Explore data-efficient fine-tuning techniques for LLM-based recommendation systems in this 15-minute conference talk presented at SIGIR 2024. Delve into the research conducted by authors Xinyu Lin, Wenjie Wang, Yongqi Li, Shuo Yang, Fuli Feng, Yinwei Wei, and Tat-Seng Chua as they discuss innovative approaches to optimize large language models for personalized recommendations. Gain insights into cutting-edge methods that aim to improve the efficiency and effectiveness of recommendation systems using LLMs while minimizing data requirements.
Data-efficient Fine-tuning for LLM-based Recommendation - SIGIR 2024 M1.6
Association for Computing Machinery (ACM) via YouTube
Overview
Syllabus
SIGIR 2024 M1.6 [fp] Data-efficient Fine-tuning for LLM-based Recommendation
Taught by
Association for Computing Machinery (ACM)
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