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In fashion recommender systems, each product usually consists of multiplesemantic attributes (e.g., sleeves, collar, etc). When making cloth decisions,people usually show preferences for different semantic attributes (e.g., theclothes with v-neck collar). Nevertheless, most previous fashion recommendationmodels comprehend the clothing images with a global content representation andlack detailed understanding of users' semantic preferences, which usually leadsto inferior recommendation performance. To bridge this gap, we propose a novelSemantic Attribute Explainable Recommender System (SAERS). Specifically, wefirst introduce a fine-grained interpretable semantic space. We then develop aSemantic Extraction Network (SEN) and Fine-grained Preferences Attention (FPA)module to project users and items into this space, respectively. With SAERS, weare capable of not only providing cloth recommendations for users, but alsoexplaining the reason why we recommend the cloth through intuitive visualattribute semantic highlights in a personalized manner. Extensive experimentsconducted on real-world datasets clearly demonstrate the effectiveness of ourapproach compared with the state-of-the-art methods.

Explainable Fashion Recommendation: A Semantic Attribute Region Guided Approach