Development of a new knowledge-based fabric recommendation system by integrating the collaborative design process and multi-criteria decision support

In this paper, a perception-based fabric recommendation system is proposed to help fashion designers to select the most appropriate fabric in the design process, meeting the perception of the target consumer. The proposed methodology is based on the development of an interactive hierarchical structure, decomposing the decision problem into five levels: goal, criteria (consumers' requirements), sub evaluation criteria (fabric properties), rating scale, and alternatives, which ensures the analysis of requirements of consumers and knowledge sharing among designers. The proposed knowledge-based recommendation system includes a collaborative design process, a commonly used sensory evaluation procedure, and a computational model using the Fuzzy AHP (Analytic Hierarchy Process) and Fuzzy TOPSIS (Technique for Order Performance by Similarity to Ideal Solution) algorithms. The Fuzzy AHP method is used to structure the fabric selection problem and determine the relevant weights of the concerned criteria and corresponding components, while the Fuzzy TOPSIS method is used to evaluate the alternative fabrics based on the criteria obtained from the AHP process and to give the total final ranking of the involved fabric alternatives. Experimental results indicate that, using the proposed interactive hierarchical structure, professional knowledge of the designers can be fully extracted to ensure a high level of consumer satisfaction.