Deciphering Compatibility Relationships with Textual Descriptions via
  Extraction and Explanation
Yu Wang Zexue He Zhankui He Hao Xu Julian McAuley
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