标题： POIFormer：一种基于Transformer的精准且可扩展的兴趣点归属框架 显示英文标题

标题： POIFormer: A Transformer-Based Framework for Accurate and Scalable Point-of-Interest Attribution

摘要： 准确地将用户访问归因于特定的兴趣点（POIs）是移动性分析、个性化服务、市场营销和城市规划的基础任务。 然而，由于GPS的不准确性，在现实环境中通常在2到20米之间，以及城市环境中兴趣点的高空间密度，多个场所可以在一个小半径内共存（例如，在密集的城市中心100米半径内有超过50个POI），因此POI归因仍然具有挑战性。 因此，仅依赖接近性通常不足以确定实际访问了哪个POI。 我们引入了 \textsf{POIFormer}，一种基于Transformer的新框架，用于准确且高效的POI归因。 与之前依赖有限的空间时间、上下文或行为特征的方法不同， \textsf{POIFormer}联合建模了一组丰富的信号，包括空间接近度、访问时间和持续时间、来自POI语义的上下文特征以及来自用户移动性和聚合人群行为模式的行为特征——使用Transformer的自注意力机制来联合建模这些维度之间的复杂交互。 通过利用Transformer来建模用户的过去和未来访问（当前访问被屏蔽），并通过预计算的KDEs整合群体层面的行为模式， \textsf{POIFormer}实现了在大型、嘈杂的移动数据集中的准确、高效的归因。 其架构支持在多种数据源和地理环境中进行泛化，同时避免依赖难以获取或不可用的数据层，使其适用于实际部署。 在真实移动数据集上的大量实验表明，与现有基线相比有显著改进，特别是在由空间噪声和密集POI聚类特征的挑战性现实环境中。 显示英文摘要

摘要： Accurately attributing user visits to specific Points of Interest (POIs) is a foundational task for mobility analytics, personalized services, marketing and urban planning. However, POI attribution remains challenging due to GPS inaccuracies, typically ranging from 2 to 20 meters in real-world settings, and the high spatial density of POIs in urban environments, where multiple venues can coexist within a small radius (e.g., over 50 POIs within a 100-meter radius in dense city centers). Relying on proximity is therefore often insufficient for determining which POI was actually visited. We introduce \textsf{POIFormer}, a novel Transformer-based framework for accurate and efficient POI attribution. Unlike prior approaches that rely on limited spatiotemporal, contextual, or behavioral features, \textsf{POIFormer} jointly models a rich set of signals, including spatial proximity, visit timing and duration, contextual features from POI semantics, and behavioral features from user mobility and aggregated crowd behavior patterns--using the Transformer's self-attention mechanism to jointly model complex interactions across these dimensions. By leveraging the Transformer to model a user's past and future visits (with the current visit masked) and incorporating crowd-level behavioral patterns through pre-computed KDEs, \textsf{POIFormer} enables accurate, efficient attribution in large, noisy mobility datasets. Its architecture supports generalization across diverse data sources and geographic contexts while avoiding reliance on hard-to-access or unavailable data layers, making it practical for real-world deployment. Extensive experiments on real-world mobility datasets demonstrate significant improvements over existing baselines, particularly in challenging real-world settings characterized by spatial noise and dense POI clustering.