  In fashion recommender systems, each product usually consists of multiple
semantic attributes (e.g., sleeves, collar, etc). When making cloth decisions,
people usually show preferences for different semantic attributes (e.g., the
clothes with v-neck collar). Nevertheless, most previous fashion recommendation
models comprehend the clothing images with a global content representation and
lack detailed understanding of users' semantic preferences, which usually leads
to inferior recommendation performance. To bridge this gap, we propose a novel
Semantic Attribute Explainable Recommender System (SAERS). Specifically, we
first introduce a fine-grained interpretable semantic space. We then develop a
Semantic Extraction Network (SEN) and Fine-grained Preferences Attention (FPA)
module to project users and items into this space, respectively. With SAERS, we
are capable of not only providing cloth recommendations for users, but also
explaining the reason why we recommend the cloth through intuitive visual
attribute semantic highlights in a personalized manner. Extensive experiments
conducted on real-world datasets clearly demonstrate the effectiveness of our
approach compared with the state-of-the-art methods.
