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.