Learning to Match on Graph for Fashion Compatibility Modeling

Abstract

Understanding the mix-and-match relationships between items receives increasing attention in the fashion industry. Existing methods have primarily learned visual compatibility from dyadic co-occurrence or co-purchase information of items to model the item-item matching interaction. Despite effectiveness, rich extra-connectivities between compatible items, e.g., user-item interactions and item-item substitutable relationships, which characterize the structural properties of items, have been largely ignored. This paper presents a graph-based fashion matching framework named Deep Relational Embedding Propagation (DREP), aiming to inject the extra-connectivities between items into the pairwise compatibility modeling. Specifically, we first build a multi-relational item-item-user graph which encodes diverse item-item and user-item relationships. Then we compute structured representations of items by an attentive relational embedding propagation rule that performs messages propagation along edges of the relational graph. This leads to expressive modeling of higher-order connectivity between items and also better representation of fashion items. Finally, we predict pairwise compatibility based on a compatibility metric learning module. Extensive experiments show that DREP can significantly improve the performance of state-of-the-art methods.