- Home
- Archives
- 2025
- Vol. 5 Issue 5
- Article
Personalized E-Commerce Product Recommendation System Using Machine Learning
Authors
1Student, MCA, Deccan College of Engineering and Technology, Hyderabad, Telangana, India. 2Associate Professor, MCA, Deccan College of Engineering and Technology, Hyderabad, Telangana, India.
Article Information
- Published In
- Volume 5, Issue 5 (2025)
- Published Online
- September-October 2025
- Pages
- 41-46
- Article Type
- Original Article
References
1. Y. Koren and R. Bell, “Advances in collaborative filtering for recommender systems,” IEEE Intell. Syst., vol. 34, no. 4, pp. 23–32, Jul.–Aug. 2019.
2. H. Wang, F. Zhang, J. Wang, M. Zhao, W. Li, X. Xie, and M. Guo, “RippleNet: Propagating user preferences on the knowledge graph for recommender systems,” ACM Trans. Inf. Syst., vol. 38, no. 4, pp. 1–23, Aug. 2020.
3. L. Sun, C. Ma, and X. Li, “A hybrid recommendation system based on deep learning for personalized e-commerce,” IEEE Access, vol. 7, pp. 175–188, Jan. 2019.
4. J. Zhang, C. Wang, and X. Chen, “Explainable recommendation with knowledge graphs for transparency in e-commerce,” Proc. IEEE ICDE, pp. 1210–1221, Apr. 2020.
5. S. Wang, L. Zhang, and X. Zhou, “Personalized recommendation based on attention mechanism and user behavior modeling,” IEEE Access, vol. 8, pp. 422–435, 2020.
6. Y. Xu, Z. Chen, and H. Wang, “Neural collaborative filtering with context-aware embeddings for recommender systems,” Proc. IEEE ICDM, pp. 520–529, Dec. 2019.
7. R. Yang, W. Huang, and Q. Li, “Sequential recommendation with transformer networks in e-commerce,” IEEE Trans. Neural Netw. Learn. Syst., vol. 32, no. 12, pp. 5162–5175, Dec. 2021.
8. M. Zhao, X. Xie, and H. Li, “DeepFM: A factorization-machine based neural network for CTR prediction,” Proc. AAAI/IEEE AIES, pp. 1725–1732, 2020.
9. T. Chen, S. Li, and Y. Luo, “Personalized product recommendation via multi-task learning with graph neural networks,” Proc. IEEE Big Data, pp. 1650–1659, Dec. 2020
10. K. Zhou, Y. Wang, and J. Lin, “Interactive recommender systems with reinforcement learning for e-commerce,” IEEE Trans. Knowl. Data Eng., vol. 34, no. 8, pp. 3760–3774, Aug. 2022.
11. H. Liu, Q. Gao, and Y. Yin, “Context-aware session-based recommendation using recurrent neural networks,” IEEE Access, vol. 8, pp. 4895–4908, 2020.
2. H. Wang, F. Zhang, J. Wang, M. Zhao, W. Li, X. Xie, and M. Guo, “RippleNet: Propagating user preferences on the knowledge graph for recommender systems,” ACM Trans. Inf. Syst., vol. 38, no. 4, pp. 1–23, Aug. 2020.
3. L. Sun, C. Ma, and X. Li, “A hybrid recommendation system based on deep learning for personalized e-commerce,” IEEE Access, vol. 7, pp. 175–188, Jan. 2019.
4. J. Zhang, C. Wang, and X. Chen, “Explainable recommendation with knowledge graphs for transparency in e-commerce,” Proc. IEEE ICDE, pp. 1210–1221, Apr. 2020.
5. S. Wang, L. Zhang, and X. Zhou, “Personalized recommendation based on attention mechanism and user behavior modeling,” IEEE Access, vol. 8, pp. 422–435, 2020.
6. Y. Xu, Z. Chen, and H. Wang, “Neural collaborative filtering with context-aware embeddings for recommender systems,” Proc. IEEE ICDM, pp. 520–529, Dec. 2019.
7. R. Yang, W. Huang, and Q. Li, “Sequential recommendation with transformer networks in e-commerce,” IEEE Trans. Neural Netw. Learn. Syst., vol. 32, no. 12, pp. 5162–5175, Dec. 2021.
8. M. Zhao, X. Xie, and H. Li, “DeepFM: A factorization-machine based neural network for CTR prediction,” Proc. AAAI/IEEE AIES, pp. 1725–1732, 2020.
9. T. Chen, S. Li, and Y. Luo, “Personalized product recommendation via multi-task learning with graph neural networks,” Proc. IEEE Big Data, pp. 1650–1659, Dec. 2020
10. K. Zhou, Y. Wang, and J. Lin, “Interactive recommender systems with reinforcement learning for e-commerce,” IEEE Trans. Knowl. Data Eng., vol. 34, no. 8, pp. 3760–3774, Aug. 2022.
11. H. Liu, Q. Gao, and Y. Yin, “Context-aware session-based recommendation using recurrent neural networks,” IEEE Access, vol. 8, pp. 4895–4908, 2020.
Related Articles
Share Article
*Instagram doesn't support direct link sharing from web. Copy the link and share it in your Instagram story or post.