Next-Generation Recommendation Systems
A Comprehensive Guide to Enabling Technologies and Tools and their Business Benefits
1st Edition
Published on 17. April 2026
Book
Hardback
640 pages
978-1-394-35154-1 (ISBN)
Description
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.
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.
More details
Language
English
Place of publication
New York
United States
Target group
Professional and scholarly
Product notice
sewn/stitched
Cloth over boards
Dimensions
Height: 235 mm
Width: 161 mm
Thickness: 40 mm
Weight
1102 gr
ISBN-13
978-1-394-35154-1 (9781394351541)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Other editions
Additional editions
Pethuru Raj Chelliah | E. Chandra Blessie | B. Sundaravadivazhagan
Next-Generation Recommendation Systems
A Comprehensive Guide to Enabling Technologies and Tools and their Business Benefits
E-Book
04/2026
1st Edition
Wiley
€111.99
Available for download
Pethuru Raj Chelliah | E. Chandra Blessie | B. Sundaravadivazhagan
Next-Generation Recommendation Systems
A Comprehensive Guide to Enabling Technologies and Tools and their Business Benefits
E-Book
04/2026
1st Edition
Wiley
€111.99
Available for download
Persons
PETHURU RAJ CHELLIAH, PhD, is Principal AI Architect in Infocion Inc., Bangalore
E. CHANDRA BLESSIE, PhD, is an Associate Professor in the Department of Computing (Artificial et al.) at the Coimbatore Institute of Technology.
B. SUNDARAVADIVAZHAGAN, PhD, is an information and communications engineering researcher and educator.
PREETHA EVANGELINE, PhD, is an experienced educator and expert in data structures, operating systems, and high-performance computing.
E. CHANDRA BLESSIE, PhD, is an Associate Professor in the Department of Computing (Artificial et al.) at the Coimbatore Institute of Technology.
B. SUNDARAVADIVAZHAGAN, PhD, is an information and communications engineering researcher and educator.
PREETHA EVANGELINE, PhD, is an experienced educator and expert in data structures, operating systems, and high-performance computing.
Content
About the Editors xxxii
List of Contributors xxxiv
1 Describing Decisive Digital Transformation Technologies and Tools 1
Mamta
1.1 Introduction 1
1.2 Core Infrastructure Technologies 4
1.3 Development Frameworks and Tools 7
1.4 Real-Time Processing and Deployment 9
1.5 Implementation Strategies 12
1.6 Future Trends and Conclusions 14
References 17
2 Delineating the Big Data Era and the Information Overload Problem 21
Sreekumar Vobugari and Shaurya Jauhari
2.1 Introduction: The Twin Challenges of Big Data 21
2.2 Defining the Big Data Era 24
2.3 The Nature of Information Overload in the Big Data Context 27
2.4 Psychological and Cognitive Impacts of Information Overload 28
2.5 Strategies and Technologies for Mitigation 33
2.6 Case Studies and Examples 36
2.7 Conclusion: Navigating the Information Deluge 39
References 41
3 Expounding Collaborative Filtering-Based Recommendation System 47
B. Sri Bhavan Prakath, B. Senthilkumar, and M. Sujithra
3.1 Introduction 47
3.2 Methodology 48
3.3 Results and Analysis 50
3.4 Types of Collaborative Filtering 51
3.5 Why Collaborative Filtering Is Used? 52
3.6 Advantages of Collaborative Filtering 52
3.7 Ethical Considerations in Recommendation Systems 53
3.8 Advanced Techniques in Collaborative Filtering 54
3.9 Challenges and Risks in Recommendation Systems 54
3.10 System Architecture and Design 57
3.11 Machine Learning Models for Recommendation Systems 57
3.12 Performance Optimization Techniques 58
3.13 Database Design and Management 59
3.14 Implementing A/B Testing in User Experience Design 59
3.15 Scalability and Load Balancing Strategies 60
3.16 Design Thinking 61
3.17 What Tools Were Used? 63
3.18 How Design Thinking Affected this Chapter? 63
3.19 Common Challenges in Design Thinking Implementation 64
3.20 How it has Been Solved? 65
3.21 Impact of Design Thinking on Customer Experience 65
3.22 Future Improvements Based on Inference 66
3.23 Conclusion 67
References 68
4 Illuminating Knowledge Graph-Based Recommendation Solutions 69
B. Rajalingam, A. Ruba, and N. Balasubramanian
4.1 Introduction 69
4.2 Foundations of Knowledge Graphs 70
4.3 Comparison with Traditional Databases 73
4.4 Examples of Real-World Knowledge Graphs 75
4.5 KG-Based Recommendation Methodologies 79
4.6 Real-World Applications of KG-Based Recommendations 85
4.7 Challenges and Ethical Considerations in KG-Based Recommendations 88
References 91
5 Next Level Recommendation Systems: Harnessing the Power of GANs 97
Gnanasankaran Natarajan, Susai Rathinam Raja, Devika Govindhan, and Rakesh Gnanasekaran
5.1 A Brief Overview of Generative Adversarial Networks 97
5.2 Catalytic Potential on GANs in Recommendation Systems 98
5.3 A Broader View on the Traditional Recommendation Systems 100
5.4 Unique Strengths of GANs in Addressing the Limitations of Traditional Recommendation Systems 103
5.5 Key Architectures and Modifications of GAN for Recommendation Systems 107
5.6 Other Notable GAN-Based Architectures for Recommendation Systems 110
5.7 Real-World Applications of GANs in E-Commerce, Streaming Platforms, and Personalized Marketing 110
5.8 Future Directions in GAN-Based Recommendation Systems 114
5.9 Conclusion 117
References 118
6 Graph Neural Networks in Recommendation Systems for Superior User Experiences 121
Priyansha Upadhyay and P.K. Nizar Banu
6.1 Introduction 121
6.2 Background 124
6.3 Graph Neural Network Architectures 128
6.4 Challenges Addressed by GNNs 133
6.5 Industry Applications of GNNs in Recommendation Systems 134
6.6 Implementation Strategies 137
6.7 Evaluation Metrics for GNN-Based Recommendation Systems 143
6.8 Conclusion 145
References 148
7 Generative AI for Next Generation Recommendation System 151
Sunil Sharma, Sandip Das, Yashwant Singh Rawal, and Prashant Sharma
7.1 Introduction to Growth of Digital Content and User Engagement 151
7.2 Overview of Generative AI Technologies 155
7.3 Enabling Tools and Frameworks 158
7.4 Methodology 159
7.5 Hybrid Integration 164
7.6 Advantages of Generative AI for RSs 165
7.7 Proposed Framework for Next-Generation RSs 168
7.8 Conclusion and Future Directions 171
References 172
8 MindGraphFusion Method to Enhance Multi-Behavior Recommendation System for Cognitive Decision 175
D. Mythili and S. Rajasekaran
8.1 Introduction 175
8.2 Literature Review 177
8.3 Materials and Methods 180
8.4 Proposed Methodology 183
8.5 Results and Discussion 190
8.6 Conclusion 193
8.7 Future Scope 194
References 194
9 Generative AI for Next-Generation Recommender Systems: Architectures, Applications, and Future Directions 201
Shaik Valli Haseena and Neha Jaswani
9.1 Introduction 201
9.2 Components of Generative AI for Recommender Systems 204
9.3 Architectures and Techniques 208
9.4 Conclusion 220
9.5 Future Enhancements 221
References 222
10 Bayesian Networks (BNs) for Recommendation Systems 225
Ketan Sarvakar, Kaushik Rana, and Chandrakant Patel
10.1 Introduction 225
10.2 Overview of Bayesian Networks 230
10.3 Recommendation Systems: Types and Challenges 232
10.4 Bayesian Networks in Recommendation Systems 233
10.5 Evaluation of BN-Based Recommendation Systems 237
10.6 Challenges and Limitations of BNs in RS 239
10.7 Future Directions 243
10.8 Conclusion 245
References 246
11 Diffusion Models - Based Recommendation Systems 253
Elakkiya Elango, Sundaravadivazhagan Balasubaramanian, Shreenidhi Krishnamurthy Subramaniyan, and Harishchander Anandaram
11.1 Introduction 253
11.2 Understanding Diffusion Models 255
11.3 Assessment of Diffusion-Based Recommenders' Performance 263
11.4 Use Cases of Diffusion Models and Recommendation Systems 266
11.5 Conclusion 268
References 268
12 Deep Learning for Personalized Recommendations: Overcoming Traditional Challenges 271
Beena Suresh Gaikwad, Jitha Janardhanan, and Arghya Das Dev
12.1 Introduction 271
12.2 Traditional Methods of Recommendation 274
12.3 Deep Learning for Recommendation Systems 278
12.4 Recurrent Neural Networks in Recommendation Systems 285
12.5 Convolutional Neural Networks in Content-Based Recommendation Systems 287
12.6 Architecture, Training, and Appraisal of Deep Learning Models for Recommendations 289
12.7 Emerging Trends in Deep Learning-Based Recommendation Systems 294
12.8 Transformers in Recommendation Systems 296
12.9 Image Recommendation 298
12.10 Text Recommendation 298
12.11 Eight Real World Applications 299
12.12 Conclusion 300
References 300
13 Dual-Stream Context-Aware GANs for Next-Generation Recommendation Systems 303
Vankayala Chethan Prakash, Raveendranadh Bokka, Aruchamy Prasanth, and Mariya Ouaissa
13.1 Introduction 303
13.2 Existing Recommendation Techniques 308
13.3 Generative Models in Recommendation Systems 312
13.4 Proposed Framework 316
13.5 Training and Optimization of DSC-GAN 323
13.6 Hypothesis and Case Study 327
13.7 Result Analysis 330
13.8 Applications and Case Studies of DSC-GAN 331
13.9 Conclusions 333
References 333
14 Revolutionizing Recommendations with LLMs: Intelligent, Adaptive, and Context-Aware Systems 337
M.K. Vidhyalakshmi, A.V. Allin Geo, Aswathy K. Cherian, and Sundaravadivazhagan Balasubaramanian
14.1 Harnessing Large Language Models for Intelligent Recommendations 337
14.2 Personalized Insights: Leveraging LLMs for Smarter Suggestions 338
14.3 Use Cases of LLM-Powered Recommendations 339
14.4 Challenges and Considerations 344
14.5 Context-Aware Recommendations with Large Language Models 344
14.6 Future of Context-Aware Recommendations 347
14.7 Applications of LLM-Driven Predictions 348
14.8 Challenges and Considerations 349
14.9 Transforming Recommendation Systems with Generative AI 349
14.10 Applications of Generative AI in Recommendation Systems 351
14.11 Challenges and Considerations 351
14.12 Adaptive Learning in Recommendations: The Role of LLMs 352
14.13 Natural Language Understanding for Next-Gen Recommendations 353
14.14 Enhancing Personalized Discovery with LLMs 356
14.15 Ethical and Bias Considerations in LLM-Based Recommendations 357
14.16 Future Trends in AI-Powered Recommendation Systems 359
References 360
15 Evaluating Recommendation Algorithms: A Case Study on Online News Platforms 363
Alvin Nishant, J Alamelu Mangai, Mohammadi Akheela Khanum, and B Meenu
15.1 Introduction 363
15.2 Literature Review 363
15.3 Methodology 368
15.4 Results and Discussion 375
15.5 Analysis of Cold-Start Problem in Recommendation Systems 378
15.6 Algorithm Computational Complexity and Scalability in Recommendation Systems 379
15.7 Ethical and Bias Considerations in Recommendation Systems 379
15.8 Conclusion and Future Work 380
References 381
16 Recommendation Systems: Applications, Challenges, Ethics, and Future Directions 385
Elakkiya Elango, Gnanasankaran Natarajan, Harishchander Anandaram, and Shreenidhi Krishnamurthy Subramaniyan
16.1 Introduction 385
16.2 Types of Recommendation Systems 387
16.3 Applications of Recommendation Systems 390
16.4 Challenges in Recommendation Systems 394
16.5 Conclusion 402
References 403
17 Beyond Prediction: Generative AI as the Engine of Future Recommender Systems 407
Balan Senthilkumaran, Karthikeyan Sowndarya, N. Mahendran, and Pham Chien Thang
17.1 Introduction 407
17.2 Progress of Recommender Systems 410
17.3 GenAI in Recommender Systems 414
17.4 Key Enabling Technologies and Tools 417
17.5 Challenges and Ethical Considerations 419
17.6 Use Cases and Open Research Areas 423
17.7 Conclusion 425
References 425
18 Enhanced Heart Disease Prediction using GANLSTM and GANSWOT - Augmented Data and Machine Learning 427
Ritu Aggarwal and Eshaan Aggarwal
18.1 Introduction 427
18.2 Objectives of Current Study 428
18.3 Literature Review 430
18.4 Results and Discussions 434
18.5 Conclusions and Feature Work 442
References 443
19 AI-Powered Recommendation System for Intelligent Lesson Planning 447
Kanagaraj Karuppiah
19.1 Introduction 447
19.2 Need for Intelligent Lesson Planning 453
19.3 System Design and Implementation 455
19.4 Results and Analysis 459
19.5 Conclusion 462
References 462
20 Graph Neural Networks for Enhanced Customer Segmentation in Next-Generation Recommendation Systems 465
Nandhini Citibabu and Ayyanathan Natarajan
20.1 Introduction 465
20.2 Literature Review 467
20.3 Research Methodology 470
20.4 Results and Discussion 473
20.5 Evaluation Metrics 481
20.6 Conclusion 482
References 483
21 Intelligent Recommendation Systems: Bridging Next-Gen AI, Knowledge Engineering, and User-Centric Innovation 487
Gaganpreet Kaur, Amandeep Kaur, Ramandeep Sandhu, Astha jain, Indu Rani, and Deepika Ghai
21.1 Introduction 487
21.2 Intersection of AI with Sustainable Development 490
21.3 Next-Generation Recommendation Systems 493
21.4 Various Techniques for Recommendation Systems 495
21.5 Applications of Recommendation Systems in Sustainability 497
21.6 Challenges in Implementing Sustainable Recommendation Systems 499
21.7 Future Directions and Innovations 501
21.8 Conclusion 503
References 505
22 Navigating Big Data: From Volume to Value in Next-Gen Recommendation Systems 509
N. Balasubramanian, A. Ruba, B. Rajalingam, and A. Manjula
22.1 Introduction 509
22.2 The Advent and Ascendance of Big Data 512
22.3 The Information Overload Challenge 516
22.4 Mitigating Information Overload: Strategies and Solutions 521
22.5 Ethical and Societal Implications 527
22.6 Conclusion 529
References 532
23 Architectures, Advancements, and Real-World Implementations of Deep Learning-Based Recommendation Systems 543
S. Janani, Rajendran Bhojan, and R. Kumuthaveni
23.1 Introduction 543
23.2 Evolution of Recommendation Systems 544
23.3 Optimization Techniques to Improve Recommendation Systems 550
23.4 Real-Time Updates 561
23.5 API Development for Recommendation Model 562
23.6 Case Study and Real-World Recommendation Systems 565
23.7 Conclusion 567
References 567
24 Deep Learning for Recommender Systems: A Comparative Analysis of RNN, LSTM, and GRU on MovieLens and Educational Data 571
Hasna Mahmoud, Es-said Boulmane, Mohamed Badouch, Omar Zaioudi, Mohamed Ouhssini, and Mehdi Boutaounte
24.1 Introduction 571
24.2 Related Works 572
24.3 Materials and Methods 576
24.4 Results and Discussion 584
24.5 Conclusion 586
References 587
Index 591
List of Contributors xxxiv
1 Describing Decisive Digital Transformation Technologies and Tools 1
Mamta
1.1 Introduction 1
1.2 Core Infrastructure Technologies 4
1.3 Development Frameworks and Tools 7
1.4 Real-Time Processing and Deployment 9
1.5 Implementation Strategies 12
1.6 Future Trends and Conclusions 14
References 17
2 Delineating the Big Data Era and the Information Overload Problem 21
Sreekumar Vobugari and Shaurya Jauhari
2.1 Introduction: The Twin Challenges of Big Data 21
2.2 Defining the Big Data Era 24
2.3 The Nature of Information Overload in the Big Data Context 27
2.4 Psychological and Cognitive Impacts of Information Overload 28
2.5 Strategies and Technologies for Mitigation 33
2.6 Case Studies and Examples 36
2.7 Conclusion: Navigating the Information Deluge 39
References 41
3 Expounding Collaborative Filtering-Based Recommendation System 47
B. Sri Bhavan Prakath, B. Senthilkumar, and M. Sujithra
3.1 Introduction 47
3.2 Methodology 48
3.3 Results and Analysis 50
3.4 Types of Collaborative Filtering 51
3.5 Why Collaborative Filtering Is Used? 52
3.6 Advantages of Collaborative Filtering 52
3.7 Ethical Considerations in Recommendation Systems 53
3.8 Advanced Techniques in Collaborative Filtering 54
3.9 Challenges and Risks in Recommendation Systems 54
3.10 System Architecture and Design 57
3.11 Machine Learning Models for Recommendation Systems 57
3.12 Performance Optimization Techniques 58
3.13 Database Design and Management 59
3.14 Implementing A/B Testing in User Experience Design 59
3.15 Scalability and Load Balancing Strategies 60
3.16 Design Thinking 61
3.17 What Tools Were Used? 63
3.18 How Design Thinking Affected this Chapter? 63
3.19 Common Challenges in Design Thinking Implementation 64
3.20 How it has Been Solved? 65
3.21 Impact of Design Thinking on Customer Experience 65
3.22 Future Improvements Based on Inference 66
3.23 Conclusion 67
References 68
4 Illuminating Knowledge Graph-Based Recommendation Solutions 69
B. Rajalingam, A. Ruba, and N. Balasubramanian
4.1 Introduction 69
4.2 Foundations of Knowledge Graphs 70
4.3 Comparison with Traditional Databases 73
4.4 Examples of Real-World Knowledge Graphs 75
4.5 KG-Based Recommendation Methodologies 79
4.6 Real-World Applications of KG-Based Recommendations 85
4.7 Challenges and Ethical Considerations in KG-Based Recommendations 88
References 91
5 Next Level Recommendation Systems: Harnessing the Power of GANs 97
Gnanasankaran Natarajan, Susai Rathinam Raja, Devika Govindhan, and Rakesh Gnanasekaran
5.1 A Brief Overview of Generative Adversarial Networks 97
5.2 Catalytic Potential on GANs in Recommendation Systems 98
5.3 A Broader View on the Traditional Recommendation Systems 100
5.4 Unique Strengths of GANs in Addressing the Limitations of Traditional Recommendation Systems 103
5.5 Key Architectures and Modifications of GAN for Recommendation Systems 107
5.6 Other Notable GAN-Based Architectures for Recommendation Systems 110
5.7 Real-World Applications of GANs in E-Commerce, Streaming Platforms, and Personalized Marketing 110
5.8 Future Directions in GAN-Based Recommendation Systems 114
5.9 Conclusion 117
References 118
6 Graph Neural Networks in Recommendation Systems for Superior User Experiences 121
Priyansha Upadhyay and P.K. Nizar Banu
6.1 Introduction 121
6.2 Background 124
6.3 Graph Neural Network Architectures 128
6.4 Challenges Addressed by GNNs 133
6.5 Industry Applications of GNNs in Recommendation Systems 134
6.6 Implementation Strategies 137
6.7 Evaluation Metrics for GNN-Based Recommendation Systems 143
6.8 Conclusion 145
References 148
7 Generative AI for Next Generation Recommendation System 151
Sunil Sharma, Sandip Das, Yashwant Singh Rawal, and Prashant Sharma
7.1 Introduction to Growth of Digital Content and User Engagement 151
7.2 Overview of Generative AI Technologies 155
7.3 Enabling Tools and Frameworks 158
7.4 Methodology 159
7.5 Hybrid Integration 164
7.6 Advantages of Generative AI for RSs 165
7.7 Proposed Framework for Next-Generation RSs 168
7.8 Conclusion and Future Directions 171
References 172
8 MindGraphFusion Method to Enhance Multi-Behavior Recommendation System for Cognitive Decision 175
D. Mythili and S. Rajasekaran
8.1 Introduction 175
8.2 Literature Review 177
8.3 Materials and Methods 180
8.4 Proposed Methodology 183
8.5 Results and Discussion 190
8.6 Conclusion 193
8.7 Future Scope 194
References 194
9 Generative AI for Next-Generation Recommender Systems: Architectures, Applications, and Future Directions 201
Shaik Valli Haseena and Neha Jaswani
9.1 Introduction 201
9.2 Components of Generative AI for Recommender Systems 204
9.3 Architectures and Techniques 208
9.4 Conclusion 220
9.5 Future Enhancements 221
References 222
10 Bayesian Networks (BNs) for Recommendation Systems 225
Ketan Sarvakar, Kaushik Rana, and Chandrakant Patel
10.1 Introduction 225
10.2 Overview of Bayesian Networks 230
10.3 Recommendation Systems: Types and Challenges 232
10.4 Bayesian Networks in Recommendation Systems 233
10.5 Evaluation of BN-Based Recommendation Systems 237
10.6 Challenges and Limitations of BNs in RS 239
10.7 Future Directions 243
10.8 Conclusion 245
References 246
11 Diffusion Models - Based Recommendation Systems 253
Elakkiya Elango, Sundaravadivazhagan Balasubaramanian, Shreenidhi Krishnamurthy Subramaniyan, and Harishchander Anandaram
11.1 Introduction 253
11.2 Understanding Diffusion Models 255
11.3 Assessment of Diffusion-Based Recommenders' Performance 263
11.4 Use Cases of Diffusion Models and Recommendation Systems 266
11.5 Conclusion 268
References 268
12 Deep Learning for Personalized Recommendations: Overcoming Traditional Challenges 271
Beena Suresh Gaikwad, Jitha Janardhanan, and Arghya Das Dev
12.1 Introduction 271
12.2 Traditional Methods of Recommendation 274
12.3 Deep Learning for Recommendation Systems 278
12.4 Recurrent Neural Networks in Recommendation Systems 285
12.5 Convolutional Neural Networks in Content-Based Recommendation Systems 287
12.6 Architecture, Training, and Appraisal of Deep Learning Models for Recommendations 289
12.7 Emerging Trends in Deep Learning-Based Recommendation Systems 294
12.8 Transformers in Recommendation Systems 296
12.9 Image Recommendation 298
12.10 Text Recommendation 298
12.11 Eight Real World Applications 299
12.12 Conclusion 300
References 300
13 Dual-Stream Context-Aware GANs for Next-Generation Recommendation Systems 303
Vankayala Chethan Prakash, Raveendranadh Bokka, Aruchamy Prasanth, and Mariya Ouaissa
13.1 Introduction 303
13.2 Existing Recommendation Techniques 308
13.3 Generative Models in Recommendation Systems 312
13.4 Proposed Framework 316
13.5 Training and Optimization of DSC-GAN 323
13.6 Hypothesis and Case Study 327
13.7 Result Analysis 330
13.8 Applications and Case Studies of DSC-GAN 331
13.9 Conclusions 333
References 333
14 Revolutionizing Recommendations with LLMs: Intelligent, Adaptive, and Context-Aware Systems 337
M.K. Vidhyalakshmi, A.V. Allin Geo, Aswathy K. Cherian, and Sundaravadivazhagan Balasubaramanian
14.1 Harnessing Large Language Models for Intelligent Recommendations 337
14.2 Personalized Insights: Leveraging LLMs for Smarter Suggestions 338
14.3 Use Cases of LLM-Powered Recommendations 339
14.4 Challenges and Considerations 344
14.5 Context-Aware Recommendations with Large Language Models 344
14.6 Future of Context-Aware Recommendations 347
14.7 Applications of LLM-Driven Predictions 348
14.8 Challenges and Considerations 349
14.9 Transforming Recommendation Systems with Generative AI 349
14.10 Applications of Generative AI in Recommendation Systems 351
14.11 Challenges and Considerations 351
14.12 Adaptive Learning in Recommendations: The Role of LLMs 352
14.13 Natural Language Understanding for Next-Gen Recommendations 353
14.14 Enhancing Personalized Discovery with LLMs 356
14.15 Ethical and Bias Considerations in LLM-Based Recommendations 357
14.16 Future Trends in AI-Powered Recommendation Systems 359
References 360
15 Evaluating Recommendation Algorithms: A Case Study on Online News Platforms 363
Alvin Nishant, J Alamelu Mangai, Mohammadi Akheela Khanum, and B Meenu
15.1 Introduction 363
15.2 Literature Review 363
15.3 Methodology 368
15.4 Results and Discussion 375
15.5 Analysis of Cold-Start Problem in Recommendation Systems 378
15.6 Algorithm Computational Complexity and Scalability in Recommendation Systems 379
15.7 Ethical and Bias Considerations in Recommendation Systems 379
15.8 Conclusion and Future Work 380
References 381
16 Recommendation Systems: Applications, Challenges, Ethics, and Future Directions 385
Elakkiya Elango, Gnanasankaran Natarajan, Harishchander Anandaram, and Shreenidhi Krishnamurthy Subramaniyan
16.1 Introduction 385
16.2 Types of Recommendation Systems 387
16.3 Applications of Recommendation Systems 390
16.4 Challenges in Recommendation Systems 394
16.5 Conclusion 402
References 403
17 Beyond Prediction: Generative AI as the Engine of Future Recommender Systems 407
Balan Senthilkumaran, Karthikeyan Sowndarya, N. Mahendran, and Pham Chien Thang
17.1 Introduction 407
17.2 Progress of Recommender Systems 410
17.3 GenAI in Recommender Systems 414
17.4 Key Enabling Technologies and Tools 417
17.5 Challenges and Ethical Considerations 419
17.6 Use Cases and Open Research Areas 423
17.7 Conclusion 425
References 425
18 Enhanced Heart Disease Prediction using GANLSTM and GANSWOT - Augmented Data and Machine Learning 427
Ritu Aggarwal and Eshaan Aggarwal
18.1 Introduction 427
18.2 Objectives of Current Study 428
18.3 Literature Review 430
18.4 Results and Discussions 434
18.5 Conclusions and Feature Work 442
References 443
19 AI-Powered Recommendation System for Intelligent Lesson Planning 447
Kanagaraj Karuppiah
19.1 Introduction 447
19.2 Need for Intelligent Lesson Planning 453
19.3 System Design and Implementation 455
19.4 Results and Analysis 459
19.5 Conclusion 462
References 462
20 Graph Neural Networks for Enhanced Customer Segmentation in Next-Generation Recommendation Systems 465
Nandhini Citibabu and Ayyanathan Natarajan
20.1 Introduction 465
20.2 Literature Review 467
20.3 Research Methodology 470
20.4 Results and Discussion 473
20.5 Evaluation Metrics 481
20.6 Conclusion 482
References 483
21 Intelligent Recommendation Systems: Bridging Next-Gen AI, Knowledge Engineering, and User-Centric Innovation 487
Gaganpreet Kaur, Amandeep Kaur, Ramandeep Sandhu, Astha jain, Indu Rani, and Deepika Ghai
21.1 Introduction 487
21.2 Intersection of AI with Sustainable Development 490
21.3 Next-Generation Recommendation Systems 493
21.4 Various Techniques for Recommendation Systems 495
21.5 Applications of Recommendation Systems in Sustainability 497
21.6 Challenges in Implementing Sustainable Recommendation Systems 499
21.7 Future Directions and Innovations 501
21.8 Conclusion 503
References 505
22 Navigating Big Data: From Volume to Value in Next-Gen Recommendation Systems 509
N. Balasubramanian, A. Ruba, B. Rajalingam, and A. Manjula
22.1 Introduction 509
22.2 The Advent and Ascendance of Big Data 512
22.3 The Information Overload Challenge 516
22.4 Mitigating Information Overload: Strategies and Solutions 521
22.5 Ethical and Societal Implications 527
22.6 Conclusion 529
References 532
23 Architectures, Advancements, and Real-World Implementations of Deep Learning-Based Recommendation Systems 543
S. Janani, Rajendran Bhojan, and R. Kumuthaveni
23.1 Introduction 543
23.2 Evolution of Recommendation Systems 544
23.3 Optimization Techniques to Improve Recommendation Systems 550
23.4 Real-Time Updates 561
23.5 API Development for Recommendation Model 562
23.6 Case Study and Real-World Recommendation Systems 565
23.7 Conclusion 567
References 567
24 Deep Learning for Recommender Systems: A Comparative Analysis of RNN, LSTM, and GRU on MovieLens and Educational Data 571
Hasna Mahmoud, Es-said Boulmane, Mohamed Badouch, Omar Zaioudi, Mohamed Ouhssini, and Mehdi Boutaounte
24.1 Introduction 571
24.2 Related Works 572
24.3 Materials and Methods 576
24.4 Results and Discussion 584
24.5 Conclusion 586
References 587
Index 591