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.

本研究旨在通过提出一种新的基于属性信息的时装推荐器（AFRec），来解决时尚推荐任务中的可解释性挑战。AFRec 推荐器通过显式地利用每个项的视觉特征提取的属性级别表示来评估服装的兼容性。通过利用显式属性，AFRec 能够在推荐过程中实现最先进的推荐精度和直观的解释。