Building Recommender Systems Using LLMs by Jianqiang (Jay) Wang(.PDF)+
File Size: 19.3 MB
Building Recommender Systems Using Large Language Models by Jianqiang (Jay) Wang
Requirements: .ePUB, .PDF reader, 19.3 MB
Overview: 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. Recommended prerequisites: • Basic knowledge of Machine Learning and NLP concepts; • Familiarity with Python programming and frameworks like PyTorch; • Exposure to tools such as the OpenAI API, LangChain, Hugging Face Transformers, or vector databases like Weaviate or FAISS is helpful but not mandatory.
Genre: Non-Fiction > Tech & Devices
Free Download links: