Next-Generation Recommendation Systems

A Comprehensive Guide to Enabling Technologies and Tools and their Business Benefits

Pethuru Raj Chelliah, E. Chandra Blessie, B. Sundaravadivazhagan, Preetha Evangeline

• Publisher: Wiley
• Year: 2026
• Print ISBN:
  9781394351541
• Edition: 1
• eBook ISBN:
  9781394351558
• Topic: Computer Science
• Subtopic: Computer Engineering
• Index: Computer Science

A detailed guide to building cutting-edge recommendation systems

In Next-Generation Recommendation Systems: A Comprehensive Guide to Enabling Technologies and Tools and their Business Benefits, a team of experienced technologists and educators, each with a proven track record in the field, delivers an expert guide to building robust recommendation systems that can interface with complex databases. The authors' deep understanding of the subject matter is evident as they explain how to use the latest AI technologies, including LLMs, graph neural networks, diffusion models, and generative adversarial networks, to create recommendation engines that users enjoy and that drive business revenue.

The book does not just delve into theoretical concepts, but also connects them to advanced implementation techniques. It demonstrates the application of practical and adaptable techniques, such as graph embeddings and Bayesian networks, to solve real-world problems faced by platform users and businesses. Readers will find the knowledge and tools to tackle these challenges head-on.

• Comprehensive coverage of practical generative AI techniques, including large language models and diffusion models
• Detailed exploration of graph neural networks and knowledge graph embeddings to solve common recommendation engine problems
• Practical guidance on implementing generative adversarial networks and variational autoencoders to address mode collapse and information bottleneck challenges
• In-depth analysis of hybrid recommendation architectures that combine content-based, collaborative, and knowledge-based filtering

Real-world deployment strategies using cloud-native computing environments are not just theoretical concepts in this book. They are actionable strategies that have been tested and proven effective. This emphasis on real-world applicability will reassure readers about the book's relevance to their professional or academic pursuits.

Perfect for data scientists, AI specialists, software engineers, architects, and graduate students, Next-Generation Recommendation Systems is an essential, up-to-date resource for everyone involved in the design, deployment, and optimization of recommendation systems that connect to large, complex datasets.

Table of contents

• Cover
• Table of Contents
• Title Page
• Copyright
• About the Editors
• List of Contributors
• 1 Describing Decisive Digital Transformation Technologies and Tools
• 2 Delineating the Big Data Era and the Information Overload Problem
• 3 Expounding Collaborative Filtering‐Based Recommendation System
• 4 Illuminating Knowledge Graph–Based Recommendation Solutions
• 5 Next Level Recommendation Systems: Harnessing the Power of GANs
• 6 Graph Neural Networks in Recommendation Systems for Superior User Experiences
• 7 Generative AI for Next Generation Recommendation System
• 8 MindGraphFusion Method to Enhance Multi‐Behavior Recommendation System for Cognitive Decision
• 9 Generative AI for Next‐Generation Recommender Systems: Architectures, Applications, and Future Directions
• 10 Bayesian Networks (BNs) for Recommendation Systems
• 11 Diffusion Models – Based Recommendation Systems
• 12 Deep Learning for Personalized Recommendations: Overcoming Traditional Challenges
• 13 Dual‐Stream Context‐Aware GANs for Next‐Generation Recommendation Systems
• 14 Revolutionizing Recommendations with LLMs: Intelligent, Adaptive, and Context‐Aware Systems
• 15 Evaluating Recommendation Algorithms: A Case Study on Online News Platforms
• 16 Recommendation Systems: Applications, Challenges, Ethics, and Future Directions
• 17 Beyond Prediction: Generative AI as the Engine of Future Recommender Systems
• 18 Enhanced Heart Disease Prediction using GANLSTM and GANSWOT – Augmented Data and Machine Learning
• 19 AI‐Powered Recommendation System for Intelligent Lesson Planning
• 20 Graph Neural Networks for Enhanced Customer Segmentation in Next‐Generation Recommendation Systems
• 21 Intelligent Recommendation Systems: Bridging Next‐Gen AI, Knowledge Engineering, and User‐Centric Innovation
• 22 Navigating Big Data: From Volume to Value in Next‐Gen Recommendation Systems
• 23 Architectures, Advancements, and Real‐World Implementations of Deep Learning‐Based Recommendation Systems
• 24 Deep Learning for Recommender Systems: A Comparative Analysis of RNN, LSTM, and GRU on MovieLens and Educational Data
• Index
• End User License Agreement