Year: 2025
Type: conference-paper
Abstract: Recently, there has been growing research on negative feedback in recommender systems. These studies use a fixed threshold to binarize feedback into positive or negative. However, such an approach bears limitations when the rating habits for expressing disappointment differ across users or when ratings are noisy. Motivated by the remarkable success of Large Language Models (LLMs), we investigate how LLM can address this challenge on the fly. To this end, we present ReFINe, Re surrecting F alsely I dentified Ne gative feedback with LLM. ReFINe classifies the negative feedback into two distinct types: Falsely identified negative with positive signals and Confirmed negative with only negative signals. To the best of our knowledge, our work is the first to propose and demonstrate the distinction between two perspectives on negative feedback. We first leverage LLM to better separate between positive and negative sets for each user, and implement Re-Weighted BPR, a dedicated Bayesian Personalized Ranking loss function tailored to our perspective on negative feedback. Experimental results show that our model outperforms strong baseline models. The code is available at https://github.com/Chanwoo-Jeong-2000/ReFINe.
Authors: Chanwoo Jeong, Yujin Kang, Yoon-Sik Cho
Institution: Chung-Ang University
Language: English
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Awards: G169117410, G4447557280, G614672494
OpenAlex ID: W4410637844