Recommendation systems in e-commerce applications with machine learning methods

TL;DR

This paper reviews how machine learning enhances e-commerce recommendation systems by analyzing current trends, challenges, and the effectiveness of methods like collaborative filtering, content-based filtering, and hybrid models.

Contribution

It provides a systematic literature review of 38 studies from 2013 to 2025, comparing the performance of various machine learning approaches in e-commerce recommendations.

Findings

Hybrid models show improved accuracy over single-method approaches.

Collaborative filtering remains popular but faces scalability issues.

Content-based filtering is effective for personalized recommendations.

Abstract

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.