  The next Point of Interest (POI) recommendation aims to recommend the next
POI for users at a specific time. As users' check-in records can be viewed as a
long sequence, methods based on Recurrent Neural Networks (RNNs) have recently
shown good applicability to this task. However, existing methods often struggle
to fully explore the spatio-temporal correlations and dependencies at the
sequence level, and don't take full consideration for various factors
influencing users' preferences. To address these issues, we propose a novel
approach called Sequence-Aware Long- and Short-Term Preference Learning
(SA-LSPL) for next-POI recommendation. We combine various information features
to effectively model users' long-term preferences. Specifically, our proposed
model uses a multi-modal embedding module to embed diverse check-in details,
taking into account both user's personalized preferences and social influences
comprehensively. Additionally, we consider explicit spatio-temporal
correlations at the sequence level and implicit sequence dependencies.
Furthermore, SA-LSPL learns the spatio-temporal correlations of consecutive and
non-consecutive visits in the current check-in sequence, as well as transition
dependencies between categories, providing a comprehensive capture of user's
short-term preferences. Extensive experiments on two real-world datasets
demonstrate the superiority of SA-LSPL over state-of-the-art baseline methods.
