How Jing
Impact in
- Information Systems top 1%
- Recommender Systems and Techniques
- Artificial Intelligence top 5%
- Advanced Graph Neural Networks
- Topic Modeling
Papers in
-
- Topic Modeling 4
- Text and Document Classification Technologies 2
- Advanced Text Analysis Techniques 2
- Speech Recognition and Synthesis 2
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- Recommender Systems and Techniques 4
- Co-authors
- Alexander Johannes Smola (2 shared papers)Alex Beutel (1 shared paper)Chao-Yuan Wu (1 shared paper)Amr Ahmed (1 shared paper)Yu Tsao (5 shared papers)Jaewon Yang (3 shared papers)Qi He (3 shared papers)Bee-Chung Chen (1 shared paper)
- Journals
- International Joint Conference on Natural Language Processing (1 paper)
- Partner nations
- United StatesTaiwan
In The Last Decade
How Jing
11 papers receiving 695 citations
How Jing's Hit Papers
Peers
Comparison fields: 5 of 68
- Information Systems 551
- Artificial Intelligence 458
- Management Science and Operations Research 152
- Transportation 71
- Computer Vision and Pattern Recognition 168
Countries citing papers authored by How Jing
This map shows the geographic impact of How Jing'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 How Jing with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites How Jing more than expected).
Fields of papers citing papers by How Jing
This network shows the impact of papers produced by How Jing. 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 How Jing. The network helps show where How Jing may publish in the future.
Co-authors
The 23 scholars most cited alongside How Jing, 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 | Recurrent Recommender Networks Hit paper breakdown → | 2017 | 507 |
| 2 | 2017 | 100 | |
| 3 | 2017 | 50 | |
| 4 | 2013 | 25 | |
| 5 | 2014 | 16 | |
| 6 | Semantic Na"ive Bayes Classifier for Document Classification | 2013 | 9 |
| 7 | 2019 | 5 | |
| 8 | 2014 | 4 | |
| 9 | 2021 | 3 | |
| 10 | 2013 | 2 | |
| 11 | 2014 | 2 |
About How Jing
How Jing is a scholar working on Artificial Intelligence, Information Systems, Transportation, Computer Vision and Pattern Recognition and Management Science and Operations Research, having authored 11 papers that have together received 723 indexed citations. Recurring topics across this work include Recommender Systems and Techniques (4 papers), Topic Modeling (4 papers), Text and Document Classification Technologies (2 papers), Generative Adversarial Networks and Image Synthesis (2 papers), Advanced Text Analysis Techniques (2 papers), Advanced Bandit Algorithms Research (2 papers), Speech Recognition and Synthesis (2 papers) and Human Mobility and Location-Based Analysis (2 papers). The work is most often cited by research in Information Systems (551 citations), Artificial Intelligence (458 citations), Management Science and Operations Research (152 citations), Transportation (71 citations) and Computer Vision and Pattern Recognition (168 citations). How Jing has collaborated with scholars based in United States and Taiwan. Frequent co-authors include Alexander Johannes Smola, Alex Beutel, Chao-Yuan Wu, Amr Ahmed, Yu Tsao, Jaewon Yang, Qi He, Bee-Chung Chen, Liangyue Li and Hanghang Tong. Their work appears in journals such as International Joint Conference on Natural Language Processing.
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.