Lizhen Cui
Impact in
- Information Systems top 0.2%
- Recommender Systems and Techniques
- Cloud Computing and Resource Management
- Artificial Intelligence top 0.5%
- Advanced Graph Neural Networks
- Topic Modeling
Papers in
-
- Recommender Systems and Techniques 36
- Cloud Computing and Resource Management 36
- Service-Oriented Architecture and Web Services 31
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- Mobile Crowdsensing and Crowdsourcing 25
- Co-authors
- Hongzhi YinJunliang YuXin XiaTong ChenQuoc Viet Hung NguyenXiangliang ZhangZi HuangQinyong Wang
In The Last Decade
Lizhen Cui
280 papers receiving 4.5k citations
Hit Papers
Peers
Comparison fields: 5 of 163
- Information Systems 2.1k
- Artificial Intelligence 2.2k
- Computer Networks and Communications 1.0k
- Transportation 275
- Signal Processing 351
Countries citing papers authored by Lizhen Cui
This map shows the geographic impact of Lizhen Cui'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 Lizhen Cui with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Lizhen Cui more than expected).
Fields of papers citing papers by Lizhen Cui
This network shows the impact of papers produced by Lizhen Cui. 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 Lizhen Cui. The network helps show where Lizhen Cui may publish in the future.
Co-authors
The 25 scholars most cited alongside Lizhen Cui, 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 | 2025 | 0 | |
| 2 | 2025 | 1 | |
| 3 | 2025 | 0 | |
| 4 | 2025 | 1 | |
| 5 | 2024 | 1 | |
| 6 | 2024 | 4 | |
| 7 | 2024 | 1 | |
| 8 | 2024 | 8 | |
| 9 | 2024 | 11 | |
| 10 | 2024 | 0 | |
| 11 | 2024 | 15 | |
| 12 | 2024 | 2 | |
| 13 | 2024 | 5 | |
| 14 | 2023 | 2 | |
| 15 | 2023 | 9 | |
| 16 | 2022 | 37 | |
| 17 | 2022 | 5 | |
| 18 | 2021 | 2 | |
| 19 | 2021 | 38 | |
| 20 | 2020 | 118 |
About Lizhen Cui
Lizhen Cui is a scholar working on Information Systems, Computer Science Applications, Artificial Intelligence, Computer Networks and Communications and Transportation, having authored 324 papers that have together received 4.6k indexed citations. Recurring topics across this work include Recommender Systems and Techniques (36 papers), Cloud Computing and Resource Management (36 papers), Service-Oriented Architecture and Web Services (31 papers), Topic Modeling (26 papers), Mobile Crowdsensing and Crowdsourcing (25 papers), Advanced Graph Neural Networks (24 papers), Caching and Content Delivery (23 papers) and Machine Learning in Healthcare (22 papers). The work is most often cited by research in Information Systems (2.1k citations), Artificial Intelligence (2.2k citations), Computer Networks and Communications (1.0k citations), Transportation (275 citations) and Signal Processing (351 citations). Lizhen Cui has collaborated with scholars based in China, Singapore and Australia. Frequent co-authors include Hongzhi Yin, Junliang Yu, Xin Xia, Tong Chen, Quoc Viet Hung Nguyen, Xiangliang Zhang, Zi Huang, Qinyong Wang, Zhi Liu and Leyi Wei. Their work appears in journals such as IEEE Access, Briefings in Bioinformatics, IEEE Journal of Biomedical and Health Informatics, IEEE Transactions on Neural Networks and Learning Systems 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.