The rapid growth of e-commerce platforms has led to overwhelming product choices for consumers, necessitating intelligent recommendation systems to enhance user experience and business performance. Traditional collaborative filtering and content-based methods, while effective, face challenges such as sparsity, scalability, and cold-start problems. Recent advances in deep learning have enabled more powerful, flexible, and scalable approaches to personalized recommendations by capturing complex user–item interactions and contextual patterns. This paper explores deep learning–driven recommendation frameworks including neural collaborative filtering, recurrent and transformer-based sequence models, and graph neural networks for user–item relationship modeling. We present a conceptual architecture for personalized e-commerce recommendations, discuss data preprocessing and feature engineering strategies, and highlight evaluation metrics for offline and online performance. The study underscores the potential of deep learning to deliver highly accurate, adaptive, and context-aware recommendations, while addressing limitations such as bias, interpretability, and computational overhead. Keywords: E-commerce, Personalized recommendation, Deep learning, Collaborative filtering, Content-based filtering, Neural collaborative filtering, Sequence modeling, Transformer networks, Graph neural networks
P. Beaulah Sucharitha (Sat,) studied this question.