Machine Learning–Based Recommendation Systems for E-Commerce Platforms: A Comprehensive Review
Keywords:
Recommendation Systems, Machine Learning, E-Commerce, Personalization
Abstract
Abstract. Recommendation systems have become a key component of
modern e-commerce platforms. Machine learning techniques enable intelligent
product suggestions based on user behavior and purchase history.
This paper presents a comprehensive review of machine learning–based
recommendation systems used in e-commerce platforms. The study explores
collaborative filtering, content-based filtering, hybrid models, and
deep learning approaches. Furthermore, the paper analyzes evaluation
metrics, system architectures, and challenges such as data sparsity and
cold-start problems. The review highlights that machine learning models
significantly improve recommendation accuracy, user engagement, and
online sales performance.
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B.N.K.: Recommendation system for e-commerce using collaborative filtering.
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13. Policarpo, L.M., da Silveira, D.E., da Rosa Righi, R., Antunes, R.S.,
da Costa, C.A., Barbosa, J.L.V., Scorsatto, R., Arcot, T.: Machine learning
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Lesion-aware ordinal transformer for diabetic retinopathy classification from fun-dus images. Biomedical and Pharmacology Journal 19(1) (2026)
2. Aneta, P.M., Pakula, M., Borowska, B.: Recommendation systems in ecommerce
applications with machine learning methods pp. 720–725 (06 2025).
https://doi.org/10.1145/3756681.3757082
3. Ather, D., Kanday, R., Rakhra, M., Kler, R., Aggarwal, G., Jairath, K.: Adaptive
intrusion detection systems: Leveraging machine learning for real-time threat
mitigation. In: 2025 International Conference on Networks and Cryptology
(NETCRYPT). pp. 1743–1746. IEEE (2025)
4. Bodduluri, K.C., Palma, F., Kurti, A., Jusufi, I., Löwenadler, H.: Exploring
the landscape of hybrid recommendation systems in e-commerce:
A systematic literature review. IEEE Access 12, 28273–28296 (01 2024).
https://doi.org/10.1109/access.2024.3365828
5. Gangadharan, K., Kanagasabai, M., Purandaran, A., Subramanian, B., Jeyaraj,
R., Jung, S.K.: From data to decisions: The transformational
power of machine learning in business recommendations. arXiv (02 2024).
https://doi.org/10.1109/access.2025.3532697
6. Gupta, S., Hamid, A.B.A., Nyamasvisva, T.E., Tyagi, N., Jain, V., Mun, N.K.,
Ather, D.: Enhanced agricultural decision-making: Machine learning approaches
for crop prediction and analysis in india. Jurnal Online Informatika 10(2), 407–
417 (2025)
7. Kler, R., Nag, B.C., Saraswat, E., Ather, D., Singh, G., Kler, R.: Modeling and
forecasting natural rate of interest for uzbekistan through bayesian filtering and
machine learning model. In: 2025 International Conference on Technology Enabled
Economic Changes (InTech). pp. 599–605. IEEE (2025)
8. Kler, R., Kler, R., Nag, B.C., Saraswat, E., Ather, D., Singh, G.: Enhancing short
run exchange rate forecasting for uzbeki soum: From econometric models to machine
learning-based hybrid approaches. In: 2025 International Conference on Technology
Enabled Economic Changes (InTech). pp. 584–590. IEEE (2025)
9. Marigowda, C., Moldovan, A.N., Siddig, A., Muntean, C.H., Pathak, P., Stynes, P.:
A novel hybrid machine learning framework to recommend e-commerce products
(06 2023). https://doi.org/10.1145/3606843.3606853
10. Nag, B.C., Saraswat, E., Ather, D., Singh, G., Kler, R., Kler, R.: Dynamic interactions
between inflation, poverty, gdp, and unemployment: Insights from econometric
and machine learning approaches. In: 2025 International Conference on
Technology Enabled Economic Changes (InTech). pp. 591–598. IEEE (2025)
11. Nagraj, S., Palayyan, B.P.: Personalized e-commerce based recommendation systems
using deep-learning techniques. IAES International Journal of Artificial Intelligence
13, 610–618 (12 2023). https://doi.org/10.11591/ijai.v13.i1.pp610-618
12. Patil, P., Kadam, S., Aruna, E.R., More, A.J., Balajee, R.M., Rao,
B.N.K.: Recommendation system for e-commerce using collaborative filtering.
Journal Européen des Systèmes Automatisés 57, 1145–1153 (08 2024).
https://doi.org/10.18280/jesa.570421
13. Policarpo, L.M., da Silveira, D.E., da Rosa Righi, R., Antunes, R.S.,
da Costa, C.A., Barbosa, J.L.V., Scorsatto, R., Arcot, T.: Machine learning
through the lens of e-commerce initiatives: An up-to-date systematic
literature review. Computer Science Review 41, 100414 (06 2021).
https://doi.org/10.1016/j.cosrev.2021.100414
14. Raji, M.A., Olodo, H.B., Oke, T.T., Addy, W.A., Ofodile, O.C., Oyewole, A.T.:
E-commerce and consumer behavior: A review of ai-powered personalization
and market trends. GSC Advanced Research and Reviews 18, 66–77 (03 2024).
https://doi.org/10.30574/gscarr.2024.18.3.0090
Published
2025-10-10
How to Cite
Shukhrat Makhmudov. (2025). Machine Learning–Based Recommendation Systems for E-Commerce Platforms: A Comprehensive Review. MATRIX Academic International Online Journal Of Engineering And Technology, 8(2), 1-15. Retrieved from https://maiojet.com/index.php/matrix/article/view/98
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Articles
