# An Interactive Personalized Garment Design Recommendation System Using Intelligent Techniques
* The previous works are limited to some specific garment styles, such as shirts and jeans. They lack generalized recommendation solutions for supporting designers' work. In this context, we propose a knowledge-based approach (see Figure 1) for garment design recommendation, with a self-adjustment mechanism for predicting and controlling the style design and garment structural parameters from the consumers' personalized body measurements and requirements (consumer profile). Compared with the current systems, our approach will enable the recommendation and design of new garments in terms of accuracy, fit, and efficiency, fully taking colors, fabrics, style, and patterns into account. As one of the current computational tools for dealing with the uncertainties and ambiguities related to human factors, fuzzy techniques have been widely applied to fashion design. The concerned work includes design knowledge-based creation [10; 21; 22], fashion recommendation [8; 9; 11], human body classification [37], pattern design [25; 38; 39; 40], product evaluation [24; 28; 41; 42], garment size selection [43], and so forth. Meanwhile, genetic algorithms (GAs) have been regarded as a powerful tool for dealing with complex optimization problems due to their fast convergence and simple encoding, including human body clustering [44; 45], clothing pressure prediction [46], thermal and moisture comfort estimation [47], production planning optimization [48], and design process optimization [26; 49], and so on. Moreover, due to its easy implementation, simple network topology, and high generalized capability, support vector regression (SVR) has gained much success in the textile and fashion industry, especially in the predictions of textile dyeing process parameters [50], yarn characteristics [51; 52], fabric qualities [53], fabric contents [54; 55], and human body dimensions [56]. Therefore, the fuzzy technique, GA, and SVR were employed to construct the study's personalized garment design recommendation system. The proposed design recommendation system (see Figure 1) is mainly supported by a formalized, professional garment design knowledge base and a garment adaptation model. The knowledge base in the system (see Figure 2) is composed of a series of basic databases (style, color, fabric, and pattern) and relational models on style, color, fabric, and garment fit. The major function of these relational models is to quantitatively characterize the relations between the formalized consumer profile provided by the user and the formalized garment profile generated from combinations of the basic databases. According to the results of the relational models, if no existing garment meets the consumer profile, a self-adjustment mechanism for design parameters will be activated by using an automatic garment adaptation model (see Figure 1) until the consumer's or designer's satisfaction is reached. A satisfactory design solution will be recommended to the consumer by a 3D virtual demonstration. Meanwhile, the design knowledge base and adaptation model will be continuously updated by successful recommendation solutions recommended by the system. Finally, the performance of the proposed system will be improved progressively by generating personalized design solutions and learning the design knowledge for different business cases. The significant contributions of our work are summarized as follows:  
Figure 1: The proposed interactive and personalized garment design recommendation system. Figure 2: Hierarchical structure of the proposed garment design knowledge base.