Jae Kyeong Kim
- Information Systems top 0.5%
- Artificial Intelligence top 2%
- Marketing top 2%
- Sociology and Political Science top 5%
- Management Science and Operations Research top 1%
- Co-authors
- Hyea Kyeong KimSoung Hie KimIl Young ChoiYoung U. RyuSang Hyun ChoiHee Seok SongJun XiangChang Hee Han
- Topics
- Recommender Systems and Techniques (21 papers)Digital Marketing and Social Media (16 papers)Customer churn and segmentation (8 papers)
- Journals
- European Journal of Operational ResearchComputers in Human BehaviorExpert Systems with Applications
- Partner nations
- South KoreaUnited StatesIsrael
In The Last Decade
Jae Kyeong Kim
47 papers receiving 2.0k citations
Hit Papers
Peers
Comparison fields: 5 of 106
- Information Systems 1.2k
- Artificial Intelligence 518
- Marketing 432
- Sociology and Political Science 422
- Management Science and Operations Research 396
Countries citing papers authored by Jae Kyeong Kim
This map shows the geographic impact of Jae Kyeong Kim'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 Jae Kyeong Kim with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Jae Kyeong Kim more than expected).
Fields of papers citing papers by Jae Kyeong Kim
This network shows the impact of papers produced by Jae Kyeong Kim. 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 Jae Kyeong Kim. The network helps show where Jae Kyeong Kim may publish in the future.
Co-authorship network of co-authors of Jae Kyeong Kim
This figure shows the co-authorship network connecting the top 25 collaborators of Jae Kyeong Kim. A scholar is included among the top collaborators of Jae Kyeong Kim based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Jae Kyeong Kim. Jae Kyeong Kim is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 0 | |
| 2 | 1 | |
| 3 | 0 | |
| 4 | 4 | |
| 5 | 3 | |
| 6 | 0 | |
| 7 | 3 | |
| 8 | 1 | |
| 9 | 7 | |
| 10 | 19 | |
| 11 | A New Item Recommendation Procedure Using Preference Boundary | 1 |
| 12 | 2 | |
| 13 | Cost-Based Directed Scheduling : Part II, An Inter-Job Cost Propagation Algorithm | 0 |
| 14 | A Hybrid Multimedia Contents Recommendation Procedure for a New Item Problem in M-commerce | 2 |
| 15 | 1 | |
| 16 | 175 | |
| 17 | 91 | |
| 18 | 293 | |
| 19 | 2 | |
| 20 | Evaluating Efficiency of Life Insurance Companies Utilizing DEA and Machine Learning | 2 |
About Jae Kyeong Kim
Jae Kyeong Kim is a scholar working on Marketing, Information Systems and Leadership and Management, having authored 57 papers that have together received 2.1k indexed citations. Recurring topics across this work include Recommender Systems and Techniques (21 papers), Digital Marketing and Social Media (16 papers) and Customer churn and segmentation (8 papers). The work is most often cited by research in Information Systems (1.2k citations), Marketing (432 citations) and Management Science and Operations Research (396 citations). Jae Kyeong Kim has collaborated with scholars based in South Korea, United States and Israel. Frequent co-authors include Hyea Kyeong Kim, Soung Hie Kim, Il Young Choi, Young U. Ryu, Sang Hyun Choi, Hee Seok Song, Jun Xiang, Chang Hee Han, Sang‐Ho Lee and Yoon Ho Cho. Their work appears in journals such as European Journal of Operational Research, Computers in Human Behavior and Expert Systems with Applications.
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.