# Recommendation systems in e-commerce applications with machine learning methods

arXiv: https://arxiv.org/abs/2506.17287 · HTML: https://mlsanity.com/paper/2506.17287/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 39 references that are in the map.
- First posted on arXiv 15 Jun 2025.

## Builds on (most cited first, 8 of 8)

- [Reinforcement learning based recommender systems: A survey](https://mlsanity.com/paper/2101.06286/) (2021)
- [Behavior Sequence Transformer for E-commerce Recommendation in Alibaba](https://mlsanity.com/paper/1905.06874/) (2019)
- [Manipulating Recommender Systems: A Survey of Poisoning Attacks and Countermeasures](https://mlsanity.com/paper/2404.14942/) (2024)
- [Personalized Embedding-based e-Commerce Recommendations at eBay](https://mlsanity.com/paper/2102.06156/) (2021)
- [Intelligent Classification and Personalized Recommendation of E-commerce Products Based on Machine Learning](https://mlsanity.com/paper/2403.19345/) (2024)
- [Enhancing E-Commerce Recommendation using Pre-Trained Language Model and Fine-Tuning](https://mlsanity.com/paper/2302.04443/) (2023)
- [E-Commerce Product Recommendation System based on ML Algorithms](https://mlsanity.com/paper/2407.21026/) (2024)
- [Price-guided user attention in large-scale E-commerce group recommendation](https://mlsanity.com/paper/2410.02074/) (2024)

## Abstract (arXiv)

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.