  Next Point-of-Interests (POIs) recommendation task aims to provide a dynamic
ranking of POIs based on users' current check-in trajectories. The
recommendation performance of this task is contingent upon a comprehensive
understanding of users' personalized behavioral patterns through Location-based
Social Networks (LBSNs) data. While prior studies have adeptly captured
sequential patterns and transitional relationships within users' check-in
trajectories, a noticeable gap persists in devising a mechanism for discerning
specialized behavioral patterns during distinct time slots, such as noon,
afternoon, or evening. In this paper, we introduce an innovative data structure
termed the ``Mobility Tree'', tailored for hierarchically describing users'
check-in records. The Mobility Tree encompasses multi-granularity time slot
nodes to learn user preferences across varying temporal periods. Meanwhile, we
propose the Mobility Tree Network (MTNet), a multitask framework for
personalized preference learning based on Mobility Trees. We develop a
four-step node interaction operation to propagate feature information from the
leaf nodes to the root node. Additionally, we adopt a multitask training
strategy to push the model towards learning a robust representation. The
comprehensive experimental results demonstrate the superiority of MTNet over
ten state-of-the-art next POI recommendation models across three real-world
LBSN datasets, substantiating the efficacy of time slot preference learning
facilitated by Mobility Tree.
