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

Counts differ because each database indexes a different set of publications. We treat OpenAlex as the canonical count; Google Scholar is not shown (no API, and crawling it violates its ToS).

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.