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Hardcover. Zustand: new. Hardcover. 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 books 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. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.…

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Hardcover. Zustand: Brand New. 310 pages. 9.00x6.00x1.00 inches. In Stock.

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Hardcover. Zustand: Brand New. 368 pages. 6.40x1.60x9.10 inches. In Stock.

Hardcover. Zustand: Brand New. 368 pages. 6.40x1.60x9.10 inches. In Stock.

Hardcover. Zustand: new. Hardcover. 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 books 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. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.…

Zustand: New. Brand New! Fast Delivery This is an International Edition and ship within 24-48 hours. Deliver by FedEx and Dhl, & Aramex, UPS, & USPS and we do accept APO and PO BOX Addresses. Order can be delivered worldwide within 6-10 days and we do have flat rate for up to 2LB. Extra shipping charges will be requested if the Book weight is more than 5 LB. This Item May be shipped from India, United states & United Kingdom. Depending on your location and availability.…

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Hardcover. Zustand: new. Hardcover. 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 books 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. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.…

Zustand: New. 2026. 1st Edition. hardcover. . . . . . Books ship from the US and Ireland.

Gebunden. Zustand: New. Provides in-depth descriptions of digital transformation technologies and tools Covers various research accomplishments in this flourishing field of relevance Includes many detailed industry use cases with all the right informatio.

Buch. Zustand: Neu. 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.…

Zustand: New. 2026. 1st Edition. hardcover. . . . . .

Zustand: new. Questo è un articolo print on demand.

Buch. Zustand: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -The Digital Twin Paradigm for Smarter Systems and Environments: The Industry Use Cases, Volume 117, the latest volume in the Advances in Computers series, presents detailed coverage of new advancements in computer hardware, software, theory, design and applications. Chapters vividly illustrate how the emerging discipline of digital twin is strategically contributing to various digital transformation initiatives. Specific chapters cover Demystifying the Digital Twin Paradigm, Digital Twin Technology for 'Smarter Manufacturing', The Fog Computing/ Edge Computing to leverage Digital Twin, The industry use cases for the Digital Twin idea, Enabling Digital Twin at the Edge, The Industrial Internet of Things (IIOT), and much more. Englisch.…

Hardcover. Zustand: Brand New. 310 pages. 9.00x6.00x1.00 inches. In Stock. This item is printed on demand.