Work

Tunable LLM-based Proactive Recommendation Agent

Year: 2025

Type: conference-paper

Abstract: Recommender systems are indispensable on various digital platforms.However, traditional methods often reinforce existing user interests, which leads to echo chambers and limits diversity.Proactive Recommendation Systems (PRS) aim to address this issue by cultivating users' latent interests through multi-step recommendations.Despite advancements, challenges persist particularly in optimizing long-term rewards and adapting to real-time user feedback.In this study, we propose an LLM-based Actor-Critic Agent framework to enhance PRS.This framework utilizes the LLM-based agent to adjust recommendations in real time based on feedback and employs agent-tuning methods to optimize long-term rewards using three proposed reward functions.Extensive experiments validate the significant superiority of this framework over existing methods by optimizing long-term rewards and dynamically evolving with user feedback.

Source: Annual Meeting of the Association for Computational Linguistics (ACL)

DOI: 10.18653/v1/2025.acl-long.944

Authors: Mingze Wang, Chongming Gao, Wenjie Wang, Yangyang Li, Fuli Feng

Institution: University of Science and Technology of China

Language: English

Topic: Recommender Systems and Techniques

Subfield: Information Systems

Field: Computer Science

Domain: Physical Sciences

Open Access status: hybrid

Funders: National Natural Science Foundation of China, National Key Research and Development Program of China

Awards: G3379573397, G5104534126, G969952163