Predicting Human Mobility via Self-supervised Disentanglement Learning

TL;DR

This paper introduces SSDL, a novel self-supervised disentanglement learning method for human mobility prediction that separates time-invariant and time-varying factors, improving interpretability and performance on real-world datasets.

Contribution

The study proposes a disentangled framework with trajectory augmentation and a POI-centric graph to better capture human mobility patterns and address data sparsity issues.

Findings

SSDL outperforms state-of-the-art methods by up to 8.57% in accuracy.

Disentanglement enhances interpretability of mobility representations.

Trajectory augmentation improves understanding of periodicity and changing intents.

Abstract

Deep neural networks have recently achieved considerable improvements in learning human behavioral patterns and individual preferences from massive spatial-temporal trajectories data. However, most of the existing research concentrates on fusing different semantics underlying sequential trajectories for mobility pattern learning which, in turn, yields a narrow perspective on comprehending human intrinsic motions. In addition, the inherent sparsity and under-explored heterogeneous collaborative items pertaining to human check-ins hinder the potential exploitation of human diverse periodic regularities as well as common interests. Motivated by recent advances in disentanglement learning, in this study we propose a novel disentangled solution called SSDL for tackling the next POI prediction problem. SSDL primarily seeks to disentangle the potential time-invariant and time-varying factors into different latent spaces from massive trajectories data, providing an interpretable view to understand the intricate semantics underlying human diverse mobility representations. To address the data sparsity issue, we present two realistic trajectory augmentation approaches to enhance the understanding of both the human intrinsic periodicity and constantly-changing intents. In addition, we devise a POI-centric graph structure to explore heterogeneous collaborative signals underlying historical check-ins. Extensive experiments conducted on four real-world datasets demonstrate that our proposed SSDL significantly outperforms the state-of-the-art approaches -- for example, it yields up to 8.57% improvements on ACC@1.