Next-Generation Recommendation Systems : A Comprehensive Guide to Enabling Technologies and Tools and Their Business Benefits
Language: English
Published by Wiley Jul 2026, 2026
- Hardcover
- New
Seller: AHA-BUCH GmbH, Einbeck, GermanyAHA-BUCH GmbH
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Add to basketItem description from seller
Neuware - 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.…
Seller Inventory # 9781394351541
- Title
- Next-Generation Recommendation Systems : A Comprehensive Guide to Enabling Technologies and Tools and Their Business Benefits
- Author
- Pethuru Raj Chelliah
- Publisher
- Wiley Jul 2026
- Publication year
- 2026
- Condition
- Neu
- Binding
- Buch
- Language
- English
- ISBN 10
- 1394351542
- ISBN 13
- 9781394351541
- Item weight
- 1,102 grams
- Dimensions
- 235x161x40 mm
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.
"Synopsis" may belong to another edition of this title.
About the Author
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.
"About the title" may belong to another edition of this title.
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