Description
Building Recommender Systems Using Large Language Models explores the exciting intersection of large language models (LLMs) and recommender systems, serving as a leading resource for researchers and data engineers. Criticizing the traditional limitations of recommender systems, the author shows how language models can revolutionize personalized user experiences by understanding linguistic nuances and dynamic reasoning.
The book is structured in a way that starts from the basic concepts of language models and moves towards more complex topics such as conversational agents and multi-faceted systems. Along with theoretical discussions, coding exercises and case studies in areas such as fashion and content creation are presented to bridge the gap between academic research and commercial applications.
Book Features
- Comprehensive analysis of the evolution of classic recommender systems into productive AI-based systems.
- Training in implementing end-to-end recommender systems with language models.
- Investigating the integration of multi-modal data in the user recommendation process.
- Focus on ethical challenges including privacy and fairness in artificial intelligence algorithms.
- Providing step-by-step tutorials and practical projects for designing the next generation of intelligent systems.
Book specifications
- Publisher: Springer
- Lecturer: Jianqiang (Jay) Wang
- Number of pages: 229
- Number of chapters: 8
- Format: PDF
Headlines
Front Matter
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
Back Matter
Pictures
User Guide
Extract the file and run it with the appropriate software.
Download link
File(s) password: www.downloadly.ir
File size
6 MB
