Evaluating Non-Negative Matrix Factorization and Singular Value Decomposition for Skincare Recommendation Systems
DOI:
https://doi.org/10.34818/INDOJC.2024.9.3.983Keywords:
Matrix Factorization, Collaborative Filtering, Skincare, Non-Negative Matrix Factorization, Singular Value DecompositionAbstract
Facial skincare plays a crucial role in maintaining clean, healthy, and radiant skin. Recommendation systems, such as Collaborative Filtering and Content-Based Filtering, can help users discover suitable skincare products based on their preferences and reviews. This study compares two Matrix Factorization techniques Non-Negative Matrix Factorization (NMF) and Singular Value Decomposition (SVD) to enhance the accuracy and relevance of skincare product recommendations. The results reveal that the SVD model outperforms NMF, achieving a Mean Absolute Error (MAE) of 0.7190, Root Mean Squared Error (RMSE) of 1.0104, Precision of 0.8054, Recall of 0.8144, and an F-1 score of 0.8099. In contrast, the NMF model produced an MAE of 0.7074, RMSE of 1.1052, Precision of 0.7865, Recall of 0.7987, and an F-1 score of 0.7926. These findings demonstrate that both models provide accurate recommendations, with SVD offering more precise and relevant predictions for skincare product recommendations.
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References
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[2] Statista, “Skin Care - Indonesia,” https://www.statista.com/outlook/cmo/beauty-personal-care/skincare/indonesia.
[3] J. E. Prayogo, A. Suharso, and A. Rizal, “Analisis Perbandingan Model Matrix Factorization dan K-Nearest Neighbor dalam Mesin Rekomendasi Collaborative Berbasis Prediksi Rating,” Jurnal Informatika Universitas Pamulang, vol. 5, no. 4, p. 506, Dec. 2021, doi: 10.32493/informatika.v5i4.7379.
[4] J. Ben Schafer, J. Konstan, and J. Riedl, “Recommender systems in e-commerce,” in Proceedings of the 1st ACM conference on Electronic commerce, New York, NY, USA: ACM, Nov. 1999, pp. 158–166. doi:10.1145/336992.337035.
[5] M. H. Abdi, G. O. Okeyo, and R. W. Mwangi, “Matrix Factorization Techniques for Context-Aware Collaborative Filtering Recommender Systems: A Survey,” Computer and Information Science, vol. 11, no. 2, p. 1, Mar. 2018, doi: 10.5539/cis.v11n2p1.
[6] M. Ilhami and Suharjito, “Film recommendation systems using matrix factorization and collaborative filtering,” in 2014 International Conference on Information Technology Systems and Innovation (ICITSI), IEEE, Nov. 2014, pp. 1–6. doi: 10.1109/ICITSI.2014.7048228.
[7] H. Ahmad Adyatma, Z. Baizal, and J. Telekomunikasi, “Book Recommender System Using Matrix Factorization with Alternating Least Square Method,” Journal of Information System Research, vol. 4, no. 4, pp. 1286–1292, 2023, doi: 10.47065/josh.v4i4.3816.
[8] F. Nissa, A. H. Primandari, and A. K. Thalib, “COLLABORATIVE FILTERING APPROACH: SKINCARE PRODUCT RECOMMENDATION USING SINGULAR VALUE DECOMPOSITION (SVD),” MEDIA STATISTIKA, vol. 15, no. 2, pp. 139–150, Apr. 2023, doi: 10.14710/medstat.15.2.139-150.
[9] I. Yoshua, H. Bunyamin, and S. Si, “Pengimplementasian Sistem Rekomendasi Musik Dengan Metode Collaborative Filtering,” 2021.
[10] G. Ferio, R. Intan, and S. Rostianingsih, “Sistem Rekomendasi Mata Kuliah Pilihan Menggunakan Metode User Based Collaborative Filtering Berbasis Algoritma Adjusted Cosine Similarity.”
[11] A. A. Huda, R. Fajarudin, and A. Hadinegoro, “Sistem Rekomendasi Content-based Filtering Menggunakan TF-IDF Vector Similarity Untuk Rekomendasi Artikel Berita,” Building of Informatics, Technology and Science (BITS), vol. 4, no. 3, Dec. 2022, doi: 10.47065/bits.v4i3.2511.
[12] J. Bobadilla, F. Ortega, A. Hernando, and A. Gutiérrez, “Recommender systems survey,” Knowl Based Syst, vol. 46, pp. 109–132, Jul. 2013, doi: 10.1016/j.knosys.2013.03.012.
[13] S. A. Gunarto, E. S. Honggara, and D. D. Purwanto, “Website Sistem Rekomendasi dengan Content Based Filtering pada Produk Perawatan Kulit,” Jurnal Sistem dan Teknologi Informasi (JustIN), vol. 11, no. 3, p. 399, Jul. 2023, doi: 10.26418/justin.v11i3.59049.
[14] M. J. Pazzani, “Framework for collaborative, content-based and demographic filtering,” Artif Intell Rev, vol. 13, no. 5, pp. 393–408, 1999, doi: 10.1023/A:1006544522159/METRICS.
[15] J. L. Herlocker, J. A. Konstan, A. Borchers, and J. Riedl, “An Algorithmic Framework for Performing Collaborative Filtering,” ACM SIGIR Forum, vol. 51, no. 2, pp. 227–234, Aug. 2017, doi: 10.1145/3130348.3130372.
[16] S. Sharma, A. Sharma, Y. Sharma, and M. Bhatia, “Recommender system using hybrid approach,” in 2016 International Conference on Computing, Communication and Automation (ICCCA), IEEE, Apr. 2016, pp. 219– 223. doi: 10.1109/CCAA.2016.7813722.
[17] R. Pan et al., “One-Class Collaborative Filtering,” in 2008 Eighth IEEE International Conference on Data Mining, IEEE, Dec. 2008, pp. 502–511. doi: 10.1109/ICDM.2008.16.
[18] P. B.Thorat, R. M. Goudar, and S. Barve, “Survey on Collaborative Filtering, Content-based Filtering and Hybrid Recommendation System,” Int J Comput Appl, vol. 110, no. 4, pp. 31–36, Jan. 2015, doi:
10.5120/19308-0760.
[19] P. Resnick, N. Iacovou, M. Suchak, P. Bergstrom, and J. Riedl, “GroupLens,” in Proceedings of the 1994 ACM conference on Computer supported cooperative work - CSCW ’94, New York, New York, USA: ACM
Press, 1994, pp. 175–186. doi: 10.1145/192844.192905.
[20] B. Santosa, “Use of Hybrid Methods in Making E-commerce Product Recommendation Systems to Overcome Cold Start Problems,” Telematika, vol. 16, no. 1, Feb. 2023, doi: 10.35671/telematika.v16i1.2080.
[21] F. Wilhelm, “Matrix Factorization for Collaborative Filtering Is Just Solving an Adjoint Latent Dirichlet Allocation Model After All,” in Fifteenth ACM Conference on Recommender Systems, New York, NY, USA:
ACM, Sep. 2021, pp. 55–62. doi: 10.1145/3460231.3474266.
[22] B. Rocca, “Introduction to recommender systems,” https://towardsdatascience.com/introduction-torecommender-systems-6c66cf15ada.
[23] Xin Luo, Mengchu Zhou, Yunni Xia, and Qingsheng Zhu, “An Efficient Non-Negative MatrixFactorization-Based Approach to Collaborative Filtering for Recommender Systems,” IEEE Trans Industr Inform, vol. 10, no. 2, pp. 1273–1284, May 2014, doi: 10.1109/TII.2014.2308433.
[24] Y. Koren, R. Bell, and C. Volinsky, “Matrix Factorization Techniques for Recommender Systems,” Computer (Long Beach Calif), vol. 42, no. 8, pp. 30–37, Aug. 2009, doi: 10.1109/MC.2009.263.
[25] F. Ricci, L. Rokach, B. Shapira, and P. B. Kantor, Eds., Recommender Systems Handbook. Boston, MA: Springer US, 2011. doi: 10.1007/978-0-387-85820-3.
[26] A. Lichterfeld, A. Hauss, C. Surber, T. Peters, U. Blume-Peytavi, and J. Kottner, “Evidence-Based Skin Care,” Journal of Wound, Ostomy & Continence Nursing, vol. 42, no. 5, pp. 501–524, Sep. 2015, doi:
10.1097/WON.0000000000000162.
[27] A. Rahmawaty, “Peran Perawatan Kulit (Skincare) Yang Dapat Merawat Atau Merusak Skin Barrier,” Berkala Ilmiah Mahasiswa Farmasi Indonesia (BIMFI), vol. 7, no. 1, pp. 005–010, Sep. 2020, doi:
10.48177/bimfi.v7i1.32.
[28] L. Retno Hariatiningsih, “Penggunaan Skincare Dan Penerapan konsep Beauty 4.0 Pada Media Sosial (Studi Netnografi Wanita Pengguna Instagram),” Journal Komunikasi, vol. 11, no. 2, 2020, doi: 10.31294/jkom.
[29] D. Bokde, S. Girase, and D. Mukhopadhyay, “Matrix Factorization Model in Collaborative Filtering Algorithms: A Survey,” Procedia Comput Sci, vol. 49, pp. 136–146, 2015, doi: 10.1016/j.procs.2015.04.237.
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Published
2024-12-26
How to Cite
Ahmad Indra Nurfauzi, & Agung Toto Wibowo. (2024). Evaluating Non-Negative Matrix Factorization and Singular Value Decomposition for Skincare Recommendation Systems. Indonesian Journal on Computing (Indo-JC), 9(3). https://doi.org/10.34818/INDOJC.2024.9.3.983
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Computational and Simulation
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