Jianqiang Wang: Building Recommender Systems Using Large Language Models, Kartoniert / Broschiert
Building Recommender Systems Using Large Language Models
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- Verlag:
- Springer-Verlag GmbH, 10/2025
- Einband:
- Kartoniert / Broschiert
- Sprache:
- Englisch
- ISBN-13:
- 9783032011510
- Artikelnummer:
- 12336021
- Umfang:
- 201 Seiten
- Sonstiges:
- X, 145 p. 21 illus. in color.
- Erscheinungstermin:
- 9.10.2025
- Hinweis
-
Achtung: Artikel ist nicht in deutscher Sprache!
Klappentext
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.
