Recommendation systems in e-commerce applications with machine learning methods
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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.
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References · 8
- 2021Reinforcement learning based recommender systems: A surveycited by 188
- 2019Behavior Sequence Transformer for E-commerce Recommendation in Alibabacited by 130
- 2024Manipulating Recommender Systems: A Survey of Poisoning Attacks and Countermeasurescited by 14
- 2021Personalized Embedding-based e-Commerce Recommendations at eBaycited by 12
- 2024Intelligent Classification and Personalized Recommendation of E-commerce Products Based on Machine Learningcited by 2
- 2023Enhancing E-Commerce Recommendation using Pre-Trained Language Model and Fine-Tuningcited by 1
- 2024E-Commerce Product Recommendation System based on ML Algorithmscited by 1
- 2024Price-guided user attention in large-scale E-commerce group recommendationcited by 1
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