Ruoyan Kong

Orcid: 0000-0003-0585-0453

According to our database1, Ruoyan Kong authored at least 15 papers between 2016 and 2024.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

Legend:

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PhD thesis 
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Links

On csauthors.net:

Bibliography

2024
What Are We Optimizing For? A Human-centric Evaluation Of Deep Learning-based Recommender Systems.
CoRR, 2024

COVID-19 as Reflected in University President Bulk Email.
CoRR, 2024

2023
Interactive Content Diversity and User Exploration in Online Movie Recommenders: A Field Experiment.
CoRR, 2023

Towards an Effective Organization-Wide Bulk Email System.
CoRR, 2023

Organizational Bulk Email Systems: Their Role and Performance in Remote Work.
CoRR, 2023

HierCat: Hierarchical Query Categorization from Weakly Supervised Data at Facebook Marketplace.
Proceedings of the Companion Proceedings of the ACM Web Conference 2023, 2023

Less Can Be More: Exploring Population Rating Dispositions with Partitioned Models in Recommender Systems.
Proceedings of the Adjunct Proceedings of the 31st ACM Conference on User Modeling, 2023

The Economics of Recommender Systems: Evidence from a Field Experiment on MovieLens.
Proceedings of the 24th ACM Conference on Economics and Computation, 2023

Getting the Most from Eye-Tracking: User-Interaction Based Reading Region Estimation Dataset and Models.
Proceedings of the 2023 Symposium on Eye Tracking Research and Applications, 2023

2022
Working for the Invisible Machines or Pumping Information into an Empty Void? An Exploration of Wikidata Contributors' Motivations.
Proc. ACM Hum. Comput. Interact., 2022

Multi-Objective Personalization in Multi-Stakeholder Organizational Bulk E-mail: A Field Experiment.
Proc. ACM Hum. Comput. Interact., 2022

2021
Learning to Ignore: A Case Study of Organization-Wide Bulk Email Effectiveness.
Proc. ACM Hum. Comput. Interact., 2021

Virtual Reality System for Invasive Therapy.
Proceedings of the IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops, 2021

NimbleLearn: A Scalable and Fast Batch-mode Active Learning Approach.
Proceedings of the 2021 International Conference on Data Mining, 2021

2016
Group Preference Aggregation: A Nash Equilibrium Approach.
Proceedings of the IEEE 16th International Conference on Data Mining, 2016


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