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Personalized clothing recommendation fusing the 4-season color system and users’ biological characteristics

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Abstract

In clothing e-commerce, the challenge of optimally recommending clothing that suits a user’s unique characteristics remains a pressing issue. Many platforms simply recommend best-selling or popular clothing, without taking into account important attributes like user’s face color, pupil color, face shape, age, etc. To solve this problem, this paper proposes a personalized clothing recommendation algorithm that incorporates the established 4-Season Color System and user-specific biological characteristics. Firstly, the attributes and colors of clothing are classified by Fnet network, that can learn disjoint label combinations and mitigate the issue of excessive labels. Secondly, on the basis of the 4-Season Color System, the user’s face color model is trained by combined MobileNetV3_DTL, which ensures the model’s generalization and improves the training speed. Thirdly, user’s face shape and age are divided into different categories by an Inception network. Finally, according to the users’ face color, age, face shape and other information, personalized clothing is recommended in a coarse-to-fine manner. Experiments on five datasets demonstrate that the algorithm proposed in this paper achieves state-of-the-art results.

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Acknowledgments

This work is supported by the National Natural Science Foundation of China (grant no. 61902301), the Natural Science Basic Research Key Program funded by Shaanxi Provincial Science and Technology Department(2022JZ-35), Shaanxi Natural Science Basic Research Project (grant no. 2022JM-394, 2022JQ-711), the Key Scientific Research Program funded by the Shaanxi Provincial Education Department (grant no. 22JS019), and Xi’an Science and Technology Bureau Science and Technology Innovation Leading Project (grant no. 21XJZZ0020 and 21XJZZ0022), and Science and Technology Guidance Program of China National Textile And Apparel Council (2020100).

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Authors and Affiliations

  1. College of Information and Technology, Northwest University of China, Xi’an, China

    Xueping Su & Jinye Peng

  2. School of Electronics and Information, Xi’an Polytechnic University, Xi’an, China

    Xueping Su, Jiawei Duan, Jie Ren & Yunhong Li

  3. Interactive and Mobile Robotics and Artificial Intelligence, Department of Engineering, Reutlingen University, Reutlingen, Germany

    Michael Danner & Matthias Rätsch

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Correspondence to Xueping Su.

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Several images in our dataset were provided by our teachers and students and may contain sensitive personal information, which makes them unsuitable for sharing.

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Su, X., Duan, J., Ren, J. et al. Personalized clothing recommendation fusing the 4-season color system and users’ biological characteristics. Multimed Tools Appl 83, 12597–12625 (2024). https://doi.org/10.1007/s11042-023-16014-4

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