Junqi Jin
Impact in
- Information Systems top 0.5%
- Recommender Systems and Techniques
-
- Image and Video Quality Assessment
- Advanced Image and Video Retrieval Techniques
- Advanced Neural Network Applications
Papers in ⓘ
-
- Image and Video Quality Assessment 2
- Multimodal Machine Learning Applications 2
- Video Analysis and Summarization 2
- Advanced Neural Network Applications 2
- Co-authors
- Kun Gai (3 shared papers)Han Li (2 shared papers)Guorui Zhou (1 shared paper)Yanghui Yan (1 shared paper)Ying Fan (1 shared paper)Xiao Ma (1 shared paper)Zhu Han (1 shared paper)Xiaoqiang Zhu (1 shared paper)
- Journals
- IEEE Transactions on Neural Networks and Learning Systems (2 papers)Acta Biomaterialia (1 paper)IEEE Transactions on Intelligent Transportation Systems (1 paper)Journal of Nondestructive Evaluation (1 paper)Materials (1 paper)
- Partner nations
- ChinaUnited KingdomUnited States
In The Last Decade
Junqi Jin
10 papers receiving 1.5k citations
Hit Papers
Peers
Comparison fields: 5 of 97
- Information Systems 975
- Computer Vision and Pattern Recognition 690
- Artificial Intelligence 719
- Management Science and Operations Research 241
- Media Technology 108
Countries citing papers authored by Junqi Jin
This map shows the geographic impact of Junqi Jin'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 Junqi Jin with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Junqi Jin more than expected).
Fields of papers citing papers by Junqi Jin
This network shows the impact of papers produced by Junqi Jin. 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 Junqi Jin. The network helps show where Junqi Jin may publish in the future.
Co-authors
The 25 scholars most cited alongside Junqi Jin, 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 | Deep Interest Network for Click-Through Rate Prediction Hit paper breakdown → | 2018 | 1134 |
| 2 | 2014 | 230 | |
| 3 | 2016 | 125 | |
| 4 | 2022 | 27 | |
| 5 | 2018 | 19 | |
| 6 | 2019 | 9 | |
| 7 | 2019 | 6 | |
| 8 | 2023 | 5 | |
| 9 | 2019 | 5 | |
| 10 | 2024 | 4 | |
| 11 | 2023 | 0 | |
| 12 | 2023 | 0 |
About Junqi Jin
Junqi Jin is a scholar working on Computer Vision and Pattern Recognition, Computer Science Applications, Automotive Engineering, Microbiology and Ocean Engineering, having authored 12 papers that have together received 1.6k indexed citations. Recurring topics across this work include Data Stream Mining Techniques (2 papers), Image and Video Quality Assessment (2 papers), Multimodal Machine Learning Applications (2 papers), Transportation and Mobility Innovations (2 papers), Non-Destructive Testing Techniques (2 papers), Geophysical Methods and Applications (2 papers), Video Analysis and Summarization (2 papers) and Advanced Neural Network Applications (2 papers). The work is most often cited by research in Information Systems (975 citations), Computer Vision and Pattern Recognition (690 citations), Artificial Intelligence (719 citations), Management Science and Operations Research (241 citations) and Media Technology (108 citations). Junqi Jin has collaborated with scholars based in China, United Kingdom and United States. Frequent co-authors include Kun Gai, Han Li, Guorui Zhou, Yanghui Yan, Ying Fan, Xiao Ma, Zhu Han, Xiaoqiang Zhu, Changshui Zhang and Kun Fu. Their work appears in journals such as IEEE Transactions on Neural Networks and Learning Systems, Acta Biomaterialia, IEEE Transactions on Intelligent Transportation Systems, Journal of Nondestructive Evaluation and Materials.
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.