Exploring Generative AI Recommender Systems in E-Commerce: Model, Evaluation Metric, and Comparative Review

Abstract

Generative Artificial Intelligence (GAI) is changing what can be done with Recommender Systems (RS) in e-commerce by allowing much more interactive, situationally aware, and highly tailored experiences for users. The purpose of this paper is to provide overall insight into how GAI, including Large Language Models (LLMs), Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and other emerging methods, is affecting the building and running of modern e-commerce RS. This paper classifies generative models into groups based on the type of models used, data modality, and specific domain of application. Their involvement in tasks such as personalized product ranking, content generation, and cold-start problem avoidance is discussed comprehensively as well. In addition, we also analyse innovation in design trends, practical challenges, such as explainability, real-time adaptability, computational scalability, and possible trade-offs, as well as pathways ahead through the lens of current literature and empirical systems. By contrasting GAI-RS with traditional RS, we highlight their advantages in handling several problems, such as data sparsity, generating diverse recommendations, and enabling dynamic user interaction. This paper should serve to broaden awareness among scholars and practitioners about the ever-changing convergence of GAI and intelligent recommendation structures within e-commerce, emphasizing both their transformative potential and operational complexities in practice.