1 citations · 1 across the 4 of their papers we have counts for
4 papers
Balancing Domestic and Global Perspectives: Evaluating Dual-Calibration and LLM-Generated Nudges for Diverse News Recommendation
Ruixuan Sun, Matthew Zent, Minzhu Zhao +3
In this study, we applied the ``personalized diversity nudge framework'' with the goal of expanding user reading coverage in terms of news locality (i.e., domestic and world news).…
Co-Authoring the Self: A Human-AI Interface for Interest Reflection in Recommenders
Ruixuan Sun, Junyuan Wang, Sanjali Roy +1
Natural language-based user profiles in recommender systems have been explored for their interpretability and potential to help users scrutinize and refine their interests, thereby…
The MovieLens Beliefs Dataset: Collecting Pre-Choice Data for Online Recommender Systems
Guy Aridor, Duarte Goncalves, Ruoyan Kong +2
An increasingly important aspect of designing recommender systems involves considering how recommendations will influence consumer choices. This paper addresses this issue by intro…
What Are We Optimizing For? A Human-centric Evaluation of Deep Learning-based Movie Recommenders
Ruixuan Sun, Xinyi Wu, Avinash Akella +3
In the past decade, deep learning (DL) models have gained prominence for their exceptional accuracy on benchmark datasets in recommender systems (RecSys). However, their evaluation…