Building Recommender Systems Using Large Language Models
- Publisher's listprice EUR 53.49
-
22 184 Ft (21 128 Ft + 5% VAT)
The price is estimated because at the time of ordering we do not know what conversion rates will apply to HUF / product currency when the book arrives. In case HUF is weaker, the price increases slightly, in case HUF is stronger, the price goes lower slightly.
- Discount 12% (cc. 2 662 Ft off)
- Discounted price 19 522 Ft (18 593 Ft + 5% VAT)
Subcribe now and take benefit of a favourable price.
Subscribe
22 184 Ft
Availability
Not yet published.
Why don't you give exact delivery time?
Delivery time is estimated on our previous experiences. We give estimations only, because we order from outside Hungary, and the delivery time mainly depends on how quickly the publisher supplies the book. Faster or slower deliveries both happen, but we do our best to supply as quickly as possible.
Product details:
- Publisher Springer Nature Switzerland
- Date of Publication 22 October 2025
- Number of Volumes 1 pieces, Book
- ISBN 9783032011510
- Binding Paperback
- No. of pages213 pages
- Size 235x155 mm
- Language English
- Illustrations XXI, 213 p. 105 illus., 103 illus. in color. Illustrations, color 700
Categories
Long description:
"
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.
" MoreTable of Contents:
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.
More
