Scalable Recommender Systems with Generative AI

In recent years, the rise of generative artificial intelligence (AI) has transformed various domains, including the development of scalable recommender systems.This paper explores the integration of generative AI techniques into recommendation frameworks, addressing the limitations of traditional collaborative filtering and contentbased approaches.By leveraging generative models, such as variational autoencoders and generative adversarial networks, we propose a novel architecture that enhances personalization and scalability.The generative AI approach facilitates the synthesis of diverse user profiles and item characteristics, enabling the system to generate nuanced recommendations that adapt to dynamic user preferences over time.Our methodology incorporates unsupervised learning techniques to analyze vast datasets efficiently, allowing for real-time updates and improved response times.Furthermore, we evaluate the proposed system through extensive experiments on multiple datasets, demonstrating significant improvements in recommendation accuracy and user satisfaction compared to existing methods.The findings indicate that generative AI not only streamlines the recommendation process but also expands the system's ability to handle large-scale environments with heterogeneous user behavior.This paper contributes to the ongoing discourse on AI-driven solutions for enhancing user experience in digital platforms and offers insights into future research directions for optimizing recommender systems using generative methodologies