Jieming Zhu

6.0k total citations · 4 hit papers
79 papers, 3.3k citations indexed

About

Jieming Zhu is a scholar working on Information Systems, Artificial Intelligence and Computer Networks and Communications. According to data from OpenAlex, Jieming Zhu has authored 79 papers receiving a total of 3.3k indexed citations (citations by other indexed papers that have themselves been cited), including 49 papers in Information Systems, 32 papers in Artificial Intelligence and 25 papers in Computer Networks and Communications. Recurrent topics in Jieming Zhu's work include Recommender Systems and Techniques (33 papers), Topic Modeling (16 papers) and Software System Performance and Reliability (13 papers). Jieming Zhu is often cited by papers focused on Recommender Systems and Techniques (33 papers), Topic Modeling (16 papers) and Software System Performance and Reliability (13 papers). Jieming Zhu collaborates with scholars based in China, Hong Kong and United States. Jieming Zhu's co-authors include Michael R. Lyu, Pinjia He, Zibin Zheng, Shilin He, Xiuqiang He, Jian Li, Xi Xiao, Kelong Mao, Dongmei Zhang and Zhaowei Wang and has published in prestigious journals such as IEEE Transactions on Image Processing, IEEE Access and IEEE Transactions on Parallel and Distributed Systems.

In The Last Decade

Jieming Zhu

74 papers receiving 3.2k citations

Hit 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.

Drain: An Online Log Parsing Approach with Fixed Depth Tree

2017 453 citations Jieming Zhu, Michael R. Lyu et al. profile →
Experience Report: System Log Analysis for Anomaly Detection

2016 339 citations Jieming Zhu, Michael R. Lyu et al. profile →
Software Defect Prediction via Convolutional Neural Network

2017 294 citations Jieming Zhu, Michael R. Lyu et al. profile →
UltraGCN

2021 182 citations Jieming Zhu, Xiuqiang He et al. profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Jieming Zhu China	26	1.9k	1.8k	1.5k	602	453	79	3.3k
Wilson C. Hsieh United States	22	3.8k 2.0×	2.6k 1.4×	1.0k 0.7×	199 0.3×	339 0.7×	66	4.8k
Marin Litoiu Canada	26	1.7k 0.9×	1.6k 0.9×	787 0.5×	195 0.3×	100 0.2×	162	2.3k
Jiqiang Liu China	29	1.2k 0.6×	1.4k 0.8×	1.2k 0.8×	338 0.6×	280 0.6×	174	3.0k
Jian–Guang Lou China	30	2.1k 1.1×	1.4k 0.8×	1.9k 1.3×	698 1.2×	723 1.6×	114	3.9k
Bing Xie China	22	812 0.4×	1.2k 0.6×	542 0.4×	546 0.9×	156 0.3×	90	2.0k
Marco Vieira Portugal	28	1.4k 0.7×	1.9k 1.1×	1.0k 0.7×	754 1.3×	71 0.2×	223	3.4k
Jianling Sun China	20	366 0.2×	924 0.5×	514 0.3×	395 0.7×	166 0.4×	79	1.4k
Raluca Ada Popa United States	27	1.2k 0.6×	1.5k 0.8×	2.9k 2.0×	104 0.2×	322 0.7×	61	3.8k
Shi Han China	24	389 0.2×	591 0.3×	571 0.4×	304 0.5×	282 0.6×	84	1.7k
T.L. McCluskey United Kingdom	19	361 0.2×	777 0.4×	1.4k 0.9×	164 0.3×	336 0.7×	103	2.2k

Countries citing papers authored by Jieming Zhu

Since Specialization

Citations

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

Fields of papers citing papers by Jieming Zhu

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Jieming Zhu

This figure shows the co-authorship network connecting the top 25 collaborators of Jieming Zhu. A scholar is included among the top collaborators of Jieming Zhu 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 Jieming Zhu. Jieming Zhu 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.

Dai, Quanyu, et al.. (2025). MCNet: Monotonic Calibration Networks for Expressive Uncertainty Calibration in Online Advertising. ArXiv.org. 4408–4419. 1 indexed citations

2.

Huang, Guohe, Xun Li, Wenzhong Zhang, et al.. (2024). Urban renewal in China with the transition to high-quality development: Challenges and paths. 自然资源学报. 40(1). 1–1. 1 indexed citations

3.

Dai, Sunhao, Jieming Zhu, Zhenhua Dong, et al.. (2024). Modeling User Attention in Music Recommendation. 761–774. 2 indexed citations

4.

Du, Zhaocheng, et al.. (2024). A Tutorial on Feature Interpretation in Recommender Systems. 1281–1282. 2 indexed citations

5.

Du, Zhaocheng, et al.. (2024). LightCS: Selecting Quadratic Feature Crosses in Linear Complexity. 38–46. 1 indexed citations

6.

Zhu, Jieming, et al.. (2024). Enhancing News Recommendation with Real-Time Feedback and Generative Sequence Modeling. 32–36. 1 indexed citations

7.

Cai, Guohao, et al.. (2024). Counteracting Duration Bias in Video Recommendation via Counterfactual Watch Time. 4455–4466. 1 indexed citations

8.

Zhu, Jieming, et al.. (2024). Benchmarking News Recommendation in the Era of Green AI. 971–974. 4 indexed citations

9.

Zhu, Jieming, et al.. (2024). Discrete Semantic Tokenization for Deep CTR Prediction. 919–922. 3 indexed citations

10.

Liu, Weiwen, Jianghao Lin, Hong Zhu, et al.. (2024). Towards Open-World Recommendation with Knowledge Augmentation from Large Language Models. 12–22. 36 indexed citations

11.

Zhu, Jieming, et al.. (2024). EAGER: Two-Stream Generative Recommender with Behavior-Semantic Collaboration. 3245–3254. 5 indexed citations

12.

Fan, Lu, et al.. (2024). Learning Category Trees for ID-Based Recommendation: Exploring the Power of Differentiable Vector Quantization. 3521–3532. 2 indexed citations

13.

Zhu, Jieming, Yankai Chen, Guohao Cai, et al.. (2024). EASE: Learning Lightweight Semantic Feature Adapters from Large Language Models for CTR Prediction. 4819–4827.

14.

Zhu, Jieming, et al.. (2024). CoST: Contrastive Quantization based Semantic Tokenization for Generative Recommendation. 969–974. 4 indexed citations

15.

Liu, Jinyang, Yichen Li, Yintong Huo, et al.. (2024). A Large-Scale Evaluation for Log Parsing Techniques: How Far Are We?. 223–234. 17 indexed citations

16.

Zhu, Jieming, Guohao Cai, Junjie Huang, et al.. (2023). ReLoop2: Building Self-Adaptive Recommendation Models via Responsive Error Compensation Loop. 5728–5738. 2 indexed citations

17.

Zhu, Jieming, Guohao Cai, Quanyu Dai, et al.. (2023). FINAL: Factorized Interaction Layer for CTR Prediction. 2006–2010. 16 indexed citations

18.

Jin, Weike, Zhou Zhao, Xiaochun Cao, et al.. (2021). Adaptive Spatio-Temporal Graph Enhanced Vision-Language Representation for Video QA. IEEE Transactions on Image Processing. 30. 5477–5489. 22 indexed citations

19.

Cheng, Cailian, et al.. (2015). Clinical effect of recombinant human brain natriuretic peptide combined with levosimendan on acute myocardial infarction complicated by heart failure. Zhonghua laonian yixue zazhi. 34(11). 1231–1234.

20.

Żytkow, Jan M., et al.. (1992). Operational definition refinement: a discovery process. National Conference on Artificial Intelligence. 76–81. 6 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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