In the task of fashion compatibility prediction, the goal is to pick an item from a candidate list to complement a partial outfit in the most appealing manner. Existing fashion compatibility recommendation work comprehends clothing images in a single metric space and lacks detailed understanding of users’ preferences in different contexts. To address this problem, we propose a novel Metric-Aware Explainable Graph Network (MAEG). In MAEG, we leverage a Latent Semantic Extraction Network (LSEN) to obtain representations of items in the metric-aware latent semantic space. Then, we develop a graph filtering network and Pairwise Preference Attention (PPA) module to model the interactions between users’ preferences and contextual information. With MAEG, we can provide recommendation to users as well as explain how each item and factor contribute to the final prediction. Extensive experiments on two large-scale real-world datasets reveal that MAEG not only outperforms the state-of-the-art methods, but also provides interpretable insights by highlighting the role of semantic attributes and contextual relationships among items.