Junqi Jin
Impact in
- Information Systems top 0.5%
- Recommender Systems and Techniques
- Information Retrieval and Search Behavior
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- Advanced Neural Network Applications
- Multimodal Machine Learning Applications
Papers in
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- Recommender Systems and Techniques 6
- Information Retrieval and Search Behavior 2
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- Advanced Neural Network Applications 2
- Multimodal Machine Learning Applications 2
- Co-authors
- Kun Gai (5 shared papers)Han Li (2 shared papers)Xiaoqiang Zhu (1 shared paper)Ying Fan (1 shared paper)Zhu Tao Han (1 shared paper)Xiao Ma (1 shared paper)Guorui Zhou (1 shared paper)Changshui Zhang (4 shared papers)
- Journals
- IEEE Transactions on Neural Networks and Learning Systems (2 papers)Journal of Nondestructive Evaluation (1 paper)Materials (1 paper)IEEE Transactions on Intelligent Transportation Systems (1 paper)IEEE Transactions on Pattern Analysis and Machine Intelligence (1 paper)
- Partner nations
- ChinaUnited KingdomUnited States
In The Last Decade
Junqi Jin
12 papers receiving 2k citations
Junqi Jin's Hit Papers
Peers
Comparison fields: 5 of 71
- Information Systems 1.4k
- Computer Vision and Pattern Recognition 355
- Automotive Engineering 79
- Management Science and Operations Research 102
- Artificial Intelligence 209
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 | 1446 |
| 2 | 2014 | 270 | |
| 3 | 2016 | 139 | |
| 4 | 2018 | 107 | |
| 5 | 2018 | 21 | |
| 6 | 2019 | 11 | |
| 7 | 2020 | 9 | |
| 8 | 2023 | 8 | |
| 9 | 2019 | 6 | |
| 10 | 2019 | 5 | |
| 11 | 2024 | 5 | |
| 12 | 2023 | 2 | |
| 13 | 2023 | 0 | |
| 14 | 2025 | 0 |
About Junqi Jin
Junqi Jin is a scholar working on Information Systems, Computer Vision and Pattern Recognition, Management Science and Operations Research, Mechanical Engineering and Marketing, having authored 14 papers that have together received 2k indexed citations. Recurring topics across this work include Recommender Systems and Techniques (6 papers), Advanced Bandit Algorithms Research (3 papers), Advanced Neural Network Applications (2 papers), Auction Theory and Applications (2 papers), Information Retrieval and Search Behavior (2 papers), Non-Destructive Testing Techniques (2 papers), Multimodal Machine Learning Applications (2 papers) and Customer churn and segmentation (1 paper). The work is most often cited by research in Information Systems (1.4k citations), Computer Vision and Pattern Recognition (355 citations), Automotive Engineering (79 citations), Management Science and Operations Research (102 citations) and Artificial Intelligence (209 citations). Junqi Jin has collaborated with scholars based in China, United Kingdom and United States. Frequent co-authors include Kun Gai, Han Li, Xiaoqiang Zhu, Ying Fan, Zhu Tao Han, Xiao Ma, Guorui Zhou, Changshui Zhang, Kun Fu and Runpeng Cui. Their work appears in journals such as IEEE Transactions on Neural Networks and Learning Systems, Journal of Nondestructive Evaluation, Materials, IEEE Transactions on Intelligent Transportation Systems and IEEE Transactions on Pattern Analysis and Machine Intelligence.
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.