Multi-task Generative Adversarial Network for Missing Mobility Data Imputation

Abstract

Mobility data collected from location-based social networks are imperative for user movement behaviour analysis and marketing strategy customization. However, due to personal privacy and temporary failure of GPS devices, mobility data suffer from missing data issues. The missing mobility data hide beneficial information that can lead to distorted data analysis. To this end, we propose a multi-task generative adversarial network, termed as MDI-MG, to mitigate the negative impact of missing mobility data by imputing possible missing records. Specifically, in MDI-MG, we first introduce region-awareness modelling to fully capture sequential dependencies. Then, the generator is designed as a multi-task network, which unifies two highly pertinent tasks, including the primary task and the auxiliary missing POI region imputation task. The joint training on the two tasks enhances presentation capabilities and brings additional benefits. Besides, we adopt a discriminator to evaluate the generated sequences. The generator and the discriminator are optimized with a minimax two-player game. Experiments on two real-world datasets show that, MDI-MG achieves better performance in terms of both imputation accuracy and effectiveness, compared with state-of-the-art methods.