Modelling mix-and-match relationships among fashion items has become increasingly demanding yet challenging for modern E-commerce recommender systems. When performing clothes matching, most existing approaches leverage the latent visual features extracted from fashion item images for compatibility modelling, which lacks explainability of generated matching results and can hardly convince users of the recommendations. Though recent methods start to incorporate pre-defined attribute information (e.g., colour, style, length, etc.) for learning item representations and improving the model interpretability, their utilisation of attribute information is still mainly reserved for enhancing the learned item representations and generating explanations via post-processing. As a result, this creates a severe bottleneck when we are trying to advance the recommendation accuracy and generating fine-grained explanations since the explicit attributes have only loose connections to the actual recommendation process. This work aims to tackle the explainability challenge in fashion recommendation tasks by proposing a novel Attribute-aware Fashion Recommender (AFRec). Specifically, AFRec recommender assesses the outfit compatibility by explicitly leveraging the extracted attribute-level representations from each item’s visual feature. The attributes serve as the bridge between two fashion items, where we quantify the affinity of a pair of items through the learned compatibility between their attributes. Extensive experiments have demonstrated that, by making full use of the explicit attributes in the recommendation process, AFRec is able to achieve state-of-the-art recommendation accuracy and generate intuitive explanations at the same time.

对时尚单品之间的混搭关系建模，对现代电子商务推荐系统的要求越来越高，但也越来越具有挑战性。在进行服装匹配时，大多数现有方法利用从时尚单品图像中提取的潜在视觉特征进行兼容性建模，缺乏对生成匹配结果的可解释性，难以说服用户接受推荐。虽然最近的方法开始结合预定义的属性信息（例如，颜色、样式、长度等）来学习项目表示并提高模型的可解释性，但它们对属性信息的利用仍然主要保留用于增强学习项目表示和生成通过后处理进行解释。因此，当我们试图提高推荐准确性并生成细粒度解释时，这会产生严重的瓶颈，因为显式属性与实际推荐过程只有松散的联系。这项工作旨在通过提出一种新颖的属性感知时尚推荐器（AFRec）来解决时尚推荐任务中的可解释性挑战。具体来说，AFRec 推荐器通过明确利用从每个项目的视觉特征中提取的属性级表示来评估服装的兼容性。属性充当两个时尚项目之间的桥梁，我们通过学习到的属性之间的兼容性来量化一对项目的亲和力。大量实验证明，通过在推荐过程中充分利用显式属性，