Building Recommender Systems Using Large Language Models

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 techniquessuch as their inability to fully understand nuanced language, reason dynamically over user preferences, or leverage multi-modal dataand 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.

Offers deep insights into leveraging LLMs for improved recommendation systems to enhance accuracy and personalization Includes step-by-step guidance for implementing LLM-based solutions in real-world scenarios, with case study examples Balances fundamental concepts like embeddings and traditional algorithms with advanced LLM techniques

Autorentext

Jianqiang (Jay) Wang is an AI and data science leader with over 16 years of experience developing machine learning, search, and recommendation systems across leading tech companies including Microsoft, Snap, Twitter, and Kuaishou. He has led data science and AI teams and built large-scale systems for content understanding, personalization, and monetization.

Jay is the founder of Curify AI, an AI-powered productivity and content platform, where he focuses on integrating Large Language Models into real-world applications. His current interests span retrieval-augmented generation, multimodal AI, and generative recommendation systems.

He holds a Ph.D. in Statistics and brings a blend of academic rigor and industrial experience to this hands-on guide for building LLM-enhanced recommendation systems.

Inhalt

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.