DEEP LEARNING FOR E‑COMMERCE RECOMMENDATIONS: CAPTURING LONG- AND SHORT-TERM USER PREFERENCES WITH CNN-BASED REPRESENTATION LEARNING
DOI:
https://doi.org/10.55640/ijidml-v02i08-02Keywords:
Recommender Systems, Deep Learning, Representation Learning, Convolutional Neural Networks, E-Commerce, User PreferencesAbstract
Recommender systems are crucial in e-commerce for matching users with products of interest. This paper proposes a deep learning approach that leverages representation learning to capture complex patterns in user behavior, combining long-term preferences with short-term, session-based interests. We develop a neural network model that integrates Convolutional Neural Networks (CNNs) for sequential pattern extraction with user embedding vectors representing historical preferences. Experiments are conducted on open e-commerce datasets with implicit (click/view) and explicit (purchase) feedback. The proposed model outperforms baseline recommendation techniques, achieving higher Hit Rate (HR@10) and Normalized Discounted Cumulative Gain (NDCG@10). The results demonstrate that blending users’ stable long-term tastes with their recent short-term actions leads to more accurate recommendations. This work reinforces deep learning as a powerful tool for representation learning in recommender systems, and our findings offer insights into modeling nuanced user behaviors for intelligent data-driven recommendations in online retail.
References
Gheewala, S., Xu, S., & Yeom, S. (2025). In-depth survey: deep learning in recommender systems—exploring prediction and ranking models, datasets, feature analysis, and emerging trends. Neural Computing and Applications, 37(10), 10875–10947.
Tang, J., & Wang, K. (2018). Personalized top-n sequential recommendation via convolutional sequence embedding. In Proceedings of the 11th ACM International Conference on Web Search and Data Mining (WSDM) (pp. 565–573).
Tran, Q., Tran, L., Chu, L. H., Ngo, L. V., & Than, K. (2021). From implicit to explicit feedback: A deep neural network for modeling sequential behaviours and long-short term preferences of online users. arXiv preprint arXiv:2107.12325.
Tran, Q., Tran, L., Chu, L. H., Ngo, L. V., & Than, K. (2022). A deep neural network for modeling sequential behaviors and long-short term preferences of online users. Applied Soft Computing, 123, 108957.
Hidasi, B., Karatzoglou, A., Baltrunas, L., & Tikk, D. (2016). Session-based recommendations with recurrent neural networks. In Proceedings of the 4th International Conference on Learning Representations (ICLR).
He, X., Liao, L., Zhang, H., Nie, L., Hu, X., & Chua, T. (2017). Neural collaborative filtering. In Proceedings of the 26th International Conference on World Wide Web (WWW) (pp. 173–182).
Xu, K., Zhou, H., Zheng, H., Zhu, M., & Xin, Q. (2024). Intelligent classification and personalized recommendation of E-commerce products based on machine learning. Applied and Computational Engineering, 64, 147–153.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2025 Qi Xin (Author)
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors retain the copyright of their manuscripts, and all Open Access articles are disseminated under the terms of the Creative Commons Attribution License 4.0 (CC-BY), which licenses unrestricted use, distribution, and reproduction in any medium, provided that the original work is appropriately cited. The use of general descriptive names, trade names, trademarks, and so forth in this publication, even if not specifically identified, does not imply that these names are not protected by the relevant laws and regulations.