Mobility Prediction via Sequential Trajectory Disentanglement (Student Abstract)

Abstract

Accurately predicting human mobility is a critical task in location-based recommendation. Most prior approaches focus on fusing multiple semantics trajectories to forecast the future movement of people, and fail to consider the distinct relations in underlying context of human mobility, resulting in a narrow perspective to comprehend human motions. Inspired by recent advances in disentanglement learning, we propose a novel self-supervised method called SelfMove for next POI prediction. SelfMove seeks to disentangle the potential time-invariant and time-varying factors from massive trajectories, which provides an interpretable view to understand the complex semantics underlying human mobility representations. To address the data sparsity issue, we present two realistic trajectory augmentation approaches to help understand the intrinsic periodicity and constantly changing intents of humans. In addition, a POI-centric graph structure is proposed to explore both homogeneous and heterogeneous collaborative signals behind historical trajectories. Experiments on two real-world datasets demonstrate the superiority of SelfMove compared to the state-of-the-art baselines.