Young Ok Kwon
Impact in
- Information Systems top 5%
- Recommender Systems and Techniques
- Service-Oriented Architecture and Web Services
-
- Advanced Bandit Algorithms Research
Papers in
-
- Recommender Systems and Techniques 4
- Co-authors
- Gediminas Adomavičius (5 shared papers)Ji Hyun Park (1 shared paper)Jingjing Zhang (1 shared paper)
- Journals
- Korean Journal of Pediatrics (1 paper)Journal of Intelligence and Information Systems (1 paper)Journal of Sport and Leisure Studies (1 paper)Conference on Recommender Systems (1 paper)Journal of the Korean Society of Neonatology (1 paper)
- Partner nations
- United StatesSouth Korea
In The Last Decade
Young Ok Kwon
7 papers receiving 289 citations
Peers
Comparison fields: 5 of 71
- Information Systems 165
- Management Science and Operations Research 71
- Artificial Intelligence 116
- Computer Science Applications 19
- Marketing 31
Countries citing papers authored by Young Ok Kwon
This map shows the geographic impact of Young Ok Kwon's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Young Ok Kwon with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Young Ok Kwon more than expected).
Fields of papers citing papers by Young Ok Kwon
This network shows the impact of papers produced by Young Ok Kwon. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Young Ok Kwon. The network helps show where Young Ok Kwon may publish in the future.
Co-authors
The 4 scholars most cited alongside Young Ok Kwon, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | CEUR Workshop Proceedings | 2011 | 166 |
| 2 | Maximizing Aggregate Recommendation Diversity: A Graph-Theoretic Approach | 2011 | 59 |
| 3 | Toward more diverse recommendations: Item re-ranking methods for recommender systems | 2009 | 38 |
| 4 | Overcoming accuracy-diversity tradeoff in recommender systems: A variance-based approach | 2008 | 23 |
| 5 | 2007 | 11 | |
| 6 | 2013 | 8 | |
| 7 | Impact of data characteristics on recommender systems performance | 2010 | 1 |
| 8 | A Case of Non-immune Hydrops Fetalis due to Intraperitoneal Hemangioma. | 2005 | 0 |
| 9 | 2005 | 0 |
About Young Ok Kwon
Young Ok Kwon is a scholar working on Information Systems, Infectious Diseases, Management Science and Operations Research, Surgery and Health Information Management, having authored 9 papers that have together received 306 indexed citations. Recurring topics across this work include Recommender Systems and Techniques (4 papers), Advanced Bandit Algorithms Research (2 papers), Innovation in Digital Healthcare Systems (1 paper), Consumer Market Behavior and Pricing (1 paper), Urologic and reproductive health conditions (1 paper), Diverse Approaches in Healthcare and Education Studies (1 paper), Hepatitis Viruses Studies and Epidemiology (1 paper) and Vascular Malformations and Hemangiomas (1 paper). The work is most often cited by research in Information Systems (165 citations), Management Science and Operations Research (71 citations), Artificial Intelligence (116 citations), Computer Science Applications (19 citations) and Marketing (31 citations). Young Ok Kwon has collaborated with scholars based in United States and South Korea. Frequent co-authors include Gediminas Adomavičius, Ji Hyun Park, Ji Hyun Park and Jingjing Zhang. Their work appears in journals such as Korean Journal of Pediatrics, Journal of Intelligence and Information Systems, Journal of Sport and Leisure Studies, Conference on Recommender Systems and Journal of the Korean Society of Neonatology.
Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.