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.

翻译：下一兴趣点推荐任务旨在基于用户当前的签到轨迹动态排序兴趣点。该任务的推荐性能依赖于通过基于位置的社交网络数据全面理解用户的个性化行为模式。尽管已有研究能够精准捕捉用户签到轨迹中的序列模式和转移关系，但在设计区分不同时段（如中午、下午或傍晚）特殊行为模式的机制方面仍存在明显空白。本文提出一种名为“移动性树”的创新数据结构，用于分层描述用户的签到记录。移动性树包含多粒度时段节点，以学习用户在不同时间段的偏好。同时，我们提出移动性树网络——一种基于移动性树进行个性化偏好学习的多任务框架。我们开发了四步节点交互操作，将特征信息从叶节点传播至根节点。此外，采用多任务训练策略推动模型学习鲁棒表示。全面的实验结果表明，在三个真实基于位置的社交网络数据集上，MTNet相对于十种最先进的下一兴趣点推荐模型具有优越性，验证了移动性树辅助的时段偏好学习的有效性。