25 citations · 91 across the 13 of their papers we have counts for
13 papers
Beyond the Individual: A Community-Engaged Framework for Ethical Online Community Research
Matthew Zent, Seraphina Yong, Dhruv Bala +4
Online community research routinely poses minimal risk to individuals, but does the same hold true for online communities? In response to high-profile breaches of online community…
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…
COVID-19 as Reflected in University President Bulk Email
Ruoyan Kong, Charles Chuankai Zhang, Jin Kang +2
E-mail ``Messages From the President'' to university students, staff, and faculty have long been used to keep campus communities aware of the latest policies, events, and news. But…
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…
Organizational Bulk Email Systems: Their Role and Performance in Remote Work
Ruoyan Kong, Haiyi Zhu, Joseph A. Konstan
The COVID-19 pandemic has forced many employees to work from home. Organizational bulk emails now play a critical role to reach employees with central information in this work-from…
Less Can Be More: Exploring Population Rating Dispositions with Partitioned Models in Recommender Systems
Ruixuan Sun, Ruoyan Kong, Qiao Jin +1
In this study, we partition users by rating disposition - looking first at their percentage of negative ratings, and then at the general use of the rating scale. We hypothesize tha…
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…
Multi-Objective Personalization in Multi-Stakeholder Organizational Bulk E-mail: A Field Experiment
Ruoyan Kong, Chuankai Zhang, Ruixuan Sun +3
Bulk email is often used in organizations to communicate ``important-to-organization'' messages such as policy changes, organizational plans, and administrative updates. However, n…
The Informational Role of Online Recommendations: Evidence from a Field Experiment
Guy Aridor, Duarte Goncalves, Daniel Kluver +2
We conduct a field experiment on a movie-recommendation platform to investigate whether and how online recommendations influence consumption choices. Using a within-subjects design…
Toward the Next Generation of News Recommender Systems
Himan Abdollahpouri, Edward Malthouse, Joseph Konstan +2
This paper proposes a vision and research agenda for the next generation of news recommender systems (RS), called the table d'hote approach. A table d'hote (translates as host's ta…
Learning to Ignore: A Case Study of Organization-Wide Bulk Email Effectiveness
Ruoyan Kong, Haiyi Zhu, Joseph A. Konstan
Bulk email is a primary communication channel within organizations, with all-company emails and regular newsletters serving as a mechanism for making employees aware of policies an…
Developing a Recommendation Benchmark for MLPerf Training and Inference
Carole-Jean Wu, Robin Burke, Ed H. Chi +4
Deep learning-based recommendation models are used pervasively and broadly, for example, to recommend movies, products, or other information most relevant to users, in order to enh…
Recommender Systems Notation: Proposed Common Notation for Teaching and Research
Michael D. Ekstrand, Joseph A. Konstan
As the field of recommender systems has developed, authors have used a myriad of notations for describing the mathematical workings of recommendation algorithms. These notations ap…