Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Book Chapter Faculty Publications- Book Chapter Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Haseena, Shaik Valli; Jaswani, Neha Title A name given to the resource Generative AI for Next-Generation Recommender Systems: Architectures, Applications, and Future Directions Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2026 Source A related resource from which the described resource is derived Next-Generation Recommendation Systems: A Comprehensive Guide to Enabling Technologies and Tools and their Business Benefits;pp.201-223 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1002/9781394351572.ch9" target="_blank" rel="noreferrer noopener">https://doi.org/10.1002/9781394351572.ch9</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105038510500?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105038510500?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Haseena S.V., Presidency College, Christ University, Bengaluru, India; Jaswani N., Presidency College, Bengaluru, India Description An account of the resource The recommender systems have become a must in delivering personalized experiences across digital platforms. Still, traditional approaches, such as collaborative and content-based filtering, suffer from some inherent limitations: data sparsity, scalability, and dynamic user adaptation. In this context, generative AI emerges as a game-changing solution empowered by state-of-the-art models like variational autoencoders (VAEs), generative adversarial networks (GANs), and transformers to overcome the above-mentioned limitations. These models make possible the synthesis of user-item interaction data, uncovering latent patterns and providing context-aware recommendations, thereby redefining personalization in recommender systems. This chapter provides a detailed survey on the role of generative AI in recommender systems, their components, architectures, and applications. Case studies in e-commerce, entertainment, and education provide insights into how generative models help drive personalization, tackle the cold-start problem, and adapt dynamically to the evolution of user behaviors. Nevertheless, open issues regarding computational complexity, privacy protection, and ethical considerations remain. To address these, the chapter outlines the future enhancements in the areas of federated learning for privacy-preserving collaboration, multimodal data integration for holistic user profiling, and explainable AI frameworks to foster transparency and trust. Bridging these gaps would let generative AI-driven recommenders further revolutionize personalization, scalability, and inclusiveness, opening up a way to innovative solutions across the board in various industries. 2026 by John Wiley &amp; Sons Inc. All rights reserved. Subject The topic of the resource Data sparsity; Explainable AI; Federated learning; Generative adversarial networks; Generative AI; Personalization; Recommender systems; Transformers; Variational autoencoders Publisher An entity responsible for making the resource available wiley Relation A related resource ISBN: 978-139435157-2; 978-139435154-1; Language A language of the resource English Type The nature or genre of the resource Book chapter Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online