Generative AI-based Explainable Personalized Hybrid Skincare Recommendation System 


Vol. 14,  No. 9, pp. 695-703, Sep.  2025
https://doi.org/10.3745/TKIPS.2025.14.9.695


  Abstract

Recently, in the cosmetics market, there is a remarkable trend of smart consumption in which consumers directly compare and analyze products suitable for their skin characteristics. However, due to the difficulty of interpreting ingredients and the limitations of information, consumers need reliable customized product recommendations and intuitive descriptions of products in the purchasing decision-making process. Therefore, this study designed and implemented a hybrid AI recommendation system that combines item-based collaborative filtering and content-based filtering by utilizing the user’s purchase history, rating pattern, product review, and ingredient information. In addition, by combining Generative AI, the system generates ingredient-based reason for recommendations, review-based keywords, and product advertisement images, thereby enhancing the explainability of recommendation results and ensuring user satisfaction. Our results showed that the proposed hybrid recommendation system achieved about 19% improvement in MAP@8(0.5199) over the conventional single filtering model and exhibiting notable advantages in both precision and explainability of recommendation. This study is expected to contribute to advancement of personalized services, the strengthening of platform competitiveness, and the improvement of purchase conversion rates in future digital commerce environments

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  Cite this article

[IEEE Style]

J. H. Jeong, S. Y. Kim, S. H. Choi, H. H. Kim, "Generative AI-based Explainable Personalized Hybrid Skincare Recommendation System," The Transactions of the Korea Information Processing Society, vol. 14, no. 9, pp. 695-703, 2025. DOI: https://doi.org/10.3745/TKIPS.2025.14.9.695.

[ACM Style]

Jin Hui Jeong, Seo Young Kim, Seon Ho Choi, and Hyon Hee Kim. 2025. Generative AI-based Explainable Personalized Hybrid Skincare Recommendation System. The Transactions of the Korea Information Processing Society, 14, 9, (2025), 695-703. DOI: https://doi.org/10.3745/TKIPS.2025.14.9.695.

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