发表机构
the College of Information, Mechanical and Electrical Engineering, Shanghai Normal University; Shanghai Urban-Rural Construction & Transportation Development Institute; Zhongke Zidong Information Technology（上海师范大学信息与机电工程学院; 上海城乡建设与交通发展研究院; 中科紫东信息技术（北京）有限公司）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
MakeTUL首次将知识图谱表示学习引入TUL，通过多关系移动知识图谱和双分支分类层，解决现有TUL方法特征利用不足等问题，提升轨迹用户链接性能。
AI 中文摘要
轨迹用户链接（Trajectory-User Linking，TUL）旨在从一组候选用户中识别匿名轨迹的所有者，为用户移动性分析和个性化位置感知服务提供基础。现有方法通常独立学习兴趣点（Point of Interest，POI）、时间和语义特征，对轨迹间共享的结构知识利用有限，且在分类前压缩结构与序列信息。为解决这些问题，本文提出面向轨迹用户链接的多关系知识图谱增强嵌入（MakeTUL），据我们所知，这是首次将知识图谱表示学习引入TUL的尝试。MakeTUL将访问时间、POI类别和转移速度信息组织为多关系移动知识图谱中的类型化关系，使异构移动语义能共同约束学习到的嵌入。所得POI表示进一步丰富了从轨迹集合中提取的高阶共现模式，为稀疏且重叠的轨迹提供结构先验知识。通过将这些经先验增强的表示与时间、类别及转移信息相结合，轨迹序列学习模块捕捉有序移动模式，而双分支分类层则在决策层面保留并结合全局结构证据与序列证据。
英文摘要
Trajectory-User Linking (TUL) aims to identify the owner of an anonymous trajectory from a set of candidate users, providing a basis for user mobility analysis and personalized location-aware services. Existing methods often learn Point of Interest (POI), temporal, and semantic features independently, make limited use of structural knowledge shared across trajectories, and compress structural and sequential information before classification. To address these issues, we propose Multi-Relational Knowledge Graph Enhanced Embedding for Trajectory-User Linking (MakeTUL), which, to the best of our knowledge, is the first attempt to introduce knowledge graph representation learning into TUL. MakeTUL organizes visit-time, POI-category, and transfer-speed information as typed relations in a multi-relational mobility knowledge graph, allowing heterogeneous mobility semantics to jointly constrain the learned embeddings. The resulting POI representations are further enriched with high-order co-occurrence patterns extracted from the trajectory collection, providing structural prior knowledge for sparse and overlapping trajectories. By integrating these prior-enhanced representations with temporal, category, and transfer information, the trajectory sequence learning module captures ordered mobility patterns, while a dual-branch classification layer preserves and combines global structural evidence and sequential evidence at the decision level.
