FashionCook: A Visual Analytics System for Human-AI Collaboration in Fashion E-Commerce Design

Fashion e-commerce design requires the integration of creativity, functionality, and responsiveness to user preferences. While AI offers valuable support, generative models often miss the nuances of user experience, and task-specific models, although more accurate, lack transparency and real-world adaptability-especially with complex multimodal data. These issues reduce designers' trust and hinder effective AI integration. To address this, we present FashionCook, a visual analytics system designed to support human-AI collaboration in the context of fashion e-commerce. The system bridges communication among model builders, designers, and marketers by providing transparent model interpretations, "what-if" scenario exploration, and iterative feedback mechanisms. We validate the system through two real-world case studies and a user study, demonstrating how FashionCook enhances collaborative workflows and improves design outcomes in data-driven fashion e-commerce environments.