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 Upadhyay, Priyansha; Banu, P.K. Nizar Title A name given to the resource Graph Neural Networks in Recommendation Systems for Superior User Experiences 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.121-150 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1002/9781394351572.ch6" target="_blank" rel="noreferrer noopener">https://doi.org/10.1002/9781394351572.ch6</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105038490822?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105038490822?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 Upadhyay P., Department of Computer Science, Christ University, Bangalore, India; Banu P.K.N., Department of Computer Science, Christ University, Bangalore, India Description An account of the resource Recommender systems are now an essential tool for traversing huge amounts of online information, especially as user tastes change in dynamic settings. They can be classified into different types based on their use, such as collaborative filtering and content-based recommendation. Graph Neural Networks (GNNs), however, are best suited to learn from graph-structured data and have become a revolutionary technology in building recommendation systems based on their capacity to capture intricate relationships and dependencies in graph-structured data. Unlike traditional methods, GNN-based recommendation systems capture both local and global connectivity patterns in user-item interaction graphs, enhancing prediction accuracy and robustness. This study looks at key types of Graph Neural Networks, known as GNNs, and includes Graph Convolutional Networks (GCNs), Graph Attention Networks (GATs), and GraphSAGE. Each type is good at tackling specific challenges like making personalized recommendations, handling cold-start problems, and managing large-scale data effectively. Big companies across different industries are using GNNs to enhance their services. For example, e-commerce giants like Amazon and Alibaba, and streaming platforms like Netflix and Spotify, use these networks to boost user engagement and satisfaction. The study also covers strategies for applying GNNs. This includes constructing user-item bipartite graphs, including domain-specific features, and employing contrastive learning methods to improve functionality. By combining theoretical concepts with real-world usage, we hope to offer an informed view of GNNs in recommendation systems. In addition, metrics like Precision, Recall, and Normalized Discounted Cumulative Gain (nDCG), providing implementable recommendations for future development in this fast-moving field, will be addressed. 2026 by John Wiley &amp; Sons Inc. All rights reserved. Subject The topic of the resource Graph Attention Networks (GATs); Graph Convolutional Networks (GCNs); Graph Neural Networks (GNNs); GraphSAGE; Recommendation systems 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