Unifying Heterogeneous and Homogeneous Relations for Personalized Compatibility Modeling

In recent years, the fashion industry has developed rapidly, leading to an increasing demand for personalized clothing matching. Although noteworthy progress has been made by existing work with advanced graph neural networks, there still remain three prominent challenges: comprehensive entity relation modeling, adaptive neighbor node sampling, and thorough characteri-zation of fashion items based on multi-modalities. To solve the above problems, we propose a novel personalized compatibility modeling framework, which explores both heterogeneous (i.e., item-user and user-item) and homogeneous (i.e., item-item and user-user) relation graphs with multi-modalities. Moreover, we design a correlation-guided neighbor sampling strategy to learn the correlation-aware subgraphs, which plays a crucial role in facilitating the exploration of compatibility relationships or personalized preferences. Finally, we conduct extensive experiments on two publicly available fashion datasets to demonstrate the superiority of our proposed model over existing benchmark methods.