Next-Generation Recommendation Systems
A Comprehensive Guide to Enabling Technologies and Tools and their Business Benefits
Seiten
2026
John Wiley & Sons Inc (Verlag)
978-1-394-35154-1 (ISBN)
John Wiley & Sons Inc (Verlag)
978-1-394-35154-1 (ISBN)
- Noch nicht erschienen (ca. August 2026)
- Versandkostenfrei
- Auch auf Rechnung
- Artikel merken
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.
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.
| Erscheint lt. Verlag | 27.8.2026 |
|---|---|
| Verlagsort | New York |
| Sprache | englisch |
| Themenwelt | Technik ► Elektrotechnik / Energietechnik |
| ISBN-10 | 1-394-35154-2 / 1394351542 |
| ISBN-13 | 978-1-394-35154-1 / 9781394351541 |
| Zustand | Neuware |
| Informationen gemäß Produktsicherheitsverordnung (GPSR) | |
| Haben Sie eine Frage zum Produkt? |
Mehr entdecken
aus dem Bereich
aus dem Bereich
Grundlagen, Systemtechnik und Analysen ausgeführter Beispiele …
Buch | Softcover (2025)
Springer Vieweg (Verlag)
CHF 55,95
Wegweiser für Elektrofachkräfte
Buch | Hardcover (2024)
VDE VERLAG
CHF 67,20
