Modeling Multi-Relational Connectivity for Personalized Fashion Matching

Personalized fashion matching task aims to predict the compatible fashion items given available ones for specific users through the effective modeling of the third-order interaction patterns among the user and item pairs. To achieve this, previous methods separately model two key components, user-item and item-item relationships, which ignore the inherent correlations between them and lead to undesirable performance. With a new perspective, this paper proposes to formulate the personalized item matching as the multi-relational connectivity and apply a single-component translation operation to model the targeted third-order interactions. With user-item-item interactions naturally constructing a multi-relational graph, we further device two graph learning modules to enhance the translation-based matching approach from two perspectives,C ontext and Path. The proposed method, named CP-TransMatch, has been tested with extensive experiments on three benchmark fashion datasets and proven effective. It sets the new SOTA for the personalized fashion matching task.