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As competition in the realm of e-commerce escalates, the provision of personalized and precise shopping recommendations emerges as a pivotal strategy for e-commerce platforms striving to engage users effectively. Traditional recommendation systems often grapple with challenges such as the inability to capture intricate relationships, limited personalization, and issues concerning diversity. In response to these challenges, this study introduces cutting-edge deep learning techniques, namely Transformer models, Generative Adversarial Networks (GANs), and reinforcement learning, with the aim of bolstering the recommendation accuracy and user experience within e-commerce shopping systems.Initially, we harness Transformer models, capitalizing on their exceptional performance in processing sequential data to adeptly extract and learn representations of both product and user features. This facilitates a more profound understanding of the correlations between products and user shopping behaviors, thus empowering the system to offer more tailored recommendations.