TrajGEOS：面向移动性预测的轨迹图增强的基于方向性的序列网络
TrajGEOS: Trajectory Graph Enhanced Orientation-based Sequential Network for Mobility Prediction
AI总结：
本文提出TrajGEOS模型，通过构建轨迹图并结合分层图卷积与基于方向性的模块，更有效地捕捉位置关系和用户多层次偏好，从而提升下一位置预测性能。
摘要：
人类移动性研究人们如何移动以获取所需资源，并在城市规划和基于位置的服务中发挥着重要作用。作为人类移动性建模的首要任务，下一位置预测具有挑战性，因为用户历史轨迹的多样性导致了复杂的移动模式和各种上下文。深度序列模型已被广泛用于通过利用轨迹数据固有的序列性来预测下一位置。然而，它们没有充分利用位置之间的关系，也未能捕捉用户的多层次偏好。本工作从用户的历史轨迹中构建轨迹图，并提出了一种轨迹图增强的基于方向性的序列网络（TrajGEOS）用于下一位置预测任务。TrajGEOS引入分层图卷积来捕获位置和用户嵌入。这种嵌入不仅考虑位置的上下文特征，还考虑它们之间的关系，并作为下游模块中的附加特征。此外，我们设计了一个基于方向性的模块，从序列建模模块和用户的近期轨迹中学习用户的中期偏好。在三个真实世界的LBSN数据集上的大量实验证实了图模块和基于方向性模块的价值，并表明TrajGEOS在下一位置预测任务上优于最先进的方法。
英文摘要：
Human mobility studies how people move to access their needed resources and plays a significant role in urban planning and location-based services. As a paramount task of human mobility modeling, next location prediction is challenging because of the diversity of users' historical trajectories that gives rise to complex mobility patterns and various contexts. Deep sequential models have been widely used to predict the next location by leveraging the inherent sequentiality of trajectory data. However, they do not fully leverage the relationship between locations and fail to capture users' multi-level preferences. This work constructs a trajectory graph from users' historical traces and proposes a \textbf{Traj}ectory \textbf{G}raph \textbf{E}nhanced \textbf{O}rientation-based \textbf{S}equential network (TrajGEOS) for next-location prediction tasks. TrajGEOS introduces hierarchical graph convolution to capture location and user embeddings. Such embeddings consider not only the contextual feature of locations but also the relation between them, and serve as additional features in downstream modules. In addition, we design an orientation-based module to learn users' mid-term preferences from sequential modeling modules and their recent trajectories. Extensive experiments on three real-world LBSN datasets corroborate the value of graph and orientation-based modules and demonstrate that TrajGEOS outperforms the state-of-the-art methods on the next location prediction task.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
