Next-Generation Recommendation Systems
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.
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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.
Content
About the Editors xxxii
List of Contributors xxxiv
1 Describing Decisive Digital Transformation Technologies and Tools 1
Mamta
2 Delineating the Big Data Era and the Information Overload Problem 21
Sreekumar Vobugari and Shaurya Jauhari
3 Expounding Collaborative Filtering-Based Recommendation System 47
B. Sri Bhavan Prakath, B. Senthilkumar, and M. Sujithra
4 Illuminating Knowledge Graph-Based Recommendation Solutions 69
B. Rajalingam, A. Ruba, and N. Balasubramanian
5 Next Level Recommendation Systems: Harnessing the Power of GANs 97
Gnanasankaran Natarajan, Susai Rathinam Raja, Devika Govindhan, and Rakesh Gnanasekaran
6 Graph Neural Networks in Recommendation Systems for Superior User Experiences 121
Priyansha Upadhyay and P.K. Nizar Banu
7 Generative AI for Next Generation Recommendation System 151
Sunil Sharma, Sandip Das, Yashwant Singh Rawal, and Prashant Sharma
8 MindGraphFusion Method to Enhance Multi-Behavior Recommendation System for Cognitive Decision 175
D. Mythili and S. Rajasekaran
9 Generative AI for Next-Generation Recommender Systems: Architectures, Applications, and Future Directions 201
Shaik Valli Haseena and Neha Jaswani
10 Bayesian Networks (BNs) for Recommendation Systems 225
Ketan Sarvakar, Kaushik Rana, and Chandrakant Patel
11 Diffusion Models-Based Recommendation Systems 253
Elakkiya Elango, Sundaravadivazhagan Balasubaramanian, Shreenidhi Krishnamurthy Subramaniyan, and Harishchander Anandaram
12 Deep Learning for Personalized Recommendations: Overcoming Traditional Challenges 271
Beena Suresh Gaikwad, Jitha Janardhanan, and Arghya Das Dev
13 Dual-Stream Context-Aware GANs for Next-Generation Recommendation Systems 303
Vankayala Chethan Prakash, Raveendranadh Bokka, Aruchamy Prasanth, and Mariya Ouaissa
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
15 Evaluating Recommendation Algorithms: A Case Study on Online News Platforms 363
Alvin Nishant, J Alamelu Mangai, Mohammadi Akheela Khanum, and B Meenu
16 Recommendation Systems: Applications, Challenges, Ethics, and Future Directions 385
Elakkiya Elango, Gnanasankaran Natarajan, Harishchander Anandaram, and Shreenidhi Krishnamurthy Subramaniyan
17 Beyond Prediction: Generative AI as the Engine of Future Recommender Systems 407
Balan Senthilkumaran, Karthikeyan Sowndarya, N. Mahendran, and Pham Chien Thang
18 Enhanced Heart Disease Prediction using GANLSTM and GANSWOT - Augmented Data and Machine Learning 427
Ritu Aggarwal and Eshaan Aggarwal
19 AI-Powered Recommendation System for Intelligent Lesson Planning 447
Kanagaraj Karuppiah
20 Graph Neural Networks for Enhanced Customer Segmentation in Next-Generation Recommendation Systems 465
Nandhini Citibabu and Ayyanathan Natarajan
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
22 Navigating Big Data: From Volume to Value in Next-Gen Recommendation Systems 509
N. Balasubramanian, A. Ruba, B. Rajalingam, and A. Manjula
23 Architectures, Advancements, and Real-World Implementations of Deep Learning-Based Recommendation Systems 543
S. Janani, Rajendran Bhojan, and R. Kumuthaveni
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
References 587
Index 591
