Effective fine-grained location prediction based on user check-in pattern in LBSNs

Location-Based Social Networks (LBSNs) have built bridges between virtual space and real-world mobility in recent years. The massive check-in data generated in LBSNs makes it possible to predict users' future check-in location, which has proved meaningful for e-commerce developments. Existing studies mainly focus on predicting the next check-in location with a coarse granularity, which only shows limited performance in practical scenarios. In this paper, we propose a comprehensive approach based on user check-in pattern to predict users' future check-in location at any fine-grained time in LBSNs. Firstly, users' check-in pattern involving time periodicity, global popularity and personal preference are analyzed. Secondly, we extract multiple features related to user check-in pattern and explore the predictive power of each individual feature. Thirdly, a set of features are combined into a supervised scoring model and a classification model respectively for predicting user's check-in location at a fine-grained time in the future. Finally, extensive experiments on three real-world Foursquare datasets are carefully designed to verify the effectiveness of the proposed approach. Experimental results show that our approach outperforms both baseline methods and state-of-the-art methods on various evaluation metrics.