14 citations · 22 across the 4 of their papers we have counts for
4 papers
Large Language Models as Conversational Movie Recommenders: A User Study
Ruixuan Sun, Xinyi Li, Avinash Akella +1
This paper explores the effectiveness of using large language models (LLMs) for personalized movie recommendations from users' perspectives in an online field experiment. Our study…
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…
Interactive Content Diversity and User Exploration in Online Movie Recommenders: A Field Experiment
Ruixuan Sun, Avinash Akella, Ruoyan Kong +2
Recommender systems often struggle to strike a balance between matching users' tastes and providing unexpected recommendations. When recommendations are too narrow and fail to cove…
Getting the Most from Eye-Tracking: User-Interaction Based Reading Region Estimation Dataset and Models
Ruoyan Kong, Ruixuan Sun, Charles Chuankai Zhang +4
A single digital newsletter usually contains many messages (regions). Users' reading time spent on, and read level (skip/skim/read-in-detail) of each message is important for platf…