Learning Individual Moving Preference and Social Interaction for Location Prediction

Location prediction has attracted increasing attention in diverse fields due to its wide applications, such as traffic planning and control, weather forecasting, homeland security, and travel recommendation. Many existing algorithms forecast a user's next location by learning that user's past moving patterns. However, the individual moving patterns in many practical applications (e.g., the moving trajectory of a taxi driver) tend to be random, which poses a big challenge for location prediction. In this paper, we propose a new robust location prediction model that considers both individual preferences and social interactions (PSI) at a group level to alleviate the effect of randomness and improve the location prediction performance. Specifically, we first extract hot places of interesting (POIs) and normal POIs, respectively, via a two-stage clustering approach. To characterize exterior social interactions, an associated group is identified, and an outline of group moving patterns is then extracted based on association rule mining. Finally, the next location is predicted by learning the individual's regular patterns and group moving patterns via a pair-wise ridge regression. In contrast to the traditional approaches, our proposed algorithm has several desirable characteristics: 1) PSI provides an intuitive and quantitative way to model human movement from two aspects: the individual's internal moving preferences and group-level exterior social interactions; 2) Building upon group-level pattern mining, PSI provides a more robust prediction model by learning both individual and group trend information simultaneously, alleviating the randomness of location prediction from individual historical trajectory data only; and 3) The experimental results demonstrate that PSI achieves a better prediction performance compared to the state-of-the-art methods.