Recommender System Based on User Evaluations and Cosmetic Ingredients

Nakajima, Yoko; Honma, Hirotoshi; Aoshima, Haruka; Akiba, Tomoyoshi; Masuyama, Shigeru

doi:10.1109/incit.2019.8912051

Cited by 5 publications

(2 citation statements)

References 4 publications

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Section: Introductionmentioning

confidence: 98%

“…On the other hand, a number of researchers have focused on the analysis of cosmetic components and the evaluation of user skin conditions to more effectively respond to user demands. Nakajima et al 6 and Rubasri et al 7 incorporated cosmetic ingredient information into their cosmetic recommendation systems. They applied document term matrix (DTM) and term frequency-inverse document frequency (TF-IDF) algorithms to find patterns of ingredients in numerous cosmetic products.…”

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confidence: 99%

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Deep learning‐based skin care product recommendation: A focus on cosmetic ingredient analysis and facial skin conditions

Lee,

Yoon,

Kim

et al. 2024

J of Cosmetic Dermatology

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BackgroundRecommendations for cosmetics are gaining popularity, but they are not being made with consideration of the analysis of cosmetic ingredients, which customers consider important when selecting cosmetics.AimsThis article aims to propose a method for estimating the efficacy of cosmetics based on their ingredients and introduces a system that recommends personalized products for consumers, combined with AI skin analysis.MethodsWe constructed a deep neural network architecture to analyze sequentially arranged cosmetic ingredients in the product and incorporated skin analysis models to get the precise skin status of users from frontal face images. Our recommendation system makes decisions based on the results optimized for the individual.ResultsOur cosmetic recommendation system has shown its effectiveness through reliable evaluation metrics, and numerous examples have demonstrated its ability to make reasonable recommendations for various skin problems.ConclusionThe result shows that deep learning methods can be used to predict the effects of products based on their cosmetic ingredients and are available for use in personalized cosmetic recommendations.

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Section: Introductionmentioning

confidence: 98%

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confidence: 99%