Jinze Wu

437 total citations
19 papers, 244 citations indexed

About

Jinze Wu is a scholar working on Artificial Intelligence, Computer Science Applications and Information Systems. According to data from OpenAlex, Jinze Wu has authored 19 papers receiving a total of 244 indexed citations (citations by other indexed papers that have themselves been cited), including 14 papers in Artificial Intelligence, 7 papers in Computer Science Applications and 3 papers in Information Systems. Recurrent topics in Jinze Wu's work include Intelligent Tutoring Systems and Adaptive Learning (9 papers), Online Learning and Analytics (7 papers) and Topic Modeling (4 papers). Jinze Wu is often cited by papers focused on Intelligent Tutoring Systems and Adaptive Learning (9 papers), Online Learning and Analytics (7 papers) and Topic Modeling (4 papers). Jinze Wu collaborates with scholars based in China, United States and Canada. Jinze Wu's co-authors include Enhong Chen, Zhenya Huang, Qi Liu, Hao Wang, Jinfeng Yi, Bowen Zhou, Yu Su, Lei Zhang, Shijin Wang and Yuqiang Zhou and has published in prestigious journals such as Expert Systems with Applications, Knowledge-Based Systems and Proceedings of the VLDB Endowment.

In The Last Decade

Jinze Wu

16 papers receiving 242 citations

Peers

Jinze Wu
Weibo Gao China
Ekaterina Kochmar United Kingdom
Asmaa Elbadrawy United States
Shaghayegh Sahebi United States
Yufei Xue China
Michael Mayo New Zealand
Marko Rosić Croatia
Jinze Wu
Citations per year, relative to Jinze Wu Jinze Wu (= 1×) peers Sruti Srinivasa Ragavan

Countries citing papers authored by Jinze Wu

Since Specialization
Citations

This map shows the geographic impact of Jinze Wu'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 Jinze Wu with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Jinze Wu more than expected).

Fields of papers citing papers by Jinze Wu

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Jinze Wu. 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 Jinze Wu. The network helps show where Jinze Wu may publish in the future.

Co-authorship network of co-authors of Jinze Wu

This figure shows the co-authorship network connecting the top 25 collaborators of Jinze Wu. A scholar is included among the top collaborators of Jinze Wu 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 Jinze Wu. Jinze Wu is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

19 of 19 papers shown
1.
Liu, Qi, et al.. (2024). Unified Uncertainty Estimation for Cognitive Diagnosis Models. arXiv (Cornell University). 3545–3554. 2 indexed citations
2.
Shen, Shuanghong, et al.. (2024). Constructing a Confidence-guided Multigraph Model for cognitive diagnosis in personalized learning. Expert Systems with Applications. 252. 124259–124259. 3 indexed citations
4.
Wu, Jinze, et al.. (2024). Difficult Airway Assessment Based on Multi-View Metric Learning. Bioengineering. 11(7). 703–703. 3 indexed citations
5.
Chen, Enhong, Zhenya Huang, Jiayu Liu, et al.. (2024). SocraticLM: Exploring Socratic Personalized Teaching with Large Language Models. 85693–85721.
6.
Shen, Shuanghong, et al.. (2024). Item-Difficulty-Aware Learning Path Recommendation: From a Real Walking Perspective. 4167–4178. 1 indexed citations
7.
9.
Huang, Zhenya, et al.. (2023). Learning Behavior-oriented Knowledge Tracing. 2789–2800. 24 indexed citations
10.
Chen, Enhong, et al.. (2023). BETA-CD: A Bayesian Meta-Learned Cognitive Diagnosis Framework for Personalized Learning. Proceedings of the AAAI Conference on Artificial Intelligence. 37(4). 5018–5026. 8 indexed citations
11.
Liu, Qi, Jinze Wu, Zhenya Huang, et al.. (2023). Federated User Modeling from Hierarchical Information. ACM Transactions on Information Systems. 41(2). 1–33. 30 indexed citations
12.
Su, Yu, Jinze Wu, Yanmin Dong, et al.. (2022). Graph-based cognitive diagnosis for intelligent tutoring systems. Knowledge-Based Systems. 253. 109547–109547. 16 indexed citations
13.
Wang, Xiaoying, et al.. (2022). ConnectorX. Proceedings of the VLDB Endowment. 15(11). 2994–3003. 5 indexed citations
14.
Su, Yu, et al.. (2021). Time-and-Concept Enhanced Deep Multidimensional Item Response Theory for interpretable Knowledge Tracing. Knowledge-Based Systems. 218. 106819–106819. 25 indexed citations
15.
Wu, Jinze, Qi Liu, Zhenya Huang, et al.. (2021). Hierarchical Personalized Federated Learning for User Modeling. 957–968. 59 indexed citations
16.
Zhou, Yuqiang, Qi Liu, Jinze Wu, et al.. (2021). Modeling Context-aware Features for Cognitive Diagnosis in Student Learning. 2420–2428. 34 indexed citations
17.
Wu, Jinze, Zhenya Huang, Qi Liu, et al.. (2021). Federated Deep Knowledge Tracing. 662–670. 10 indexed citations
18.
Huang, Zhenya, Qi Liu, Weibo Gao, et al.. (2020). Neural Mathematical Solver with Enhanced Formula Structure. 1729–1732. 19 indexed citations
19.
Lei, Ting, et al.. (2018). A New Grasping Mode Based on a Sucked-type Underactuated Hand. Chinese Journal of Mechanical Engineering. 31(1). 1 indexed citations

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.

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