This study examines the incorporation of generative artificial intelligence (Gen-AI) into e-commerce recommendation systems. Traditional approaches, such as collaborative filtering and content-based filtering, face challenges like sparse data, cold-start issues, and changing user preferences. Gen-AI models, especially transformer-based frameworks like GPT and diffusion models, provide innovative solutions for understanding and creating personalized content. This paper reviews the progression of recommendation systems, introduces generative models, and proposes a framework that integrates Gen-AI with current recommendation strategies to enhance accuracy, diversity, and contextual relevance.