| Title | A Literature Review on Recent Advances in E-Commerce Recommender Systems |
| Research Area | Computer Engineering |
| Abstract | Recommender systems have become a cornerstone of modern e-commerce, enabling platforms to provide personalized shopping experiences and improve customer engagement. In the past five years, research has moved beyond classical collaborative and content-based filtering toward advanced architectures powered by deep learning, graph neural networks, reinforcement learning, and multimodal fusion. Recent scholarship has also emphasized the importance of fairness, explainability, and privacy, signaling a broader shift toward trustworthy artificial intelligence. This literature review synthesizes contributions from 2020 to 2025, organizes them into thematic categories, evaluates their benefits and shortcomings, and identifies future directions |
| Keywords | Literature review, recommender systems, e-commerce, deep learning, graph neural networks, reinforcement learning, multimodal recommendation, fairness |
| Paper Status | Published |
| Volume | 2 |
| Issue | 2 |
| Published On | 25/08/2025 |
| Published File |
IJSRTD_4834.pdf
|