Traj2User：利用嵌入计算用户移动行为的相似性
Traj2User: exploiting embeddings for computing similarity of users mobile behavior
摘要：
语义轨迹是用户运动的高级表示，其中与运动上下文相关的几个方面被表示为异构文本标签。为了找到一个有意义的语义丰富的轨迹相似性度量的目标，我们提出Traj2User，Word2Vec启发的方法，用于生成用户运动的矢量表示作为用户嵌入。Traj2User使用简单的轨迹表示，并将相似性模型的定义委托给网络的学习过程。初步结果表明，Traj2User能够生成有效的用户嵌入。
英文摘要：
Semantic trajectories are high level representations of user movements where several aspects related to the movement context are represented as heterogeneous textual labels. With the objective of finding a meaningful similarity measure for semantically enriched trajectories, we propose Traj2User, a Word2Vec-inspired method for the generation of a vector representation of user movements as user embeddings. Traj2User uses simple representations of trajectories and delegates the definition of the similarity model to the learning process of the network. Preliminary results show that Traj2User is able to generate effective user embeddings.
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。
