Fei Sun

9.9k total citations · 5 hit papers
66 papers, 4.4k citations indexed

About

Fei Sun is a scholar working on Artificial Intelligence, Information Systems and Computer Vision and Pattern Recognition. According to data from OpenAlex, Fei Sun has authored 66 papers receiving a total of 4.4k indexed citations (citations by other indexed papers that have themselves been cited), including 36 papers in Artificial Intelligence, 29 papers in Information Systems and 20 papers in Computer Vision and Pattern Recognition. Recurrent topics in Fei Sun's work include Recommender Systems and Techniques (28 papers), Topic Modeling (14 papers) and Advanced Graph Neural Networks (12 papers). Fei Sun is often cited by papers focused on Recommender Systems and Techniques (28 papers), Topic Modeling (14 papers) and Advanced Graph Neural Networks (12 papers). Fei Sun collaborates with scholars based in China, United States and Netherlands. Fei Sun's co-authors include Wenwu Ou, Changhua Pei, Peng Jiang, Lin Xiao, Jian Wu, Jun Liu, Bin Cui, Shiwen Wu, X. H. Xie and Wentao Zhang and has published in prestigious journals such as SHILAP Revista de lepidopterología, IEEE Access and ACM Computing Surveys.

In The Last Decade

Fei Sun

60 papers receiving 4.2k citations

Hit Papers

5 papers align trajectories

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.

BERT4Rec

2019 1.2k citations Fei Sun, Wenwu Ou et al. profile →
FBNet: Hardware-Aware Efficient ConvNet Design via Differentiable Neural Architecture Search

2019 718 citations Fei Sun et al. profile →
Graph Neural Networks in Recommender Systems: A Survey

2022 594 citations Shiwen Wu, Fei Sun et al. ACM Computing Surveys profile →
Learning in the Frequency Domain

2020 309 citations Fei Sun et al. profile →
Contrastive Learning for Sequential Recommendation

2022 301 citations Fei Sun, Shiwen Wu et al. profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Fei Sun China	22	2.7k	2.2k	1.7k	541	323	66	4.4k
Scott Sanner Canada	28	2.2k 0.8×	1.3k 0.6×	856 0.5×	315 0.6×	339 1.0×	149	3.7k
Fuzhen Zhuang China	31	2.9k 1.1×	2.1k 0.9×	843 0.5×	471 0.9×	287 0.9×	118	4.6k
Tal Shaked United States	8	3.2k 1.2×	2.8k 1.3×	1.2k 0.7×	667 1.2×	513 1.6×	9	5.0k
Gui-Rong Xue China	28	2.9k 1.1×	1.6k 0.7×	1.3k 0.8×	165 0.3×	378 1.2×	56	4.6k
Xiang Wang China	34	4.8k 1.8×	4.5k 2.0×	2.1k 1.2×	523 1.0×	752 2.3×	174	7.3k
Fuzheng Zhang China	22	3.6k 1.3×	3.0k 1.3×	852 0.5×	568 1.0×	354 1.1×	52	4.7k
Parham Moradi Iran	28	1.9k 0.7×	1.1k 0.5×	941 0.6×	175 0.3×	389 1.2×	108	3.2k
Qingsheng Zhu China	35	2.0k 0.7×	2.0k 0.9×	1.1k 0.6×	200 0.4×	1.3k 3.9×	146	4.3k
Ziyu Guan China	28	1.6k 0.6×	923 0.4×	1.1k 0.6×	194 0.4×	242 0.7×	133	2.9k

Countries citing papers authored by Fei Sun

Since Specialization

Citations

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

Fields of papers citing papers by Fei Sun

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Fei Sun

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

All Works

Sort: Min cites: Since: Top N: Style:

20 of 20 papers shown

1.

Wu, Yunfan, Yingqiang Ge, Shuchang Liu, et al.. (2025). The 1st Workshop on Human-Centered Recommender Systems. 2088–2091. 1 indexed citations

2.

Cao, Qi, et al.. (2025). Personalized Denoising Implicit Feedback for Robust Recommender System. 4470–4481.

3.

Cao, Qi, et al.. (2025). Robust Recommender System: A Survey and Future Directions. ACM Computing Surveys. 58(1). 1–38.

4.

Sun, Fei, et al.. (2025). Information service and quality strategies in a supply chain. Transportation Research Part E Logistics and Transportation Review. 199. 104169–104169. 1 indexed citations

5.

Sun, Fei, Bilge Acun, Zachary DeVito, et al.. (2024). Generative AI Beyond LLMs: System Implications of Multi-Modal Generation. 257–267. 7 indexed citations

6.

Jiang, Peng, et al.. (2024). MMGCL: Meta Knowledge-Enhanced Multi-view Graph Contrastive Learning for Recommendations. 538–548. 2 indexed citations

7.

Wu, Yunfan, et al.. (2024). Accelerating the Surrogate Retraining for Poisoning Attacks against Recommender Systems. 701–711. 2 indexed citations

8.

Sun, Fei, et al.. (2024). Unlink to Unlearn: Simplifying Edge Unlearning in GNNs. 489–492. 5 indexed citations

9.

Cao, Qi, et al.. (2024). LoRec: Combating Poisons with Large Language Model for Robust Sequential Recommendation. 1733–1742. 6 indexed citations

10.

Jawahar, Ganesh, Haichuan Yang, Yunyang Xiong, et al.. (2024). Mixture-of-Supernets: Improving Weight-Sharing Supernet Training with Architecture-Routed Mixture-of-Experts. 10424–10443.

11.

Xu, Chengjin, et al.. (2024). Toward Practical Entity Alignment Method Design: Insights from New Highly Heterogeneous Knowledge Graph Datasets. 2325–2336. 4 indexed citations

12.

Sun, Fei, et al.. (2024). Blinded by Generated Contexts: How Language Models Merge Generated and Retrieved Contexts When Knowledge Conflicts?. 6207–6227.

13.

Wang, Shengnan, et al.. (2024). A ConvLSTM model with word-level attention for sentiment analysis of review data. 86–86. 1 indexed citations

14.

Sun, Fei, Jing Chen, Yang Hui, Bintong Chen, & Zeying Wan. (2023). Production outsourcing decision in the presence of consumer’s preference uncertainty. Transportation Research Part E Logistics and Transportation Review. 175. 103180–103180. 6 indexed citations

15.

Cao, Qi, Huawei Shen, Yunfan Wu, et al.. (2023). Adversarial camouflage for node injection attack on graphs. Information Sciences. 649. 119611–119611. 16 indexed citations

16.

Chen, Ziqian, et al.. (2022). Studying the Impact of Data Disclosure Mechanism in Recommender Systems via Simulation. ACM Transactions on Information Systems. 41(3). 1–26. 6 indexed citations

17.

Wu, Shiwen, Fei Sun, Wentao Zhang, X. H. Xie, & Bin Cui. (2022). Graph Neural Networks in Recommender Systems: A Survey. ACM Computing Surveys. 55(5). 1–37. 594 indexed citations breakdown →

18.

Zhang, Yuan, Fei Sun, Xiaoyong Yang, et al.. (2020). Graph-based Regularization on Embedding Layers for Recommendation. ACM Transactions on Information Systems. 39(1). 1–27. 12 indexed citations

19.

Sun, Fei, Jiafeng Guo, Yanyan Lan, Jun Xu, & Xueqi Cheng. (2016). Sparse word embeddings using l 1 regularized online learning. International Joint Conference on Artificial Intelligence. 2915–2921. 21 indexed citations

20.

Sun, Fei, et al.. (2014). Method Research on Cultivated Land Extraction Based on Object One-Class Classification of High-Spatial-Resolution Images. Bulletin of Surveying and Mapping. 78. 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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