Understanding and accurately explaining compatibility relationships between fashion items is a challenging problem in the burgeoning domain of AI-driven outfit recommendations. Present models, while making strides in this area, still occasionally fall short, offering explanations that can be elementary and repetitive. This work aims to address these shortcomings by introducing the Pair Fashion Explanation (PFE) dataset, a unique resource that has been curated to illuminate these compatibility relationships. Furthermore, we propose an innovative two-stage pipeline model that leverages this dataset. This fine-tuning allows the model to generate explanations that convey the compatibility relationships between items. Our experiments showcase the model's potential in crafting descriptions that are knowledgeable, aligned with ground-truth matching correlations, and that produce understandable and informative descriptions, as assessed by both automatic metrics and human evaluation. Our code and data are released at https://github.com/wangyu-ustc/PairFashionExplanation

通过引入Pair Fashion Explanation（PFE）数据集和创新的两阶段流水线模型，本研究旨在理解和准确解释时尚物品之间的兼容关系，并提供可以传达物品之间兼容关系的解释。实验证明该模型具有潜力，能生成符合实际匹配关系的知识性、形象性的描述，经由多种自动指标和人工评估可证明。

通过提取和说明文本描述来解读兼容性关系