  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.
