{"type":"ScholarlyArticle","title":"Explainable Fashion Recommendation: A Semantic Attribute Region Guided Approach","authors":[{"name":"Min Hou","slug":"min-hou","given":"Min","family":"Hou","affiliation":"Anhui Province Key Lab. of Big Data Analysis and Application , University of S&T of China; School of Data Science , University of S&T of China"},{"name":"Le Wu","slug":"le-wu","given":"Le","family":"Wu","affiliation":"Hefei University of Technology"},{"name":"Enhong Chen","slug":"enhong-chen","given":"Enhong","family":"Chen","affiliation":"Anhui Province Key Lab. of Big Data Analysis and Application , University of S&T of China; School of Data Science , University of S&T of China"},{"name":"Zhi Li","slug":"zhi-li","given":"Zhi","family":"Li","affiliation":"Anhui Province Key Lab. of Big Data Analysis and Application , University of S&T of China; School of Data Science , University of S&T of China"},{"name":"Vincent W. Zheng","slug":"vincent-w-zheng","given":"Vincent W.","family":"Zheng","affiliation":"WeBank"},{"name":"Qi Liu","slug":"qi-liu","given":"Qi","family":"Liu","affiliation":"Anhui Province Key Lab. of Big Data Analysis and Application , University of S&T of China; School of Data Science , University of S&T of China"}],"abstract":"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.","doi":"10.24963/ijcai.2019/650","url":"https://ijcai.org/proceedings/2019/650","pdf":"https://ijcai.org/proceedings/2019/0650.pdf","markdown":"https://ijcai.org/proceedings/2019/650.md","bibtex":"https://ijcai.org/proceedings/2019/bibtex/650","volume":{"slug":"2019","title":"Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2019","url":"https://ijcai.org/proceedings/2019/","year":2019,"location":"Macao","dates":"10-16 August 2019","editors":"Sarit Kraus","isbn":"978-0-9992411-4-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2019"},"section":"Multidisciplinary Topics and Applications","track":"Main track","number":650,"pages":{"first":4681,"last":4688},"keywords":["Multidisciplinary Topics and Applications: Recommender Systems","Machine Learning: Interpretability","Humans and AI: Personalization and User Modeling"],"videos":[],"references":[{"position":1,"raw":"[Abdollahi, 2017] Behnoush Abdollahi. 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