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COLLABORATIVE FILTERING APPROACH: SKINCARE PRODUCT RECOMMENDATION USING SINGULAR VALUE DECOMPOSITION (SVD)

Farhatun Nissa  -  Statistics Study Program, Faculty of Mathematics and Natural Science, Universitas Islam Indonesia, Indonesia
*Arum Handini Primandari orcid scopus  -  Statistics Study Program, Faculty of Mathematics and Natural Science, Universitas Islam Indonesia, Indonesia
Achmad Kurniansyah Thalib  -  Purwadika Digital Technology School, Indonesia
Open Access Copyright (c) 2022 MEDIA STATISTIKA under http://creativecommons.org/licenses/by-nc-sa/4.0.

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Abstract
The recommendation system provides recommendations for something, be it goods, songs, or movies. The term system is not limited to a service system but concerns a model that can provide recommendations. With recent technological advances, many companies provide various skincare products because current generations are increasingly aware of self-care. With various choices, someone may experience confusion in determining the product they want to buy. Therefore, we need a system that can provide product recommendations run on any platform we use. The most common method for recommendation systems often comes with Collaborating Filtering (CF) where it relies on the past user and item dataset. The singular value decomposition (SVD) method uses a matrix factorization technique that predict the user's rating based on historical ratings. The measurement of the model's accuracy is the RMSE average of 1.01276, indicating that this value results from the best parameters. The results focus on showing skincare product recommendations to users sorted based on rating predictions.
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Keywords: Collaborative filtering; Singular Value Decomposition; Skincare recommendation;

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Last update: 2024-06-20 17:25:26

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