Reinforcement Learning-based Recommender Systems with Large Language Models - SIGIR 2024

Explore a cutting-edge conference talk on Reinforcement Learning-based Recommender Systems that leverage Large Language Models (LLMs) for state reward and action modeling. Presented at SIGIR 2024, this 18-minute session delves into the integration of LLMs with recommender systems, focusing on their application in reinforcement learning frameworks. Learn how authors Jie Wang, Alexandros Karatzoglou, Ioannis Arapakis, and Joemon Jose propose novel approaches to enhance recommendation algorithms using the power of large language models. Gain insights into the potential of this innovative combination to improve state representation, reward modeling, and action selection in recommender systems, potentially revolutionizing personalized content delivery and user experience in various digital platforms.

SIGIR 2024 M1.6 [fp] Reinforcement Learning-based Recommender Systems with LLMs