Urban region embedding is an important and yet highly challenging issue due
to the complexity and constantly changing nature of urban data. To address the
challenges, we propose a Region-Wise Multi-View Representation Learning (ROMER)
to capture multi-view dependencies and learn expressive representations of
urban regions without the constraints of rigid neighbourhood region conditions.
Our model focus on learn urban region representation from multi-source urban
data. First, we capture the multi-view correlations from mobility flow
patterns, POI semantics and check-in dynamics. Then, we adopt global graph
attention networks to learn similarity of any two vertices in graphs. To
comprehensively consider and share features of multiple views, a two-stage
fusion module is further proposed to learn weights with external attention to
fuse multi-view embeddings. Extensive experiments for two downstream tasks on
real-world datasets demonstrate that our model outperforms state-of-the-art
methods by up to 17\% improvement.

通过多视图表示学习，我们提出了一种区域化多视图表示学习（ROMER）方法，用于捕捉多视图依赖关系并学习表达性强的城市区域表示。我们的模型关注于从多源城市数据中学习城市区域表示，通过捕捉流动模式、兴趣点语义和签到动态等多视图相关性，采用全局图注意力网络学习图中任意两个顶点的相似度，并进一步提出了一个两阶段融合模块来综合考虑和共享多视图嵌入的特征。对真实世界数据集上的两个下游任务进行的大量实验证明，我们的模型性能优于现有方法，改进了最高达 17%。