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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

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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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  1. Akter, N., Hoque, A. H. M. S., Mustafa, R., & Chowdhury, M. S. (2017). Accuracy analysis of recommendation system using singular value decomposition. 19th International Conference on Computer and Information Technology, ICCIT 2016, 405–408.
  2. Bokde, D., Girase, S., & Mukhopadhyay, D. (2015). Matrix Factorization model in Collaborative Filtering algorithms: A survey. Procedia Computer Science, 49(1), 136–146.
  3. Guan, X., Li, C. T., & Guan, Y. (2016). Enhanced SVD for collaborative filtering. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 9652 LNAI, 503–514.
  4. He, X., Zhang, H., Kan, M. Y., & Chua, T. S. (2016). Fast matrix factorization for online recommendation with implicit feedback. SIGIR 2016 - Proceedings of the 39th International ACM SIGIR Conference on Research and Development in Information Retrieval, 549–558.
  5. Prasetyo, B., Haryanto, H., Astuti, S., Astuti, E. Z., & Rahayu, Y. (2019). 2019_View of Implementasi Metode Item-Based Collaborative Filtering dalam Pemberian Rekomendasi Calon Pembeli Aksesoris Smartphone.pdf. Jurnal Eksplora Informatika, 9(1), 17–27
  6. Vegeborn, V. H., & Rahmani, H. (2017). Comparison and Improvement Of Collaborative Filtering Algorithms Comparison and Improvement Of Collaborative Filtering Algorithms Victor Hansjons Vegeborn
  7. Vozalis, M. G., & Margaritis, K. G. (2007). Using SVD and demographic data for the enhancement of generalized Collaborative Filtering. Information Sciences, 177(15), 3017–3037.
  8. Wang, J., Han, P., Miao, Y., & Zhang, F. (2019). A Collaborative Filtering Algorithm Based on SVD and Trust Factor. 88(Cnci), 33–39.
  9. Zhang, S., Wang, W., Ford, J., Makedon, F., & Pearlman, J. (2005). Using singular value decomposition approximation for collaborative filtering. Proceedings - Seventh IEEE International Conference on E-Commerce Technology, CEC 2005, 2005, 257–265.

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

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