Building Recommender Systems Using Large Language Models

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Building Recommender Systems Using Large Language Models (Paperback)

Jianqiang Wang

Edit� par Springer Nature Switzerland AG, Cham, 2025

ISBN 10 : 3032011515 ISBN 13 : 9783032011510

Neuf Paperback

Vendeur : Grand Eagle Retail, Bensenville, IL, Etats-Unis

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Paperback. Etat : new. Paperback. This book offers a comprehensive exploration of the intersection between Large Language Models (LLMs) and recommendation systems, serving as a practical guide for practitioners, researchers, and students in AI, natural language processing, and data science. It addresses the limitations of traditional recommendation techniquessuch as their inability to fully understand nuanced language, reason dynamically over user preferences, or leverage multi-modal dataand demonstrates how LLMs can revolutionize personalized recommendations. By consolidating fragmented research and providing structured, hands-on tutorials, the book bridges the gap between cutting-edge research and real-world application, empowering readers to design and deploy next-generation recommender systems.Structured for progressive learning, the book covers foundational LLM concepts, the evolution from classic to LLM-powered recommendation systems, and advanced topics including end-to-end LLM recommenders, conversational agents, and multi-modal integration. Each chapter blends theoretical insights with practical coding exercises and real-world case studies, such as fashion recommendation and generative content creation. The final chapters discuss emerging challenges, including privacy, fairness, and future trends, offering a forward-looking roadmap for research and application. Readers with a basic understanding of machine learning and NLP will find this resource both accessible and invaluable for building effective, modern recommendation systems enhanced by LLMs. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. N� de r�f. du vendeur 9783032011510

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Jianqiang Wang

Edit� par Springer, 2025

ISBN 10 : 3032011515 ISBN 13 : 9783032011510

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Etat : Brand New. New. US edition. Expediting shipping for all USA and Europe orders excluding PO Box. Excellent Customer Service. N� de r�f. du vendeur ABEOCT25-360857

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Building Recommender Systems Using Large Language Models

Wang, Jianqiang

Edit� par Springer-Nature New York Inc, 2025

ISBN 10 : 3032011515 ISBN 13 : 9783032011510

Neuf Paperback

Vendeur : Revaluation Books, Exeter, Royaume-Uni

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Paperback. Etat : Brand New. 234 pages. 9.25x6.10x8.90 inches. In Stock. N� de r�f. du vendeur __3032011515

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Building Recommender Systems Using Large Language Models

Jianqiang (Jay) Wang

Edit� par Springer, Springer Nature Switzerland Okt 2025, 2025

ISBN 10 : 3032011515 ISBN 13 : 9783032011510

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Vendeur : BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Allemagne

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Taschenbuch. Etat : Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This book offers a comprehensive exploration of the intersection between Large Language Models (LLMs) and recommendation systems, serving as a practical guide for practitioners, researchers, and students in AI, natural language processing, and data science. It addresses the limitations of traditional recommendation techniques such as their inability to fully understand nuanced language, reason dynamically over user preferences, or leverage multi-modal data and demonstrates how LLMs can revolutionize personalized recommendations. By consolidating fragmented research and providing structured, hands-on tutorials, the book bridges the gap between cutting-edge research and real-world application, empowering readers to design and deploy next-generation recommender systems.Structured for progressive learning, the book covers foundational LLM concepts, the evolution from classic to LLM-powered recommendation systems, and advanced topics including end-to-end LLM recommenders, conversational agents, and multi-modal integration. Each chapter blends theoretical insights with practical coding exercises and real-world case studies, such as fashion recommendation and generative content creation. The final chapters discuss emerging challenges, including privacy, fairness, and future trends, offering a forward-looking roadmap for research and application. Readers with a basic understanding of machine learning and NLP will find this resource both accessible and invaluable for building effective, modern recommendation systems enhanced by LLMs. 236 pp. Englisch. N� de r�f. du vendeur 9783032011510

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Building Recommender Systems Using Large Language Models

Wang, Jianqiang (Jay)

Edit� par Springer Verlag GmbH, 2025

ISBN 10 : 3032011515 ISBN 13 : 9783032011510

Neuf Couverture souple

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Vendeur : moluna, Greven, Allemagne

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Etat : New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. N� de r�f. du vendeur 2495082411

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Building Recommender Systems Using Large Language Models (Paperback)

Jianqiang Wang

Edit� par Springer Nature Switzerland AG, Cham, 2025

ISBN 10 : 3032011515 ISBN 13 : 9783032011510

Neuf Paperback

Vendeur : CitiRetail, Stevenage, Royaume-Uni

�valuation du vendeur 5 sur 5 �toiles

Paperback. Etat : new. Paperback. This book offers a comprehensive exploration of the intersection between Large Language Models (LLMs) and recommendation systems, serving as a practical guide for practitioners, researchers, and students in AI, natural language processing, and data science. It addresses the limitations of traditional recommendation techniquessuch as their inability to fully understand nuanced language, reason dynamically over user preferences, or leverage multi-modal dataand demonstrates how LLMs can revolutionize personalized recommendations. By consolidating fragmented research and providing structured, hands-on tutorials, the book bridges the gap between cutting-edge research and real-world application, empowering readers to design and deploy next-generation recommender systems.Structured for progressive learning, the book covers foundational LLM concepts, the evolution from classic to LLM-powered recommendation systems, and advanced topics including end-to-end LLM recommenders, conversational agents, and multi-modal integration. Each chapter blends theoretical insights with practical coding exercises and real-world case studies, such as fashion recommendation and generative content creation. The final chapters discuss emerging challenges, including privacy, fairness, and future trends, offering a forward-looking roadmap for research and application. Readers with a basic understanding of machine learning and NLP will find this resource both accessible and invaluable for building effective, modern recommendation systems enhanced by LLMs. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability. N� de r�f. du vendeur 9783032011510

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Building Recommender Systems Using Large Language Models

Jianqiang (Jay) Wang

Edit� par Springer, Springer Okt 2025, 2025

ISBN 10 : 3032011515 ISBN 13 : 9783032011510

Neuf Taschenbuch

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Vendeur : buchversandmimpf2000, Emtmannsberg, BAYE, Allemagne

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Taschenbuch. Etat : Neu. This item is printed on demand - Print on Demand Titel. Neuware -This book offers a comprehensive exploration of the intersection between Large Language Models (LLMs) and recommendation systems, serving as a practical guide for practitioners, researchers, and students in AI, natural language processing, and data science. It addresses the limitations of traditional recommendation techniquessuch as their inability to fully understand nuanced language, reason dynamically over user preferences, or leverage multi-modal dataand demonstrates how LLMs can revolutionize personalized recommendations. By consolidating fragmented research and providing structured, hands-on tutorials, the book bridges the gap between cutting-edge research and real-world application, empowering readers to design and deploy next-generation recommender systems.Springer-Verlag KG, Sachsenplatz 4-6, 1201 Wien 236 pp. Englisch. N� de r�f. du vendeur 9783032011510

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Building Recommender Systems Using Large Language Models

Jianqiang (Jay) Wang

Edit� par Springer, Springer Okt 2025, 2025

ISBN 10 : 3032011515 ISBN 13 : 9783032011510

Neuf Taschenbuch

Vendeur : AHA-BUCH GmbH, Einbeck, Allemagne

�valuation du vendeur 5 sur 5 �toiles

Taschenbuch. Etat : Neu. Neuware - This book offers a comprehensive exploration of the intersection between Large Language Models (LLMs) and recommendation systems, serving as a practical guide for practitioners, researchers, and students in AI, natural language processing, and data science. It addresses the limitations of traditional recommendation techniques such as their inability to fully understand nuanced language, reason dynamically over user preferences, or leverage multi-modal data and demonstrates how LLMs can revolutionize personalized recommendations. By consolidating fragmented research and providing structured, hands-on tutorials, the book bridges the gap between cutting-edge research and real-world application, empowering readers to design and deploy next-generation recommender systems.Structured for progressive learning, the book covers foundational LLM concepts, the evolution from classic to LLM-powered recommendation systems, and advanced topics including end-to-end LLM recommenders, conversational agents, and multi-modal integration. Each chapter blends theoretical insights with practical coding exercises and real-world case studies, such as fashion recommendation and generative content creation. The final chapters discuss emerging challenges, including privacy, fairness, and future trends, offering a forward-looking roadmap for research and application. Readers with a basic understanding of machine learning and NLP will find this resource both accessible and invaluable for building effective, modern recommendation systems enhanced by LLMs. N� de r�f. du vendeur 9783032011510

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Building Recommender Systems Using Large Language Models (Paperback)

Jianqiang Wang

Edit� par Springer Nature Switzerland AG, Cham, 2025

ISBN 10 : 3032011515 ISBN 13 : 9783032011510

Neuf Paperback

Vendeur : AussieBookSeller, Truganina, VIC, Australie

�valuation du vendeur 5 sur 5 �toiles

Paperback. Etat : new. Paperback. This book offers a comprehensive exploration of the intersection between Large Language Models (LLMs) and recommendation systems, serving as a practical guide for practitioners, researchers, and students in AI, natural language processing, and data science. It addresses the limitations of traditional recommendation techniquessuch as their inability to fully understand nuanced language, reason dynamically over user preferences, or leverage multi-modal dataand demonstrates how LLMs can revolutionize personalized recommendations. By consolidating fragmented research and providing structured, hands-on tutorials, the book bridges the gap between cutting-edge research and real-world application, empowering readers to design and deploy next-generation recommender systems.Structured for progressive learning, the book covers foundational LLM concepts, the evolution from classic to LLM-powered recommendation systems, and advanced topics including end-to-end LLM recommenders, conversational agents, and multi-modal integration. Each chapter blends theoretical insights with practical coding exercises and real-world case studies, such as fashion recommendation and generative content creation. The final chapters discuss emerging challenges, including privacy, fairness, and future trends, offering a forward-looking roadmap for research and application. Readers with a basic understanding of machine learning and NLP will find this resource both accessible and invaluable for building effective, modern recommendation systems enhanced by LLMs. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability. N� de r�f. du vendeur 9783032011510

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Building Recommender Systems Using Large Language Models - Couverture souple

Wang, Jianqiang (Jay)

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