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International Journal of Intelligent Data and Machine Learning

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A Deep Learning-Based Personalized Recommendation Architecture for E-Commerce Using CNN-Driven Sequential Representation Learning and Temporal User Behavior Optimization

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Dr. Priya Sharma 1 ,
4 Department of Information Technology National Institute of Technology Bhopal (MANIT) Bhopal, Madhya Pradesh, India

Abstract

E-commerce recommendation systems have evolved significantly with the integration of deep learning architectures capable of modeling complex user-item interactions. This study proposes a deep learning-based personalized recommendation architecture leveraging CNN-driven sequential representation learning and temporal user behavior optimization. The framework is designed to capture both short-term session-level preferences and long-term behavioral dependencies by integrating convolutional feature extraction with temporal dynamics modeling. Building upon advancements in neural recommender systems (He et al., 2017; Hidasi et al., 2016), and convolution-based sequential embeddings (Tang & Wang, 2018), the proposed architecture enhances representation learning efficiency for sparse and high-dimensional interaction data.

A key theoretical foundation of this research is derived from hybrid sequential modeling strategies that combine implicit and explicit feedback signals, as emphasized in prior deep neural modeling approaches for long-short term preference learning (Tran et al., 2021). The proposed model introduces a dual-path CNN structure integrated with temporal attention mechanisms to optimize recommendation accuracy under dynamic user behavior scenarios. Comparative insights from large-scale recommender system surveys further validate the necessity of integrating ranking-aware and sequence-aware learning paradigms (Gheewala et al., 2025).

Experimental reasoning demonstrates that combining CNN-based feature extraction with temporal optimization significantly improves recommendation relevance, reduces cold-start limitations, and enhances adaptability to evolving user preferences. The study contributes a scalable and interpretable architecture suitable for real-world e-commerce platforms.

Keywords

Deep learning recommender systems, CNN-based representation learning, e-commerce personalization

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.
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).
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).
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.
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.

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