Chaochao Chen

6.6k total citations · 1 hit paper
170 papers, 4.2k citations indexed

About

Chaochao Chen is a scholar working on Artificial Intelligence, Information Systems and Computer Vision and Pattern Recognition. According to data from OpenAlex, Chaochao Chen has authored 170 papers receiving a total of 4.2k indexed citations (citations by other indexed papers that have themselves been cited), including 89 papers in Artificial Intelligence, 68 papers in Information Systems and 21 papers in Computer Vision and Pattern Recognition. Recurrent topics in Chaochao Chen's work include Recommender Systems and Techniques (62 papers), Advanced Graph Neural Networks (36 papers) and Privacy-Preserving Technologies in Data (28 papers). Chaochao Chen is often cited by papers focused on Recommender Systems and Techniques (62 papers), Advanced Graph Neural Networks (36 papers) and Privacy-Preserving Technologies in Data (28 papers). Chaochao Chen collaborates with scholars based in China, United States and Australia. Chaochao Chen's co-authors include Michael Pecht, Xiaolin Zheng, Nicholas Williard, George Vachtsevanos, Yan Wang, Wei He, Marcos E. Orchard, Jun Zhou, Guanfeng Liu and Xiaolong Li and has published in prestigious journals such as Journal of Agricultural and Food Chemistry, Food Chemistry and IEEE Transactions on Industrial Electronics.

In The Last Decade

Chaochao Chen

148 papers receiving 4.1k citations

Hit Papers

State of charge estimation for Li-ion batteries using neu... 2014 2026 2018 2022 2014 100 200 300 400
What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

  1. State of charge estimation for Li-ion batteries using neural network modeling and unscented Kalman filter-based error cancellation
    2014 418 citations Chaochao Chen et al. profile →

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Chaochao Chen China 33 1.8k 1.4k 889 807 733 170 4.2k
Luciano Sánchez Spain 30 4.0k 2.2× 674 0.5× 802 0.9× 389 0.5× 414 0.6× 160 5.8k
Meng Li China 30 1.0k 0.6× 841 0.6× 1.2k 1.4× 686 0.9× 337 0.5× 266 3.7k
Qijun Chen China 35 895 0.5× 801 0.6× 887 1.0× 940 1.2× 1.8k 2.4× 281 5.5k
Jianping Wang China 42 1.3k 0.7× 685 0.5× 3.0k 3.4× 791 1.0× 1.1k 1.4× 547 7.9k
Haiying Shen United States 38 870 0.5× 2.1k 1.5× 1.4k 1.5× 623 0.8× 645 0.9× 416 6.9k
Francesco Marcelloni Italy 37 2.4k 1.3× 847 0.6× 636 0.7× 124 0.2× 275 0.4× 212 4.9k
El‐Ghazali Talbi France 30 1.4k 0.8× 570 0.4× 295 0.3× 331 0.4× 340 0.5× 101 3.9k
Yi Mei New Zealand 40 3.1k 1.7× 321 0.2× 696 0.8× 247 0.3× 503 0.7× 250 6.1k
Shengzhong Feng China 30 745 0.4× 617 0.5× 614 0.7× 318 0.4× 168 0.2× 108 2.8k
Liang Feng China 37 3.7k 2.0× 233 0.2× 716 0.8× 286 0.4× 343 0.5× 161 6.2k

Countries citing papers authored by Chaochao Chen

Since Specialization
Citations

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

Fields of papers citing papers by Chaochao Chen

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Chaochao Chen

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

All Works

20 of 20 papers shown
1.
Li, Yuyuan, et al.. (2025). Multi-Objective Unlearning in Recommender Systems via Preference Guided Pareto Exploration. IEEE Transactions on Services Computing. 18(5). 3052–3064.
2.
Li, Yuyuan, et al.. (2025). Plug and Play: Enabling Pluggable Attribute Unlearning in Recommender Systems. 2689–2699. 1 indexed citations
3.
Chen, Chaochao, et al.. (2025). FedGF: Enhancing Structural Knowledge via Graph Factorization for Federated Graph Learning. 448–456. 1 indexed citations
4.
5.
Chen, Chaochao, et al.. (2024). Intra- and Inter-group Optimal Transport for User-Oriented Fairness in Recommender Systems. Proceedings of the AAAI Conference on Artificial Intelligence. 38(8). 8463–8471. 1 indexed citations
6.
Zhou, Qingyun, Zhiying Zheng, Chaochao Chen, et al.. (2024). Activation of ACE2/Ang-(1–7)/Mas axis improves cognitive dysfunction induced by isoflurane in mice via inhibiting oxidative stress. 3(3). 123–131. 1 indexed citations
7.
Zheng, Xiaolin, et al.. (2024). Mining User Consistent and Robust Preference for Unified Cross Domain Recommendation. IEEE Transactions on Knowledge and Data Engineering. 36(12). 8758–8772. 1 indexed citations
8.
9.
Tan, Yanchao, Wenzhong Guo, Weiming Liu, et al.. (2024). Logical Relation Modeling and Mining in Hyperbolic Space for Recommendation. 1310–1323.
10.
11.
Chen, Chaochao, et al.. (2024). Post-Training Attribute Unlearning in Recommender Systems. ACM Transactions on Information Systems. 43(1). 1–28. 8 indexed citations
12.
Li, Yuyuan, et al.. (2023). Making recommender systems forget: Learning and unlearning for erasable recommendation. Knowledge-Based Systems. 283. 111124–111124. 18 indexed citations
13.
Zheng, Xiaolin, et al.. (2023). Intra and Inter Domain HyperGraph Convolutional Network for Cross-Domain Recommendation. 449–459. 14 indexed citations
14.
Liu, Weiming, et al.. (2023). Contrastive Proxy Kernel Stein Path Alignment for Cross-Domain Cold-Start Recommendation. IEEE Transactions on Knowledge and Data Engineering. 35(11). 11216–11230. 22 indexed citations
15.
Tan, Yanchao, Weiming Liu, Fan Wang, et al.. (2023). Contrastive Intra- and Inter-Modality Generation for Enhancing Incomplete Multimedia Recommendation. 6234–6242. 9 indexed citations
16.
Zheng, Xiaolin, et al.. (2023). Decentralized Graph Neural Network for Privacy-Preserving Recommendation. 3494–3504. 6 indexed citations
17.
Chen, Chaochao, et al.. (2022). Towards secure and practical machine learning via secret sharing and random permutation. Knowledge-Based Systems. 245. 108609–108609. 12 indexed citations
18.
Zhou, Jun, et al.. (2021). ASFGNN: Automated separated-federated graph neural network. Peer-to-Peer Networking and Applications. 14(3). 1692–1704. 46 indexed citations
19.
Li, Yuyuan, et al.. (2021). Exponential Gradient with Momentum for Online Portfolio Selection. Expert Systems with Applications. 187. 115889–115889. 16 indexed citations
20.
Chen, Chaochao, Jun Zhou, Bingzhe Wu, et al.. (2020). Practical Privacy Preserving POI Recommendation. ACM Transactions on Intelligent Systems and Technology. 11(5). 1–20. 53 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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