{"product_id":"next-generation-recommendation-systems-a-comprehensive-guide-to-enabling-technologies-and-tools-and-their-business-benefits-hardback-9781394351541","title":"Next-Generation Recommendation Systems; A Comprehensive Guide to Enabling Technologies and Tools and their Business Benefits (Hardback) 9781394351541","description":"\u003cfont face=\"Georgia\"\u003e\r\n\u003cp\u003e\u003cfont size=\"6\"\u003eNext-Generation Recommendation Systems\u003c\/font\u003e\u003cbr\u003e\r\n\u003cfont size=\"5\"\u003eA Comprehensive Guide to Enabling Technologies and Tools and their Business Benefits\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\r\n\r\n\u003cp\u003e\u003cfont size=\"4\"\u003ePethuru Raj Chelliah (Edited by), Pethuru Raj Chelliah (Author), E. Chandra Blessie (Edited by), B. Sundaravadivazhagan (Edited by), Preetha Evangeline (Edited by)\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e9781394351541, Wiley\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eHardback, published 17 April 2026\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e640 pages\u003cbr\u003e23.1 x 16.3 x 4.1 cm, 1.111 kg\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\r\n\r\n\r\n\u003cp align=\"justify\"\u003e\u003cstrong\u003e\u003cfont size=\"3\"\u003e\u003cp\u003e\u003cb\u003eA detailed guide to building cutting-edge recommendation systems\u003c\/b\u003e \u003c\/p\u003e\n\u003cp\u003eIn \u003ci\u003eNext-Generation Recommendation Systems: A Comprehensive Guide to Enabling Technologies and Tools and their Business Benefits\u003c\/i\u003e, 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. \u003c\/p\u003e\n\u003cp\u003eThe 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. \u003c\/p\u003e\n\u003cul\u003e\n\u003cli\u003e Comprehensive coverage of practical generative AI techniques, including large language models and diffusion models\u003c\/li\u003e\n\u003cli\u003e Detailed exploration of graph neural networks and knowledge graph embeddings to solve common recommendation engine problems\u003c\/li\u003e\n\u003cli\u003e Practical guidance on implementing generative adversarial networks and variational autoencoders to address mode collapse and information bottleneck challenges\u003c\/li\u003e\n\u003cli\u003e In-depth analysis of hybrid recommendation architectures that combine content-based, collaborative, and knowledge-based filtering\u003c\/li\u003e\n\u003c\/ul\u003e \u003cp\u003eReal-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. \u003c\/p\u003e\n\u003cp\u003ePerfect for data scientists, AI specialists, software engineers, architects, and graduate students, \u003ci\u003eNext-Generation Recommendation Systems \u003c\/i\u003eis an essential, up-to-date resource for everyone involved in the design, deployment, and optimization of recommendation systems that connect to large, complex datasets.\u003c\/p\u003e\u003c\/font\u003e\u003c\/strong\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e\u003cp\u003eAbout the Editors xxxii\u003c\/p\u003e \u003cp\u003eList of Contributors xxxiv\u003c\/p\u003e \u003cp\u003e\u003cb\u003e1 Describing Decisive Digital Transformation Technologies and Tools 1\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eMamta\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e1.1 Introduction 1\u003c\/p\u003e \u003cp\u003e1.2 Core Infrastructure Technologies 4\u003c\/p\u003e \u003cp\u003e1.3 Development Frameworks and Tools 7\u003c\/p\u003e \u003cp\u003e1.4 Real-Time Processing and Deployment 9\u003c\/p\u003e \u003cp\u003e1.5 Implementation Strategies 12\u003c\/p\u003e \u003cp\u003e1.6 Future Trends and Conclusions 14\u003c\/p\u003e \u003cp\u003eReferences 17\u003c\/p\u003e \u003cp\u003e\u003cb\u003e2 Delineating the Big Data Era and the Information Overload Problem 21\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eSreekumar Vobugari and Shaurya Jauhari\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e2.1 Introduction: The Twin Challenges of Big Data 21\u003c\/p\u003e \u003cp\u003e2.2 Defining the Big Data Era 24\u003c\/p\u003e \u003cp\u003e2.3 The Nature of Information Overload in the Big Data Context 27\u003c\/p\u003e \u003cp\u003e2.4 Psychological and Cognitive Impacts of Information Overload 28\u003c\/p\u003e \u003cp\u003e2.5 Strategies and Technologies for Mitigation 33\u003c\/p\u003e \u003cp\u003e2.6 Case Studies and Examples 36\u003c\/p\u003e \u003cp\u003e2.7 Conclusion: Navigating the Information Deluge 39\u003c\/p\u003e \u003cp\u003eReferences 41\u003c\/p\u003e \u003cp\u003e\u003cb\u003e3 Expounding Collaborative Filtering-Based Recommendation System 47\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eB. Sri Bhavan Prakath, B. Senthilkumar, and M. Sujithra\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e3.1 Introduction 47\u003c\/p\u003e \u003cp\u003e3.2 Methodology 48\u003c\/p\u003e \u003cp\u003e3.3 Results and Analysis 50\u003c\/p\u003e \u003cp\u003e3.4 Types of Collaborative Filtering 51\u003c\/p\u003e \u003cp\u003e3.5 Why Collaborative Filtering Is Used? 52\u003c\/p\u003e \u003cp\u003e3.6 Advantages of Collaborative Filtering 52\u003c\/p\u003e \u003cp\u003e3.7 Ethical Considerations in Recommendation Systems 53\u003c\/p\u003e \u003cp\u003e3.8 Advanced Techniques in Collaborative Filtering 54\u003c\/p\u003e \u003cp\u003e3.9 Challenges and Risks in Recommendation Systems 54\u003c\/p\u003e \u003cp\u003e3.10 System Architecture and Design 57\u003c\/p\u003e \u003cp\u003e3.11 Machine Learning Models for Recommendation Systems 57\u003c\/p\u003e \u003cp\u003e3.12 Performance Optimization Techniques 58\u003c\/p\u003e \u003cp\u003e3.13 Database Design and Management 59\u003c\/p\u003e \u003cp\u003e3.14 Implementing A\/B Testing in User Experience Design 59\u003c\/p\u003e \u003cp\u003e3.15 Scalability and Load Balancing Strategies 60\u003c\/p\u003e \u003cp\u003e3.16 Design Thinking 61\u003c\/p\u003e \u003cp\u003e3.17 What Tools Were Used? 63\u003c\/p\u003e \u003cp\u003e3.18 How Design Thinking Affected this Chapter? 63\u003c\/p\u003e \u003cp\u003e3.19 Common Challenges in Design Thinking Implementation 64\u003c\/p\u003e \u003cp\u003e3.20 How it has Been Solved? 65\u003c\/p\u003e \u003cp\u003e3.21 Impact of Design Thinking on Customer Experience 65\u003c\/p\u003e \u003cp\u003e3.22 Future Improvements Based on Inference 66\u003c\/p\u003e \u003cp\u003e3.23 Conclusion 67\u003c\/p\u003e \u003cp\u003eReferences 68\u003c\/p\u003e \u003cp\u003e\u003cb\u003e4 Illuminating Knowledge Graph–Based Recommendation Solutions 69\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eB. Rajalingam, A. Ruba, and N. Balasubramanian\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e4.1 Introduction 69\u003c\/p\u003e \u003cp\u003e4.2 Foundations of Knowledge Graphs 70\u003c\/p\u003e \u003cp\u003e4.3 Comparison with Traditional Databases 73\u003c\/p\u003e \u003cp\u003e4.4 Examples of Real-World Knowledge Graphs 75\u003c\/p\u003e \u003cp\u003e4.5 KG-Based Recommendation Methodologies 79\u003c\/p\u003e \u003cp\u003e4.6 Real-World Applications of KG-Based Recommendations 85\u003c\/p\u003e \u003cp\u003e4.7 Challenges and Ethical Considerations in KG-Based Recommendations 88\u003c\/p\u003e \u003cp\u003eReferences 91\u003c\/p\u003e \u003cp\u003e\u003cb\u003e5 Next Level Recommendation Systems: Harnessing the Power of GANs 97\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eGnanasankaran Natarajan, Susai Rathinam Raja, Devika Govindhan, and Rakesh Gnanasekaran\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e5.1 A Brief Overview of Generative Adversarial Networks 97\u003c\/p\u003e \u003cp\u003e5.2 Catalytic Potential on GANs in Recommendation Systems 98\u003c\/p\u003e \u003cp\u003e5.3 A Broader View on the Traditional Recommendation Systems 100\u003c\/p\u003e \u003cp\u003e5.4 Unique Strengths of GANs in Addressing the Limitations of Traditional Recommendation Systems 103\u003c\/p\u003e \u003cp\u003e5.5 Key Architectures and Modifications of GAN for Recommendation Systems 107\u003c\/p\u003e \u003cp\u003e5.6 Other Notable GAN-Based Architectures for Recommendation Systems 110\u003c\/p\u003e \u003cp\u003e5.7 Real-World Applications of GANs in E-Commerce, Streaming Platforms, and Personalized Marketing 110\u003c\/p\u003e \u003cp\u003e5.8 Future Directions in GAN-Based Recommendation Systems 114\u003c\/p\u003e \u003cp\u003e5.9 Conclusion 117\u003c\/p\u003e \u003cp\u003eReferences 118\u003c\/p\u003e \u003cp\u003e\u003cb\u003e6 Graph Neural Networks in Recommendation Systems for Superior User Experiences 121\u003cbr\u003e \u003c\/b\u003e\u003ci\u003ePriyansha Upadhyay and P.K. Nizar Banu\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e6.1 Introduction 121\u003c\/p\u003e \u003cp\u003e6.2 Background 124\u003c\/p\u003e \u003cp\u003e6.3 Graph Neural Network Architectures 128\u003c\/p\u003e \u003cp\u003e6.4 Challenges Addressed by GNNs 133\u003c\/p\u003e \u003cp\u003e6.5 Industry Applications of GNNs in Recommendation Systems 134\u003c\/p\u003e \u003cp\u003e6.6 Implementation Strategies 137\u003c\/p\u003e \u003cp\u003e6.7 Evaluation Metrics for GNN-Based Recommendation Systems 143\u003c\/p\u003e \u003cp\u003e6.8 Conclusion 145\u003c\/p\u003e \u003cp\u003eReferences 148\u003c\/p\u003e \u003cp\u003e\u003cb\u003e7 Generative AI for Next Generation Recommendation System 151\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eSunil Sharma, Sandip Das, Yashwant Singh Rawal, and Prashant Sharma\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e7.1 Introduction to Growth of Digital Content and User Engagement 151\u003c\/p\u003e \u003cp\u003e7.2 Overview of Generative AI Technologies 155\u003c\/p\u003e \u003cp\u003e7.3 Enabling Tools and Frameworks 158\u003c\/p\u003e \u003cp\u003e7.4 Methodology 159\u003c\/p\u003e \u003cp\u003e7.5 Hybrid Integration 164\u003c\/p\u003e \u003cp\u003e7.6 Advantages of Generative AI for RSs 165\u003c\/p\u003e \u003cp\u003e7.7 Proposed Framework for Next-Generation RSs 168\u003c\/p\u003e \u003cp\u003e7.8 Conclusion and Future Directions 171\u003c\/p\u003e \u003cp\u003eReferences 172\u003c\/p\u003e \u003cp\u003e\u003cb\u003e8 MindGraphFusion Method to Enhance Multi-Behavior Recommendation System for Cognitive Decision 175\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eD. Mythili and S. Rajasekaran\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e8.1 Introduction 175\u003c\/p\u003e \u003cp\u003e8.2 Literature Review 177\u003c\/p\u003e \u003cp\u003e8.3 Materials and Methods 180\u003c\/p\u003e \u003cp\u003e8.4 Proposed Methodology 183\u003c\/p\u003e \u003cp\u003e8.5 Results and Discussion 190\u003c\/p\u003e \u003cp\u003e8.6 Conclusion 193\u003c\/p\u003e \u003cp\u003e8.7 Future Scope 194\u003c\/p\u003e \u003cp\u003eReferences 194\u003c\/p\u003e \u003cp\u003e\u003cb\u003e9 Generative AI for Next-Generation Recommender Systems: Architectures, Applications, and Future Directions 201\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eShaik Valli Haseena and Neha Jaswani\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e9.1 Introduction 201\u003c\/p\u003e \u003cp\u003e9.2 Components of Generative AI for Recommender Systems 204\u003c\/p\u003e \u003cp\u003e9.3 Architectures and Techniques 208\u003c\/p\u003e \u003cp\u003e9.4 Conclusion 220\u003c\/p\u003e \u003cp\u003e9.5 Future Enhancements 221\u003c\/p\u003e \u003cp\u003eReferences 222\u003c\/p\u003e \u003cp\u003e\u003cb\u003e10 Bayesian Networks (BNs) for Recommendation Systems 225\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eKetan Sarvakar, Kaushik Rana, and Chandrakant Patel\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e10.1 Introduction 225\u003c\/p\u003e \u003cp\u003e10.2 Overview of Bayesian Networks 230\u003c\/p\u003e \u003cp\u003e10.3 Recommendation Systems: Types and Challenges 232\u003c\/p\u003e \u003cp\u003e10.4 Bayesian Networks in Recommendation Systems 233\u003c\/p\u003e \u003cp\u003e10.5 Evaluation of BN-Based Recommendation Systems 237\u003c\/p\u003e \u003cp\u003e10.6 Challenges and Limitations of BNs in RS 239\u003c\/p\u003e \u003cp\u003e10.7 Future Directions 243\u003c\/p\u003e \u003cp\u003e10.8 Conclusion 245\u003c\/p\u003e \u003cp\u003eReferences 246\u003c\/p\u003e \u003cp\u003e\u003cb\u003e11 Diffusion Models – Based Recommendation Systems 253\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eElakkiya Elango, Sundaravadivazhagan Balasubaramanian, Shreenidhi Krishnamurthy Subramaniyan, and Harishchander Anandaram\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e11.1 Introduction 253\u003c\/p\u003e \u003cp\u003e11.2 Understanding Diffusion Models 255\u003c\/p\u003e \u003cp\u003e11.3 Assessment of Diffusion-Based Recommenders’ Performance 263\u003c\/p\u003e \u003cp\u003e11.4 Use Cases of Diffusion Models and Recommendation Systems 266\u003c\/p\u003e \u003cp\u003e11.5 Conclusion 268\u003c\/p\u003e \u003cp\u003eReferences 268\u003c\/p\u003e \u003cp\u003e\u003cb\u003e12 Deep Learning for Personalized Recommendations: Overcoming Traditional Challenges 271\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eBeena Suresh Gaikwad, Jitha Janardhanan, and Arghya Das Dev\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e12.1 Introduction 271\u003c\/p\u003e \u003cp\u003e12.2 Traditional Methods of Recommendation 274\u003c\/p\u003e \u003cp\u003e12.3 Deep Learning for Recommendation Systems 278\u003c\/p\u003e \u003cp\u003e12.4 Recurrent Neural Networks in Recommendation Systems 285\u003c\/p\u003e \u003cp\u003e12.5 Convolutional Neural Networks in Content-Based Recommendation Systems 287\u003c\/p\u003e \u003cp\u003e12.6 Architecture, Training, and Appraisal of Deep Learning Models for Recommendations 289\u003c\/p\u003e \u003cp\u003e12.7 Emerging Trends in Deep Learning-Based Recommendation Systems 294\u003c\/p\u003e \u003cp\u003e12.8 Transformers in Recommendation Systems 296\u003c\/p\u003e \u003cp\u003e12.9 Image Recommendation 298\u003c\/p\u003e \u003cp\u003e12.10 Text Recommendation 298\u003c\/p\u003e \u003cp\u003e12.11 Eight Real World Applications 299\u003c\/p\u003e \u003cp\u003e12.12 Conclusion 300\u003c\/p\u003e \u003cp\u003eReferences 300\u003c\/p\u003e \u003cp\u003e\u003cb\u003e13 Dual-Stream Context-Aware GANs for Next-Generation Recommendation Systems 303\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eVankayala Chethan Prakash, Raveendranadh Bokka, Aruchamy Prasanth, and Mariya Ouaissa\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e13.1 Introduction 303\u003c\/p\u003e \u003cp\u003e13.2 Existing Recommendation Techniques 308\u003c\/p\u003e \u003cp\u003e13.3 Generative Models in Recommendation Systems 312\u003c\/p\u003e \u003cp\u003e13.4 Proposed Framework 316\u003c\/p\u003e \u003cp\u003e13.5 Training and Optimization of DSC-GAN 323\u003c\/p\u003e \u003cp\u003e13.6 Hypothesis and Case Study 327\u003c\/p\u003e \u003cp\u003e13.7 Result Analysis 330\u003c\/p\u003e \u003cp\u003e13.8 Applications and Case Studies of DSC-GAN 331\u003c\/p\u003e \u003cp\u003e13.9 Conclusions 333\u003c\/p\u003e \u003cp\u003eReferences 333\u003c\/p\u003e \u003cp\u003e\u003cb\u003e14 Revolutionizing Recommendations with LLMs: Intelligent, Adaptive, and Context-Aware Systems 337\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eM.K. Vidhyalakshmi, A.V. Allin Geo, Aswathy K. Cherian, and Sundaravadivazhagan Balasubaramanian\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e14.1 Harnessing Large Language Models for Intelligent Recommendations 337\u003c\/p\u003e \u003cp\u003e14.2 Personalized Insights: Leveraging LLMs for Smarter Suggestions 338\u003c\/p\u003e \u003cp\u003e14.3 Use Cases of LLM-Powered Recommendations 339\u003c\/p\u003e \u003cp\u003e14.4 Challenges and Considerations 344\u003c\/p\u003e \u003cp\u003e14.5 Context-Aware Recommendations with Large Language Models 344\u003c\/p\u003e \u003cp\u003e14.6 Future of Context-Aware Recommendations 347\u003c\/p\u003e \u003cp\u003e14.7 Applications of LLM-Driven Predictions 348\u003c\/p\u003e \u003cp\u003e14.8 Challenges and Considerations 349\u003c\/p\u003e \u003cp\u003e14.9 Transforming Recommendation Systems with Generative AI 349\u003c\/p\u003e \u003cp\u003e14.10 Applications of Generative AI in Recommendation Systems 351\u003c\/p\u003e \u003cp\u003e14.11 Challenges and Considerations 351\u003c\/p\u003e \u003cp\u003e14.12 Adaptive Learning in Recommendations: The Role of LLMs 352\u003c\/p\u003e \u003cp\u003e14.13 Natural Language Understanding for Next-Gen Recommendations 353\u003c\/p\u003e \u003cp\u003e14.14 Enhancing Personalized Discovery with LLMs 356\u003c\/p\u003e \u003cp\u003e14.15 Ethical and Bias Considerations in LLM-Based Recommendations 357\u003c\/p\u003e \u003cp\u003e14.16 Future Trends in AI-Powered Recommendation Systems 359\u003c\/p\u003e \u003cp\u003eReferences 360\u003c\/p\u003e \u003cp\u003e\u003cb\u003e15 Evaluating Recommendation Algorithms: A Case Study on Online News Platforms 363\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eAlvin Nishant, J Alamelu Mangai, Mohammadi Akheela Khanum, and B Meenu\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e15.1 Introduction 363\u003c\/p\u003e \u003cp\u003e15.2 Literature Review 363\u003c\/p\u003e \u003cp\u003e15.3 Methodology 368\u003c\/p\u003e \u003cp\u003e15.4 Results and Discussion 375\u003c\/p\u003e \u003cp\u003e15.5 Analysis of Cold-Start Problem in Recommendation Systems 378\u003c\/p\u003e \u003cp\u003e15.6 Algorithm Computational Complexity and Scalability in Recommendation Systems 379\u003c\/p\u003e \u003cp\u003e15.7 Ethical and Bias Considerations in Recommendation Systems 379\u003c\/p\u003e \u003cp\u003e15.8 Conclusion and Future Work 380\u003c\/p\u003e \u003cp\u003eReferences 381\u003c\/p\u003e \u003cp\u003e\u003cb\u003e16 Recommendation Systems: Applications, Challenges, Ethics, and Future Directions 385\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eElakkiya Elango, Gnanasankaran Natarajan, Harishchander Anandaram, and Shreenidhi Krishnamurthy Subramaniyan\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e16.1 Introduction 385\u003c\/p\u003e \u003cp\u003e16.2 Types of Recommendation Systems 387\u003c\/p\u003e \u003cp\u003e16.3 Applications of Recommendation Systems 390\u003c\/p\u003e \u003cp\u003e16.4 Challenges in Recommendation Systems 394\u003c\/p\u003e \u003cp\u003e16.5 Conclusion 402\u003c\/p\u003e \u003cp\u003eReferences 403\u003c\/p\u003e \u003cp\u003e\u003cb\u003e17 Beyond Prediction: Generative AI as the Engine of Future Recommender Systems 407\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eBalan Senthilkumaran, Karthikeyan Sowndarya, N. Mahendran, and Pham Chien Thang\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e17.1 Introduction 407\u003c\/p\u003e \u003cp\u003e17.2 Progress of Recommender Systems 410\u003c\/p\u003e \u003cp\u003e17.3 GenAI in Recommender Systems 414\u003c\/p\u003e \u003cp\u003e17.4 Key Enabling Technologies and Tools 417\u003c\/p\u003e \u003cp\u003e17.5 Challenges and Ethical Considerations 419\u003c\/p\u003e \u003cp\u003e17.6 Use Cases and Open Research Areas 423\u003c\/p\u003e \u003cp\u003e17.7 Conclusion 425\u003c\/p\u003e \u003cp\u003eReferences 425\u003c\/p\u003e \u003cp\u003e\u003cb\u003e18 Enhanced Heart Disease Prediction using GANLSTM and GANSWOT – Augmented Data and Machine Learning 427\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eRitu Aggarwal and Eshaan Aggarwal\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e18.1 Introduction 427\u003c\/p\u003e \u003cp\u003e18.2 Objectives of Current Study 428\u003c\/p\u003e \u003cp\u003e18.3 Literature Review 430\u003c\/p\u003e \u003cp\u003e18.4 Results and Discussions 434\u003c\/p\u003e \u003cp\u003e18.5 Conclusions and Feature Work 442\u003c\/p\u003e \u003cp\u003eReferences 443\u003c\/p\u003e \u003cp\u003e\u003cb\u003e19 AI-Powered Recommendation System for Intelligent Lesson Planning 447\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eKanagaraj Karuppiah\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e19.1 Introduction 447\u003c\/p\u003e \u003cp\u003e19.2 Need for Intelligent Lesson Planning 453\u003c\/p\u003e \u003cp\u003e19.3 System Design and Implementation 455\u003c\/p\u003e \u003cp\u003e19.4 Results and Analysis 459\u003c\/p\u003e \u003cp\u003e19.5 Conclusion 462\u003c\/p\u003e \u003cp\u003eReferences 462\u003c\/p\u003e \u003cp\u003e\u003cb\u003e20 Graph Neural Networks for Enhanced Customer Segmentation in Next-Generation Recommendation Systems 465\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eNandhini Citibabu and Ayyanathan Natarajan\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e20.1 Introduction 465\u003c\/p\u003e \u003cp\u003e20.2 Literature Review 467\u003c\/p\u003e \u003cp\u003e20.3 Research Methodology 470\u003c\/p\u003e \u003cp\u003e20.4 Results and Discussion 473\u003c\/p\u003e \u003cp\u003e20.5 Evaluation Metrics 481\u003c\/p\u003e \u003cp\u003e20.6 Conclusion 482\u003c\/p\u003e \u003cp\u003eReferences 483\u003c\/p\u003e \u003cp\u003e\u003cb\u003e21 Intelligent Recommendation Systems: Bridging Next-Gen AI, Knowledge Engineering, and User-Centric Innovation 487\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eGaganpreet Kaur, Amandeep Kaur, Ramandeep Sandhu, Astha jain, Indu Rani, and Deepika Ghai\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e21.1 Introduction 487\u003c\/p\u003e \u003cp\u003e21.2 Intersection of AI with Sustainable Development 490\u003c\/p\u003e \u003cp\u003e21.3 Next-Generation Recommendation Systems 493\u003c\/p\u003e \u003cp\u003e21.4 Various Techniques for Recommendation Systems 495\u003c\/p\u003e \u003cp\u003e21.5 Applications of Recommendation Systems in Sustainability 497\u003c\/p\u003e \u003cp\u003e21.6 Challenges in Implementing Sustainable Recommendation Systems 499\u003c\/p\u003e \u003cp\u003e21.7 Future Directions and Innovations 501\u003c\/p\u003e \u003cp\u003e21.8 Conclusion 503\u003c\/p\u003e \u003cp\u003eReferences 505\u003c\/p\u003e \u003cp\u003e\u003cb\u003e22 Navigating Big Data: From Volume to Value in Next-Gen Recommendation Systems 509\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eN. Balasubramanian, A. Ruba, B. Rajalingam, and A. Manjula\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e22.1 Introduction 509\u003c\/p\u003e \u003cp\u003e22.2 The Advent and Ascendance of Big Data 512\u003c\/p\u003e \u003cp\u003e22.3 The Information Overload Challenge 516\u003c\/p\u003e \u003cp\u003e22.4 Mitigating Information Overload: Strategies and Solutions 521\u003c\/p\u003e \u003cp\u003e22.5 Ethical and Societal Implications 527\u003c\/p\u003e \u003cp\u003e22.6 Conclusion 529\u003c\/p\u003e \u003cp\u003eReferences 532\u003c\/p\u003e \u003cp\u003e\u003cb\u003e23 Architectures, Advancements, and Real-World Implementations of Deep Learning-Based Recommendation Systems 543\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eS. Janani, Rajendran Bhojan, and R. Kumuthaveni\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e23.1 Introduction 543\u003c\/p\u003e \u003cp\u003e23.2 Evolution of Recommendation Systems 544\u003c\/p\u003e \u003cp\u003e23.3 Optimization Techniques to Improve Recommendation Systems 550\u003c\/p\u003e \u003cp\u003e23.4 Real-Time Updates 561\u003c\/p\u003e \u003cp\u003e23.5 API Development for Recommendation Model 562\u003c\/p\u003e \u003cp\u003e23.6 Case Study and Real-World Recommendation Systems 565\u003c\/p\u003e \u003cp\u003e23.7 Conclusion 567\u003c\/p\u003e \u003cp\u003eReferences 567\u003c\/p\u003e \u003cp\u003e\u003cb\u003e24 Deep Learning for Recommender Systems: A Comparative Analysis of RNN, LSTM, and GRU on MovieLens and Educational Data 571\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eHasna Mahmoud, Es-said Boulmane, Mohamed Badouch, Omar Zaioudi, Mohamed Ouhssini, and Mehdi Boutaounte\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e24.1 Introduction 571\u003c\/p\u003e \u003cp\u003e24.2 Related Works 572\u003c\/p\u003e \u003cp\u003e24.3 Materials and Methods 576\u003c\/p\u003e \u003cp\u003e24.4 Results and Discussion 584\u003c\/p\u003e \u003cp\u003e24.5 Conclusion 586\u003c\/p\u003e \u003cp\u003eReferences 587\u003c\/p\u003e \u003cp\u003eIndex 591\u003c\/p\u003e\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eSubject Areas: Electronics \u0026amp; communications engineering [\u003ca title=\"See our other books on Electronics \u0026amp; communications engineering\" href=\"https:\/\/freshlyprintedbooks.co.uk\/search?q=%22Electronics%20\u0026amp;%20communications%20engineering%20%5BTJ%5D%22\"\u003eTJ\u003c\/a\u003e]\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\u003c\/font\u003e","brand":"Wiley","offers":[{"title":"Brand New","offer_id":52433823596824,"sku":"9781394351541","price":86.85,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0730\/2037\/5320\/files\/9781394351541.jpg?v=1784854211","url":"https:\/\/freshlyprintedbooks.co.uk\/products\/next-generation-recommendation-systems-a-comprehensive-guide-to-enabling-technologies-and-tools-and-their-business-benefits-hardback-9781394351541","provider":"Freshly Printed Books","version":"1.0","type":"link"}