Optimization of Machine Learning–Based Recommendation Systems on E-Commerce Platforms

Stephen Gregorius Kurnia; Muhammad Rizki Perdana; Aldian Yusup

doi:10.55927/eajmr.v5i8.284

Authors

Stephen Gregorius Kurnia Bina Nusantara University
Muhammad Rizki Perdana Sekolah Tinggi Manajemen Informatika dan Komputer IKMI Cirebon
Aldian Yusup Institut Prima Bangsa Cirebon

DOI:

https://doi.org/10.55927/eajmr.v5i8.284

Keywords:

Machine Learning, Recommendation Systems, E-Commerce.

Abstract

This study aims to analyze optimization strategies for machine learning–based recommendation systems in e-commerce environments, identify commonly applied algorithms, and examine emerging opportunities and implementation challenges. A Systematic Literature Review (SLR) was conducted following the PRISMA 2020 framework. Literature was collected from six major academic databases, covering publications from 2020 to 2025. From an initial pool of 286 records, 10 studies met the eligibility criteria and were included in the final analysis. The findings indicate that deep learning, hybrid recommendation models, sequential recommendation approaches, and large language model–based systems significantly enhance recommendation accuracy and personalization. User behavior analytics emerged as a critical factor in adaptive recommendation systems, while conversational AI and multimodal technologies represent promising future directions. Despite these advancements, issues related to scalability, explainability, fairness, and privacy remain significant challenges requiring further research and optimization.

References

Braun, V., & Clarke, V. (2022). Thematic Analysis: A Practical Guide. SAGE Publications.

Castells, P., & Jannach, D. (2023). Recommender systems: A primer. arXiv. https://arxiv.org/abs/2302.02579

Ezeife, C. I., & Karlapalepu, H. (2023). A survey of sequential pattern based e-commerce recommendation systems. Algorithms, 16(10), 467. https://doi.org/10.3390/a16100467

Felfernig, A., Tran, T. N. T., Le, V. M., Popescu, A., Uta, M., & Atas, M. (2024). Knowledge-based recommender systems: Overview and research directions. User Modeling and User-Adapted Interaction, 34(1), 1–49.

Karimova, F. (2016). A survey of e-commerce recommender systems. European Scientific Journal, 12(34), 75–89. https://doi.org/10.19044/esj.2016.v12n34p75

Kitchenham, B., & Charters, S. (2007). Guidelines for Performing Systematic Literature Reviews in Software Engineering. EBSE Technical Report, Keele University and Durham University.

Makhmudov, S. (2025). Machine learning-based recommendation systems for e-commerce platforms: A comprehensive review. MATRIX Academic International Online Journal of Engineering and Technology, 8(2), 1–12.

Page, M. J., McKenzie, J. E., Bossuyt, P. M., Boutron, I., Hoffmann, T. C., Mulrow, C. D., Shamseer, L., Tetzlaff, J. M., Akl, E. A., Brennan, S. E., Chou, R., Glanville, J., Grimshaw, J. M., Hróbjartsson, A., Lalu, M. M., Li, T., Loder, E. W., Mayo-Wilson, E., McDonald, S., ... Moher, D. (2021). The PRISMA 2020 statement: An updated guideline for reporting systematic reviews. BMJ, 372, n71. https://doi.org/10.1136/bmj.n71

Poniszewska-Maranda, A., Pakula, M., & Borowska, B. (2025). Recommendation systems in e-commerce applications with machine learning methods. arXiv. https://arxiv.org/abs/2506.17287

Portugal, I., Alencar, P., & Cowan, D. (2018). The use of machine learning algorithms in recommender systems: A systematic review. Expert Systems with Applications, 97, 205–227. https://doi.org/10.1016/j.eswa.2017.12.020

Rajpoot, C. S., Tiwari, V., & Vishwakarma, S. K. (2026). Emerging trends of recommender system for e-commerce: A comprehensive review. Discover Computing, 29(63), 1–34.

Raza, S., Rahman, M., Kamawal, S., Toroghi, A., Raval, A., Navah, F., & Kazemeini, A. (2025). A comprehensive review of recommender systems: Transitioning from theory to practice. Computer Science Review, 56, 100849.

Roy, D., & Dutta, M. (2022). A systematic review and research perspective on recommender systems. Journal of Big Data, 9(59), 1–38. https://doi.org/10.1186/s40537-022-00592-5

Salunke, T., & Nichite, U. (2022). Recommender systems in e-commerce. arXiv. https://arxiv.org/abs/2212.13910

Snyder, H. (2019). Literature review as a research methodology: An overview and guidelines. Journal of Business Research, 104, 333–339. https://doi.org/10.1016/j.jbusres.2019.07.039

Syamsuri, A. R., Arohman, R., Saputra, M. R., Ikhlash, M., & Damanik, S. K. (2025). Integration of machine learning in e-commerce: A systematic literature review on consumer behavior prediction and product recommendation. Social Sciences Insights Journal, 4(1), 45–61.

Tahir, M. R., Nazir, N., Ishaq, K., & Ahmed, S. (2025). A data-driven review of machine learning techniques for e-commerce product recommendation systems. International Journal of Innovations in Science & Technology, 7(3), 1–15.

Xiao, Y., & Watson, M. (2019). Guidance on conducting a systematic literature review. Journal of Planning Education and Research, 39(1), 93–112. https://doi.org/10.1177/0739456X17723971