류성은’s paper has been accepted in

Title: PRISM: A Personalized News Recommendation System for Mitigating Political Echo Chambers,
Author: Seongeun Ryu, Hoyeol Yang, Kayoung Lee, and Sang-Wook Kim
Abstract
Personalized news recommendation systems often reinforce users’ pre-existing beliefs by limiting their exposure, contributing to polit-ical echo chambers. Existing mitigation approaches—such as rewrit-ing biased text or diversifying recommendations—risk distorting article semantics or deepening polarization, while typically offering no rationale for why content is judged biased. We present PRISM, an interactive web-based news recommendation system that mit-igates political echo chambers across three levels of granularity: feed level, article level, and event level. Building on KHAN (political stance prediction) and CROWN (personalized news recommenda-tion) from our prior work, PRISM lets users monitor the political distribution of their reading history and current recommendations, inspect each article’s predicted stance with bias-indicative key-words and natural-language rationales, and voluntarily explore cross-stance coverage of the same news event, preserving user au-tonomy in line with self-determination theory. PRISM is available at https://github.com/Hoyeol-Yang/PRISM, with a demonstration video at https://youtu.be/NbcvHME__cI.