Jianqiang Wang (Jay): Building Recommender Systems Using Large Language Models, Kartoniert / Broschiert
Building Recommender Systems Using Large Language Models
- Publisher:
- Springer-Verlag GmbH, 11/2025
- Binding:
- Kartoniert / Broschiert
- Language:
- Englisch
- ISBN-13:
- 9783032011510
- Item number:
- 12336021
- Volume:
- 213 Pages
- other:
- XXIII, 213 p. 105 illus., 103 illus. in color.
- Release date:
- 15.11.2025
- Note
-
Caution: Product is not in German language
Blurb
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.
