Multi-View Urban Region Embedding via Commonality-Specificity Disentanglement

Multi-view region embedding has become an important technique to be pursued due to the increasing availability of diverse urban sensing data. Existing methods typically adopt attention-based fusion or contrastive alignment to integrate multiple data sources such as human mobility and points-of-interest (POIs) into unified region representations. However, these approaches often fail to effectively balance cross-view commonality modeling with the preservation of view-specific characteristics. To address this limitation, we propose ComSRE, a novel region embedding framework that explicitly disentangles shared and distinctive components in multi-view region representations. ComSRE integrates three key modules: (1) a Multi-view Representation Learning module that fuses complementary information across views, (2) a View-to-Commonality Contrastive Alignment module that aligns the representation of each view to a shared commonality anchor to enhance cross-view consistency, and (3) a Multi-view Differential Orthogonality module that isolates distinctive signals unique to each view and promotes their independence through orthogonality constraints. Extensive experiments on three real-world urban datasets demonstrate that ComSRE consistently outperforms state-of-the-art methods across multiple downstream tasks, achieving superior predictive accuracy and representation quality.