A Multi-Task Sequential State Model for the Human Trajectory Data Understanding

Abstract

Abstract Human trajectory data, collected from various location-based services, is of great significance to the understanding of users. However, trajectory-based user understanding is very challenging, due to the huge semantic gap between the existing low-level GEO spatial information and the target high-level semantic information. In this work, we propose a sequential state model as well as a multi-task based learning method to bridge the above semantic gap. First, we propose a sequential state model to organize the human trajectory data as well as the POI information. Second, we employ a LSTM based representation method to extract the semantic representation from the sequential state model, in which various representations are learned by using the user tags as the supervise information individually. Finally, we devise a multi-task fine-tuning LSTM method to take advantage of the dependency among the tags. We also demonstrate the usage of the proposed method and the effectiveness of the proposal in a real-world demand-side-platform system.