Torrent details for "Jianqiang W. Building Recommender Systems Using Large Language M…" Log in to bookmark
Controls:
×
Report Torrent
Please select a reason for reporting this torrent:
Your report will be reviewed by our moderation team.
×
Report Information
Loading report information...
This torrent has been reported 0 times.
Report Summary:
| User | Reason | Date |
|---|
Failed to load report information.
×
Success
Your report has been submitted successfully.
Checked by:
Category:
Language:
None
Total Size:
11.0 MB
Info Hash:
50D1561F17483469DC7A2AE01E36B487F4B985DC
Added By:
Added:
Oct. 23, 2025, 9:16 a.m.
Stats:
|
(Last updated: Oct. 23, 2025, 9:17 a.m.)
| File | Size |
|---|---|
| Jianqiang W. Building Recommender Systems Using Large Language Models 2025.pdf | 11.0 MB |
Name
DL
Uploader
Size
S/L
Added
NOTE
SOURCE: Jianqiang W. Building Recommender Systems Using Large Language Models 2025
-----------------------------------------------------------------------------------
COVER
-----------------------------------------------------------------------------------
MEDIAINFO
Textbook in PDF format 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. The motivations for applying LLMs to recommendation tasks are both theoretical and practical. On one hand, LLMs offer a flexible, unified architecture that can represent user interests, item content, temporal sequences, and even conversational context without hand-crafted features or rigid schemas. On the other hand, they enable new application modes: chat-based recommendation, cold-start reasoning, dynamic personalization, and explainability. Who This Book Is For: This book is intended for professionals, researchers, and students who are interested in understanding and building modern recommendation systems enhanced by Large Language Models (LLMs). Readers will benefit most if they have a foundational understanding of machine learning and natural language processing though much of the material is self-contained and accessible to those with technical curiosity. Primary audiences include: Practicing data scientists, machine learning engineers, and developers working on recommendation systems or personalization. Graduate students and researchers in fields such as NLP, IR, AI, and data science. Lecturers, educators, and technical managers seeking a comprehensive resource on this rapidly evolving domain. 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. Introduction to LLMs From Traditional to LLM-Powered Recommendation Systems LLM-Enhanced Recommendation Systems LLM as Recommender Conversational Recommendation Systems Leveraging Multi-modal Data Generative Recommendation and Planning Systems Challenges and Trends in LLMs for Recommendation Systems Index
×