# An Interactive Personalized Garment Design Recommendation System Using Intelligent Techniques
## 6 Conclusions  
In this research, we put forth a new interactive, personalized garment design recommendation approach based on a series of intelligent techniques, including fuzzy logic, genetic algorithms, and support vector regression. This system can be easily combined with classical 3D clothing design software (e.g., Lectra Modaris 3D, Clo 3D, Vstitcher, etc.) to form a new, interactive, personalized garment design process, enhancing the level of mass customization for various fashion brand companies. The proposed personalized design recommendation system is composed of a formalized garment design knowledge base (databases and relational models) and a garment design adaptation model. It can effectively characterize relations between consumer profiles and garment profiles to find the most relevant garment design solution. It can also be used for the estimation of the garment design technical parameters. More significantly, the self-adjusting adaptation mechanism for the garment patterns provided in the proposed system is critical in garment mass customization. The main contributions of this study can be summarized as follows: (1) the proposed approach can supply personalized garments with high accuracy and velocity, facilitating the implementation of mass customization; (2) the model proposed can not only help general consumers without a mastery of professional design knowledge to select proper personalized garments, but can also make the enterprises to control the design qualities precisely; (3) a new collaborative garment design process can be formed by combining the proposed knowledge-based system with a commercial 3D garment design software to satisfy the individualized demands of the consumer more intuitively, accurately, and promptly. Moreover, the performance of the knowledge bases and the system put forward can be enhanced progressively by supplementing new successful recommendation and design cases from the continuous implementation. Due to the length limitation of the article, the implementation of the proposed system was validated in a personalized pants design only. The performance of the proposed system should be verified further by various garment styles, such as shirts, skirts, coats, dresses, and so on. Apart from the garment styles, the factors affecting the decision making in the garment recommendation processes, such as socio-cultural backgrounds and the user's emotions, should also be fully taken into account in future research. Furthermore, the development of a personalized garment design recommendation system using more advanced intelligent techniques for higher quality recommendations constitutes one of our future research focuses. Figure 12: Comparison of the pants patterns and fitting effects. (**a**): pants patterns comparison; (**b**): pants fitting effects comparison. **Author Contributions:** Conceptualization, [PERSON] and X.Z. ; methodology, [PERSON] and Y.X. ([PERSON]); software, [PERSON], X.T., Y.X. ([PERSON]) and P.B. ; validation, [PERSON], Y.X. ([PERSON]) and Z.X. ; resources, X.Z., Z.X. and J.W. ; writing--original draft preparation, [PERSON], X.T. and Y.X. ; writing--review and editing, [PERSON], X.T. and X.Z. ; visualization, [PERSON] and Y.X. ([PERSON]); supervision, X.T. and X.Z. ; project administration, [PERSON], Y.X. ([PERSON]) and X.Z. ; funding acquisition, Z.W., Y.X. ([PERSON]), Y.X. ([PERSON]) and X.Z. All authors have read and agreed to the published version of the manuscript. **Funding:** This research was funded by the Social Science Planning Project in Anhui (grant No. AHSKQ2019D085), the Scientific Research Project of Department of Education of Zhejiang Province (grant No. Y202148250), the Open Project Program of Anhui Province College Key Laboratory of Textile Fabrics, the Anhui Engineering and Technology Research Center of Textile (grant No. 2021 AETKL04), the Key Teaching and Research project of Colleges and Universities in Anhui (grant No.