and users’ implicit preference, respectively. Then we use the multi-social circle model (MSC) to model the influence of three social circles. Finally, we evaluate our location prediction framework in the real-world data sets, and the experimental results show that our model performs better than the state-of-the-art approaches in predicting the next check-in location.