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Building recommender systems using large language models

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Building recommender systems using large language modelsby Jianqiang (Jay) Wang.
作者:	Wang, Jianqiang.
出版者:	Cham :Springer Nature Switzerland :2025.
面頁冊數:	xxi, 213 p. :ill., digital ;24 cm.
Contained By:	Springer Nature eBook
標題:	Recommender systems (Information filtering)
電子資源:	https://doi.org/10.1007/978-3-032-01152-7
ISBN:	9783032011527$q(electronic bk.)

Building recommender systems using large language models
Wang, Jianqiang.

Building recommender systems using large language models[electronic resource] /by Jianqiang (Jay) Wang. - Cham :Springer Nature Switzerland :2025. - xxi, 213 p. :ill., digital ;24 cm.

Chapter 1 Introduction to LLMs -- Chapter 2 From Traditional to LLM-powered Recommendation Systems -- Chapter 3 LLM-enhanced recommendation system -- Chapter 4 LLM as recommendation system -- Chapter 5 Conversational recommendation systems -- Chapter 6 Leveraging Multi-Modal Data -- Chapter 7 Generative Recommendation and Planning Systems -- Chapter 8 Challenges and Trends in LLMs for Recommendation Systems.

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.

ISBN: 9783032011527$q(electronic bk.)

Standard No.: 10.1007/978-3-032-01152-7doiSubjects--Topical Terms:

310886
Recommender systems (Information filtering)

LC Class. No.: ZA3084 / .W35 2025

Dewey Class. No.: 005.56

Building recommender systems using large language models
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520 $a 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.
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