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A New Consumer Profile Definition Method Based on Fuzzy Technology and Fuzzy AHP

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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
The research in this paper aims to set up a new consumer profile definition method based on fuzzy technology and fuzzy AHP. The result of the study could be applied to garment recommendation systems for a special consumer. Consumer profiles are chosen as research objects. The fuzzy technology and fuzzy AHP are applied in this research, which aims to provide a new method of using fuzzy technology and fuzzy AHP to define consumer profiles. We define tall–short and fat–thin by fuzzy technology and set up the weights of consumer profile by fuzzy AHP methods. The fuzzy technology and fuzzy AHP are applied for building consumer profiles that can be used for a consumer-oriented intelligent garment recommendation system.
Rocznik
Strony
208--216
Opis fizyczny
Bibliogr. 14 poz.
Twórcy
autor
  • Hubei Key Laboratory of Digital Textile Equipment, Engineering Research Center of Hubei Province for Clothing Information, Wuhan Textile University, 430073 Wuhan, China
autor
  • Hubei Key Laboratory of Digital Textile Equipment, Engineering Research Center of Hubei Province for Clothing Information, Wuhan Textile University, 430073 Wuhan, China
  • GEMTEX Laboratory, ENSAIT, Roubaix, France
autor
  • Hubei Key Laboratory of Digital Textile Equipment, Engineering Research Center of Hubei Province for Clothing Information, Wuhan Textile University, 430073 Wuhan, China
autor
  • Wuhan Textile and Apparel Digital Engineering Technology Research Center, School of fashion, Wuhan Textile University, 430073 Wuhan, China
Bibliografia
  • [1] PROFILES, B. C. (2001). Using data mining methods to build customer profiles.
  • [2] Zhang, J., Zeng, X., Liu, K., Yan, H., Dong, M. (2008). Jeans knowledge base development based on sensory evaluation technology for customers’ personalized recommendation. International Journal of Clothing Science & Technology, 30(1), 101–111.
  • [3] Sun, M., Li, T., Ji, B., Jiao, Y., Tang, S. (2012). Evaluation research on assessment of clinical nursing teaching quality based on fuzzy comprehensive evaluation method. Journal of Convergence Information Technology, 7(8), 82–91.
  • [4] Civanlar, M. R., Trussell, H. J. (1986). Constructing membership functions using statistical data. Fuzzy sets and Systems, 18(1), 1–13.
  • [5] Kutlu, A. C., Ekmekçioğlu, M. (2012). Fuzzy failure modes and effects analysis by using fuzzy TOPSIS-based fuzzy AHP. Expert Systems with Applications, 39(1), 61–67.
  • [6] Zhu, Y., Ruan, D., Zeng, X., Koehl, L., Chaigneau, C. (2010). Characterization of fashion themes using fuzzy techniques for designing new human centered products. International Journal of Computational Intelligence Systems, 3(4), p. 452–460.
  • [7] Lijin, Z. K. Z. X. X. (1997). The method and applications of fuzzy AHP. Systems Engineering – -Theory & Practice, 12.
  • [8] Zhang, J., Zeng, X., Koehl, L., Dong, M. (2016). Consumer-oriented intelligent garment recommendation system. In Uncertainty Modelling in Knowledge Engineering and Decision Making: Proceedings of the 12th International FLINS Conference. World Scientific.
  • [9] Kuczmarski, M. F., Kuczmarski, R. J., Najjar, M. (2001). Effects of age on validity of self-reported height, weight, and body mass index: Findings from the Third National Health and Nutrition Examination Survey, 1988–1994. Journal of the American Dietetic Association, 101(1), 28–34.
  • [10] Eknoyan, G. (2008). Adolphe Quetelet (1796–1874)—the average man and indices of obesity. Nephrology Dialysis Transplantation, 23(1), 47–51.
  • [11] Baladad, B. M. S., Magsombol, J. V., Roxas, J. N. B., De castro, E. L., Dolot, J. A. (2016). Development of automated body mass index calculation device. International Journal of Applied Engineering Research, 11(7), 5195–5201.
  • [12] World Health Organization. (2006). Global database on body mass index.
  • [13] Feng, S., Xu, L. (1999). An intelligent decision support system for fuzzy comprehensive evaluation of urban development. Expert Systems with Applications, 16(1), 21–32.
  • [14] Zhang, J., Zeng, X., Koehl, L., Dong, M. (2018). Recommending garment products in E-shopping environment by exploiting an evolutionary knowledge base. International Journal of Computational Intelligence Systems, 11(1), 340–354.
Uwagi
Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-2b3ba5cb-589a-4adf-91fa-81420bc33eb3
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