Next-Generation Recommendation Systems : A Comprehensive Guide to Enabling Technologies and Tools and Their Business Benefits.

Format:

Book

Author/Creator:

Chelliah, Pethuru Raj.

Language:

English

Subjects (All):

Recommender systems (Information filtering).

Physical Description:

1 online resource (642 pages)

Edition:

1st ed.

Place of Publication:

Newark : John Wiley & Sons, Incorporated, 2026.

Summary:

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.

Contents:

Cover

Title Page

Copyright

Contents

About the Editors

List of Contributors

Chapter 1 Describing Decisive Digital Transformation Technologies and Tools

1.1 Introduction

1.1.1 Evolution of Recommendation Systems

1.1.2 Impact of Digital Transformation

1.1.3 Chapter Overview and Objectives

1.2 Core Infrastructure Technologies

1.2.1 Cloud Computing Platforms

1.2.2 Microservices Architecture

1.2.3 Edge Computing Solutions

1.2.4 Data Storage and Processing Systems

1.3 Development Frameworks and Tools

1.3.1 MLOps Frameworks

1.3.2 DevOps Integration

1.3.3 Model Development Tools

1.3.4 Testing and Validation Frameworks

1.4 Real-Time Processing and Deployment

1.4.1 Stream Processing Technologies

1.4.2 Model Serving Platforms

1.4.3 Monitoring Solutions

1.4.4 Performance Optimization Tools

1.5 Implementation Strategies

1.5.1 System Architecture Design

1.5.2 Scalability Considerations

1.5.3 Security and Privacy Measures

1.5.4 Case Studies and Best Practices

1.6 Future Trends and Conclusions

1.6.1 Emerging Technologies

1.6.2 Industry Directions

1.6.3 Implementation Guidelines

1.6.4 Summary and Recommendations

References

Chapter 2 Delineating the Big Data Era and the Information Overload Problem

2.1 Introduction: The Twin Challenges of Big Data

2.2 Defining the Big Data Era

2.2.1 Characteristics of Big Data (The 5 Vs, and Potentially Others)

2.2.2 Driving Forces

2.3 The Nature of Information Overload in the Big Data Context

2.4 Psychological and Cognitive Impacts of Information Overload

2.4.1 Impact on Attention Span

2.4.2 Impact on Memory

2.4.3 Impact on Decision-Making Processes

2.4.4 Impact on Mental Health

2.5 Strategies and Technologies for Mitigation

2.5.1 Distributed Data Storage and Processing.

2.5.2 Data Filtering and Prioritization

2.5.3 Information Visualization and Summarization

2.5.4 Cloud Solutions

2.5.5 Intelligent Systems and AI-Driven Solutions

2.5.6 Information Management Strategies

2.6 Case Studies and Examples

2.7 Conclusion: Navigating the Information Deluge

Chapter 3 Expounding Collaborative Filtering-Based Recommendation System

3.1 Introduction

3.2 Methodology

3.2.1 Data Collection

3.2.2 Collaborative Filtering

3.2.3 Model Training and Evaluation

3.3 Results and Analysis

3.3.1 Top-Rated Items

3.3.2 User Preference Distribution

3.4 Types of Collaborative Filtering

3.5 Why Collaborative Filtering Is Used?

3.6 Advantages of Collaborative Filtering

3.7 Ethical Considerations in Recommendation Systems

3.7.1 Privacy Concerns

3.7.2 Bias and Fairness

3.7.3 Transparency and Explainability

3.8 Advanced Techniques in Collaborative Filtering

3.8.1 Matrix Factorization Methods

3.8.2 Singular Value Decomposition (SVD)

3.8.3 Deep Learning in Collaborative Filtering

3.8.4 Graph-Based Approaches

3.9 Challenges and Risks in Recommendation Systems

3.9.1 Cold Start Problem in Recommendation Systems

3.9.2 Scalability Challenges in Large-Scale Recommendation Systems

3.9.3 Cold Start Solutions and Hybrid Approaches

3.9.4 Overfitting in Recommendation Models

3.9.5 Handling Noisy and Inconsistent Data

3.10 System Architecture and Design

3.11 Machine Learning Models for Recommendation Systems

3.12 Performance Optimization Techniques

3.13 Database Design and Management

3.14 Implementing A/B Testing in User Experience Design

3.15 Scalability and Load Balancing Strategies

3.16 Design Thinking

3.16.1 The Five Stages of Design Thinking

3.17 What Tools Were Used?

3.18 How Design Thinking Affected this Chapter?.

3.19 Common Challenges in Design Thinking Implementation

3.20 How it has Been Solved?

3.21 Impact of Design Thinking on Customer Experience

3.22 Future Improvements Based on Inference

3.23 Conclusion

Chapter 4 Illuminating Knowledge Graph-Based Recommendation Solutions

4.1 Introduction

4.2 Foundations of Knowledge Graphs

4.2.1 Definition and Core Concepts

4.2.2 Components of a Knowledge Graph

4.2.2.1 Entities

4.2.2.2 Attributes

4.2.2.3 Relationships

4.2.3 Key Characteristics of Knowledge Graphs

4.2.4 Applications of Knowledge Graphs

4.3 Comparison with Traditional Databases

4.4 Examples of Real-World Knowledge Graphs

4.4.1 Google Knowledge Graph

4.4.2 Wikidata

4.4.3 Facebook Entity Graph

4.4.4 The Microsoft Academic Graph (MAG)

4.4.5 Amazon Product Knowledge Graph

4.5 KG-Based Recommendation Methodologies

4.5.1 Enhancing Collaborative Filtering with KGs

4.5.2 Improving Content-Based Filtering using KGs

4.5.3 Hybrid Approaches for Recommendation Systems

4.5.4 Graph Embeddings for Knowledge Graphs

4.5.4.1 Why Graph Embeddings are Important

4.5.4.2 Types of Graphs Embedding Methods

4.5.5 Deep Learning and Graph Neural Networks for KG-Based Recommendations

4.6 Real-World Applications of KG-Based Recommendations

4.6.1 E-Commerce and Personalized Shopping

4.6.2 Media Streaming and Content Discovery

4.6.3 Education and Skill-Based Learning Platforms

4.7 Challenges and Ethical Considerations in KG-Based Recommendations

4.7.1 Technical Challenge

4.7.2 Scalability and Computational Complexity

4.7.3 Data Bias and Fairness in Knowledge Graphs

4.7.4 Privacy Concerns and Regulatory Compliance

Chapter 5 Next Level Recommendation Systems: Harnessing the Power of GANs.

5.1 A Brief Overview of Generative Adversarial Networks

5.1.1 Key Mechanisms of GANs

5.1.1.1 Generator

5.1.1.2 Discriminator

5.1.1.3 Adversarial Process

5.1.1.4 Loss Functions

5.2 Catalytic Potential on GANs in Recommendation Systems

5.2.1 Handling Data Sparsity

5.2.2 Cold-Start Problem

5.2.3 Personalization and Diversity

5.2.4 Implicit Feedback Modeling

5.2.5 Cross-Domain Recommendations

5.2.6 Adversarial Training for Robustness

5.3 A Broader View on the Traditional Recommendation Systems

5.3.1 Collaborative Filtering (CF)

5.3.1.1 Types of Collaborative Filtering

5.3.1.2 Strengths of Collaborative Filtering

5.3.1.3 Limitations of Collaborative Filtering

5.3.2 Content-Based Filtering (CBF)

5.3.2.1 How Content-Based Filtering Works

5.3.2.2 Strengths of Content-Based Filtering

5.3.2.3 Limitations of Content-Based Filtering

5.3.3 Hybrid Approaches

5.3.3.1 Strengths of Hybrid Approaches

5.3.3.2 Limitations of Hybrid Approaches

5.4 Unique Strengths of GANs in Addressing the Limitations of Traditional Recommendation Systems

5.4.1 Handling Data Sparsity

5.4.1.1 Synthetic Data Generation

5.4.1.2 Enriched Training Data

5.4.2 Addressing the Cold-Start Problem

5.4.2.1 Synthetic User Profiles

5.4.2.2 Synthetic Item Representations

5.4.3 Enhancing Personalization and Diversity

5.4.3.1 Exploration of Latent Space

5.4.3.2 Novelty in Recommendations

5.4.4 Modeling Implicit Feedback

5.4.4.1 Noise Robustness

5.4.4.2 Refinement of Feedback

5.4.5 Cross-Domain Recommendations

5.4.5.1 Cross-Domain Interaction Generation

5.4.5.2 Transfer Learning

5.4.6 Adversarial Training for Robustness

5.4.6.1 Robustness to Attacks

5.4.7 Handling Non-Linear and Complex Relationships

5.4.7.1 Non-Linear Modeling

5.4.8 Scalability and Efficiency.

5.4.8.1 Efficient Data Generation

5.4.8.2 Parallel Processing

5.5 Key Architectures and Modifications of GAN for Recommendation Systems

5.5.1 Adversarial Personalized Ranking (APR)

5.5.1.1 Key Components of APR

5.5.1.2 Strengths of APR

5.5.1.3 Example Use Case

5.5.2 Collaborative GAN (CollaGAN)

5.5.2.1 Key Components of CollaGAN

5.5.2.2 Strengths of CollaGAN

5.5.2.3 Example Use Case

5.6 Other Notable GAN-Based Architectures for Recommendation Systems

5.6.1 IRGAN (Information Retrieval GAN)

5.6.2 Graph GAN

5.6.3 Causal GAN

5.7 Real-World Applications of GANs in E-Commerce, Streaming Platforms, and Personalized Marketing

5.7.1 Application in E-Commerce

5.7.1.1 Personalized Product Recommendations

5.7.1.2 Visual Search and Recommendation

5.7.1.3 Virtual Try-Ons

5.7.1.4 Practical Considerations

5.7.1.5 Ethical Concerns

5.7.2 Applications in Streaming Platforms

5.7.2.1 Personalized Content Recommendations

5.7.2.2 Content Generation

5.7.2.3 Enhanced User Engagement

5.7.2.4 Practical Considerations

5.7.2.5 Ethical Concerns

5.7.3 Applications in Personalized Marketing

5.7.3.1 Targeted Advertising

5.7.3.2 Customer Segmentation

5.7.3.3 Content Creation

5.7.3.4 Practical Considerations

5.7.3.5 Ethical Concerns

5.8 Future Directions in GAN-Based Recommendation Systems

5.8.1.1 Multi-Modal Data Integration

5.8.1.2 Enhanced User Profiling

5.8.1.3 Cross-Modal Recommendations

5.8.1.4 Context-Aware Recommendations

5.8.1.5 Challenges

5.8.2 Federated Learning

5.8.2.1 Privacy-Preserving Recommendations

5.8.2.2 Personalized Recommendations

5.8.2.3 Collaborative Filtering

5.8.2.4 Challenges

5.8.3 Explainable Recommendations

5.8.3.1 Interpretable User Profiles

5.8.3.2 Transparent Recommendations

5.8.3.3 Fairness and Bias Mitigation.

5.8.3.4 Challenges.

Notes:

Description based on publisher supplied metadata and other sources.

ISBN:

1-394-35157-7

1-394-35156-9

OCLC:

1587889976