YoungOk Kwon
Impact in
- Information Systems top 1%
- Recommender Systems and Techniques
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- Advanced Bandit Algorithms Research
Papers in
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- Advanced Bandit Algorithms Research 5
- Stock Market Forecasting Methods 1
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- Recommender Systems and Techniques 5
- Co-authors
- Gediminas Adomavičius (4 shared papers)Young Bong Chang (4 shared papers)V.M. Hietala (1 shared paper)K.S. Champlin (1 shared paper)Dongwon Lee (1 shared paper)Ho Hur (1 shared paper)Hyeokkoo Eric Kwon (1 shared paper)Sug Kyun Shin (1 shared paper)
- Journals
- Journal of Business Research (2 papers)Computers in Human Behavior (1 paper)IEEE Intelligent Systems (1 paper)IEEE Transactions on Knowledge and Data Engineering (1 paper)INFORMS journal on computing (1 paper)
- Partner nations
- United StatesSouth KoreaSingapore
In The Last Decade
YoungOk Kwon
9 papers receiving 892 citations
YoungOk Kwon's Hit Papers
Peers
Comparison fields: 5 of 59
- Information Systems 778
- Management Science and Operations Research 264
- Computational Mathematics 12
- Marketing 143
- Computer Vision and Pattern Recognition 243
Countries citing papers authored by YoungOk Kwon
This map shows the geographic impact of YoungOk 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 YoungOk Kwon with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites YoungOk Kwon more than expected).
Fields of papers citing papers by YoungOk Kwon
This network shows the impact of papers produced by YoungOk 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 YoungOk Kwon. The network helps show where YoungOk Kwon may publish in the future.
Co-authors
The 12 scholars most cited alongside YoungOk 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 | Improving Aggregate Recommendation Diversity Using Ranking-Based Techniques Hit paper breakdown → | 2011 | 461 |
| 2 | 2007 | 348 | |
| 3 | 2014 | 52 | |
| 4 | Towards more confident recommendations: Improving recommender systems using filtering approach based on rating variance | 2007 | 23 |
| 5 | 2019 | 23 | |
| 6 | 2008 | 15 | |
| 7 | 1986 | 12 | |
| 8 | 2023 | 1 | |
| 9 | An Analytics Approach to Managing Provider Treatment Variety to Improve Patient Outcomes for a Type-2 Diabetes Clinic | 2014 | 1 |
| 10 | 2018 | 1 | |
| 11 | 2016 | 0 | |
| 12 | 2017 | 0 | |
| 13 | 2015 | 0 |
About YoungOk Kwon
YoungOk Kwon is a scholar working on Management Science and Operations Research, Information Systems, Marketing, Finance and Economics and Econometrics, having authored 13 papers that have together received 937 indexed citations. Recurring topics across this work include Recommender Systems and Techniques (5 papers), Advanced Bandit Algorithms Research (5 papers), Financial Markets and Investment Strategies (3 papers), Consumer Market Behavior and Pricing (3 papers), Complex Systems and Time Series Analysis (2 papers), Health Policy Implementation Science (1 paper), Stock Market Forecasting Methods (1 paper) and Corporate Finance and Governance (1 paper). The work is most often cited by research in Information Systems (778 citations), Management Science and Operations Research (264 citations), Computational Mathematics (12 citations), Marketing (143 citations) and Computer Vision and Pattern Recognition (243 citations). YoungOk Kwon has collaborated with scholars based in United States, South Korea and Singapore. Frequent co-authors include Gediminas Adomavičius, Young Bong Chang, V.M. Hietala, K.S. Champlin, Dongwon Lee, Ho Hur, Hyeokkoo Eric Kwon, Sug Kyun Shin, Ashish Gupta and Beom Seok Kim. Their work appears in journals such as Journal of Business Research, Computers in Human Behavior, IEEE Intelligent Systems, IEEE Transactions on Knowledge and Data Engineering and INFORMS journal on computing.
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.