【摘要】 为进一步探究人类移动行为模式，提出一种基于孪生分层注意力的网络模型解决轨迹用户链接预测问题。该模型框架包括判别模块和检索模块，其中：判别模块对轨迹位置信息进行编码，采用改进的分层注意力网络捕获轨迹间的潜在相关性；检索模块利用判别模块计算已知用户轨迹与未知轨迹间的相似性得分，并将KNN作为分类器实现未知轨迹与用户的链接预测。在某城市的基于位置服务(LBS)的数据集上进行实验，结果表明该模型在不同用户数量中性能表现优越。更多还原

【Abstract】 In order to further explore the human mobility behavior pattern, this paper proposes a network model based on Siamese hierarchical attention to solve the task of trajectory-user link prediction. The framework of model was divided into discriminant module and retrieval module, in which the discriminant module encoded the position information through trajectory embedding, and the improved hierarchical attention network was used to capture the latent correlation between trajectories. The retrieval module used the discriminant module to calculate the similarity score between the known user trajectories and the unknown trajectory. KNN was used as a classifier to link between the unknown trajectory and the user. Experiments were conducted on the dataset based on location-based service(LBS) in a city. The results show that the model in this paper performs well in different user numbers.更多还原