Building Recommender Systems Using Large Language Models
Language: English
Published by Springer, Springer Okt 2025, 2025
- Softcover
- New
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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.
Seller Inventory # 9783032011510
- Title
- Building Recommender Systems Using Large Language Models
- Author
- Jianqiang (Jay) Wang
- Publisher
- Springer, Springer Okt 2025
- Publication year
- 2025
- Condition
- Neu
- Binding
- Taschenbuch
- Language
- English
- ISBN 10
- 3032011515
- ISBN 13
- 9783032011510
- Item weight
- 365 grams
- Dimensions
- 235x155x13 mm
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.
"Synopsis" may belong to another edition of this title.
About the Author
Jianqiang (Jay) Wang is an AI and data science leader with over 16 years of experience developing machine learning, search, and recommendation systems across leading tech companies including Microsoft, Snap, Twitter, and Kuaishou. He has led data science and AI teams and built large-scale systems for content understanding, personalization, and monetization.
Jay is the founder of Curify AI, an AI-powered productivity and content platform, where he focuses on integrating Large Language Models into real-world applications. His current interests span retrieval-augmented generation, multimodal AI, and generative recommendation systems.
He holds a Ph.D. in Statistics and brings a blend of academic rigor and industrial experience to this hands-on guide for building LLM-enhanced recommendation systems.
"About the title" may belong to another edition of this title.
buchversandmimpf2000
Emtmannsberg, BAYE, Germany
AbeBooks seller since January 23, 2017
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