A Data-Driven Review of Machine Learning Techniques for E-commerce Product Recommendation Systems

In today’s digital economy, recommendation systems are essential for enhancing customer experience and driving e-commerce growth. This study presents a comparative, quality-ranked review of machine learning-based product recommendation techniques, evaluating five key approaches: association rule mining, content-based filtering, collaborative filtering, knowledge-based systems, and hybrid models. Using a systematic literature review of 44 peer-reviewed publications across major publishers, the analysis includes geographic and publisher-wise trends and a structured quality assessment rubric. Results highlight hybrid systems as the most promising strategy, offering superior accuracy, diversity, and personalization while addressing cold-start, sparsity, and scalability challenges. Each technique’s strengths, limitations, and practical deployment considerations are critically examined to support evidence-based decision-making. The study concludes by recommending hybrid approaches tailored to domain-specific needs, offering actionable insights for both researchers and industry practitioners seeking effective and adaptable recommendation systems.