YoVDO

Data-efficient Fine-tuning for LLM-based Recommendation - SIGIR 2024 M1.6

Offered By: Association for Computing Machinery (ACM) via YouTube

Tags

Recommender Systems Courses Machine Learning Courses Information Retrieval Courses Fine-Tuning Courses

Course Description

Overview

Save Big on Coursera Plus. 7,000+ courses at $160 off. Limited Time Only!
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.

Syllabus

SIGIR 2024 M1.6 [fp] Data-efficient Fine-tuning for LLM-based Recommendation


Taught by

Association for Computing Machinery (ACM)

Related Courses

Introduction to Recommender Systems
University of Minnesota via Coursera Text Retrieval and Search Engines
University of Illinois at Urbana-Champaign via Coursera Machine Learning: Recommender Systems & Dimensionality Reduction
University of Washington via Coursera Java Programming: Build a Recommendation System
Duke University via Coursera Introduction to Recommender Systems: Non-Personalized and Content-Based
University of Minnesota via Coursera