Jianqiang W Building Recommender Systems Using Large Language Models 2025
General:
Name: Jianqiang W Building Recommender Systems Using Large Language Models 2025
Format: pdf
Size: 11.01 MB
Book:
Title: Building Recommender Systems Using Large Language Models
Author: Jianqiang (Jay) Wang
Language: polski
Year: 2025
Subjects: N/A
Publisher: Springer-Verlag New York, LLC
ISBN: 9783032011527
Total pages: 229
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.
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