Recommendation systems in e-commerce applications with machine learning methods
It provides a review for e-commerce practitioners, but it is incremental as it synthesizes existing research without introducing new methods.
This paper conducted a systematic literature review of 38 publications to analyze current trends and challenges in e-commerce recommendation systems, evaluating the effectiveness of machine learning methods like collaborative filtering and hybrid models.
E-commerce platforms are increasingly reliant on recommendation systems to enhance user experience, retain customers, and, in most cases, drive sales. The integration of machine learning methods into these systems has significantly improved their efficiency, personalization, and scalability. This paper aims to highlight the current trends in e-commerce recommendation systems, identify challenges, and evaluate the effectiveness of various machine learning methods used, including collaborative filtering, content-based filtering, and hybrid models. A systematic literature review (SLR) was conducted, analyzing 38 publications from 2013 to 2025. The methods used were evaluated and compared to determine their performance and effectiveness in addressing e-commerce challenges.