TransNFCM: translation-based neural fashion compatibility modeling

• Main Authors: YANG, Xun, MA, Yunshan, LIAO, Lizi, WANG, Meng, CHUA, Tat-Seng
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2019
• Subjects: Arts computing; Deep learning; Deep neural networks; Embeddings; Knowledge representation; Product design; User experience; Vector spaces; Artificial Intelligence and Robotics
• Online Access: https://ink.library.smu.edu.sg/sis_research/7570; https://ink.library.smu.edu.sg/context/sis_research/article/8573/viewcontent/TransNFCM_translation_based_neural_fashion_compatibility_modeling.pdf
• Institution: Singapore Management University

Identifying mix-and-match relationships between fashion items is an urgent task in a fashion e-commerce recommender system. It will significantly enhance user experience and satisfaction. However, due to the challenges of inferring the rich yet complicated set of compatibility patterns in a large e-...