Next-Generation Recommendation Systems - Pethuru Raj Chelliah

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Résumé :

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....

Biographie:

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....

Sommaire:

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...