activity
20162026
most citedUser-centered Evaluation of Popularity Bias in Recommender Systems

145 citations · 186 across the 8 of their papers we have counts for

collaborators

9 papers

cs.IR2026

Evaluating Brand Retrieval and Ranking in Large Language Model Recommendations

Edward Malthouse, Kun-Yu Lee, Jing Yang +2

Large language models (LLMs) are increasingly used for product recommendation, but evaluating their recommendations presents challenges that differ from conventional information re…

cs.SI2024

Large Language Model-driven Multi-Agent Simulation for News Diffusion Under Different Network Structures

Xinyi Li, Yu Xu, Yongfeng Zhang +1

The proliferation of fake news in the digital age has raised critical concerns, particularly regarding its impact on societal trust and democratic processes. Diverging from convent…

cs.CL2024★ 5 cited

Large Language Model Agent for Fake News Detection

Xinyi Li, Yongfeng Zhang, Edward C. Malthouse

In the current digital era, the rapid spread of misinformation on online platforms presents significant challenges to societal well-being, public trust, and democratic processes, i…

cs.IR2023★ 2 cited

Exploring Fine-tuning ChatGPT for News Recommendation

Xinyi Li, Yongfeng Zhang, Edward C Malthouse

News recommendation systems (RS) play a pivotal role in the current digital age, shaping how individuals access and engage with information. The fusion of natural language processi…

cs.IR2023★ 5 cited

A Preliminary Study of ChatGPT on News Recommendation: Personalization, Provider Fairness, Fake News

Xinyi Li, Yongfeng Zhang, Edward C. Malthouse

Online news platforms commonly employ personalized news recommendation methods to assist users in discovering interesting articles, and many previous works have utilized language m…

cs.IR2023★ 3 cited

PBNR: Prompt-based News Recommender System

Xinyi Li, Yongfeng Zhang, Edward C. Malthouse

Online news platforms often use personalized news recommendation methods to help users discover articles that align with their interests. These methods typically predict a matching…