Group Preference Aggregation: A Nash Equilibrium Approach. - Hongke Zhao; Qi Liu 003; Yong Ge; Ruoyan Kong; Enhong Chen (ICDM, 2016)

NimbleLearn: A Scalable and Fast Batch-mode Active Learning Approach. - Ruoyan Kong; Zhanlong Qiu; Yang Liu; Qi Zhao (ICDM (Workshops), 2021)

Virtual Reality System for Invasive Therapy. - Ruoyan Kong; Ruobing Wang; Zitao Shen (VR Workshops, 2021)

Learning to Ignore: A Case Study of Organization-Wide Bulk Email Effectiveness. - Ruoyan Kong; Haiyi Zhu; Joseph A. Konstan (Proc. ACM Hum. Comput. Interact., 2021)

Multi-Objective Personalization in Multi-Stakeholder Organizational Bulk E-mail: A Field Experiment. - Ruoyan Kong; Charles Chuankai Zhang; Ruixuan Sun; Vishnu Chhabra; Tanushsrisai Nadimpalli; Joseph A. Konstan (Proc. ACM Hum. Comput. Interact., 2022)

Working for the Invisible Machines or Pumping Information into an Empty Void? An Exploration of Wikidata Contributors' Motivations. - Charles Chuankai Zhang; Mo Houtti; C. Estelle Smith; Ruoyan Kong; Loren Terveen (Proc. ACM Hum. Comput. Interact., 2022)

Getting the Most from Eye-Tracking: User-Interaction Based Reading Region Estimation Dataset and Models. - Ruoyan Kong; Ruixuan Sun; Charles Chuankai Zhang; Chen Chen 109; Sneha Patri; Gayathri Gajjela; Joseph A. Konstan (ETRA, 2023)

The Economics of Recommender Systems: Evidence from a Field Experiment on MovieLens. - Guy Aridor; Duarte Gonçalves; Daniel Kluver; Ruoyan Kong; Joseph A. Konstan (EC, 2023)

Less Can Be More: Exploring Population Rating Dispositions with Partitioned Models in Recommender Systems. - Ruixuan Sun; Ruoyan Kong; Qiao Jin 002; Joseph A. Konstan (UMAP (Adjunct Publication), 2023)

HierCat: Hierarchical Query Categorization from Weakly Supervised Data at Facebook Marketplace. - Yunzhong He; Cong Zhang; Ruoyan Kong; Chaitanya Kulkarni; Qing Liu 040; Ashish Gandhe; Amit Nithianandan; Arul Prakash (WWW (Companion Volume), 2023)

Organizational Bulk Email Systems: Their Role and Performance in Remote Work. - Ruoyan Kong; Haiyi Zhu; Joseph A. Konstan (CoRR, 2023)

Towards an Effective Organization-Wide Bulk Email System. - Ruoyan Kong (CoRR, 2023)

What Are We Optimizing For? A Human-centric Evaluation of Deep Learning-based Movie Recommenders. - Ruixuan Sun; Xinyi Wu; Avinash Akella; Ruoyan Kong; Bart P. Knijnenburg; Joseph A. Konstan (IntRS@RecSys, 2024)

The MovieLens Beliefs Dataset: Collecting Pre-Choice Data for Online Recommender Systems. - Guy Aridor; Duarte Gonçalves; Ruoyan Kong; Daniel Kluver; Joseph A. Konstan (RecSys, 2024)

COVID-19 as Reflected in University President Bulk Email. - Ruoyan Kong; Charles Chuankai Zhang; Jin Kang; Haiyi Zhu; Joseph A. Konstan (CoRR, 2024)

What Are We Optimizing For? A Human-centric Evaluation Of Deep Learning-based Recommender Systems. - Ruixuan Sun; Avinash Akella; Xinyi Wu; Ruoyan Kong; Joseph A. Konstan (CoRR, 2024)

Supporting Organizations in Improving Employee Bulk E-mail - A Tool Design and Evaluation Study. - Ruoyan Kong; Ruixuan Sun; Charles Chuankai Zhang; Ye Yuan 010; Joseph A. Konstan (Proc. ACM Hum. Comput. Interact., 2024)

Interactive Content Diversity and User Exploration in Online Movie Recommenders: A Field Experiment. - Ruixuan Sun; Avinash Akella; Ruoyan Kong; Moyan Zhou; Joseph A. Konstan (Int. J. Hum. Comput. Interact., 2024)

Why They Come And Go: A Case Study of Productive Flyby Users and Their Rating Integrity Challenge in Movie Recommenders. - Ruixuan Sun; Ruoyan Kong; Ashlee Milton; Daniel Kluver; Ian Paterson; Joseph A. Konstan (CHIIR, 2025)