Learning Time Slot Preferences via Mobility Tree for Next POI Recommendation
<|reference_start|>Learning Time Slot Preferences via Mobility Tree for Next POI Recommendation: 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.<|reference_end|>
@article{huang2024learning,
    title={Learning Time Slot Preferences via Mobility Tree for Next POI
  Recommendation},
    author={Tianhao Huang, Xuan Pan, Xiangrui Cai, Ying Zhang, Xiaojie Yuan},
    journal={arXiv preprint arXiv:2403.12100},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.12100},
    primaryClass={cs.IR cs.AI cs.LG}
}