Predicting Human Mobility With Semantic Motivation via Multi-Task Attentional Recurrent Networks

Human mobility prediction is of great importance for a wide spectrum of location-based applications. However, predicting mobility is not trivial because of four challenges: 1) the complex sequential transition regularities exhibited with time-dependent and high-order nature; 2) the multi-level periodicity of human mobility; 3) the heterogeneity and sparsity of the collected trajectory data; and 4) the complicated semantic motivation behind the mobility. In this paper, we propose DeepMove, an attentional recurrent network for mobility prediction from lengthy and sparse trajectories. In DeepMove, we first design a multi-modal embedding recurrent neural network to capture the complicated sequential transitions by jointly embedding the multiple factors that govern human mobility. Then, we propose a historical attention model with two mechanisms to capture the multi-level periodicity in a principle way, which effectively utilizes the periodicity nature to augment the recurrent neural network for mobility prediction. Furthermore, we design a context adaptor to capture the semantic effects of Point-Of-Interest (POI)-based activity and temporal factor (e.g., dwell time). Finally, we use the multi-task framework to encourage the model to learn comprehensive motivations with mobility by introducing the task of the next activity type prediction and the next check-in time prediction. We perform experiments on four representative real-life mobility datasets, and extensive evaluation results demonstrate that our model outperforms the state-of-the-art models by more than 10 percent. Moreover, compared with the state-of-the-art neural network models, DeepMove provides intuitive explanations into the prediction and sheds light on interpretable mobility prediction.