In today's fashion retailing business, providing "fashion mix-and-match" or "fashion coordination" recommendations is a 'must' strategy to enhance customer service and improve sales. In this study, a fashion mix-and-match expert system is developed to provide customers with professional and systematic mix-and-match recommendations automatically. The system can capture the knowledge and emulate the decisions of fashion designers on apparel coordination and its knowledge base can store the literal form of information. A set of attributes of the apparel for coordination are identified and formulated; their corresponding importance is also defined with designers' opinions using ordered weighted averaging operators. The Fashion Coordination Satisfaction Index is devised and computed using the fuzzy screening approach to represent the satisfaction degree of the coordinating pairs of apparel product items. The experimental results demonstrate that the proposed system can generate effective mix-and-match recommendations and is now integrated with a smart dressing system used effectively in a fashion chain store company in Hong Kong.
- Expert systems
- Fuzzy screening
- Multi-criteria decision-making
ASJC Scopus subject areas
- Artificial Intelligence
- Computer Science Applications