Jun Ma

5.1k total citations · 2 hit papers
104 papers, 3.1k citations indexed

About

Jun Ma is a scholar working on Information Systems, Artificial Intelligence and Computer Vision and Pattern Recognition. According to data from OpenAlex, Jun Ma has authored 104 papers receiving a total of 3.1k indexed citations (citations by other indexed papers that have themselves been cited), including 52 papers in Information Systems, 50 papers in Artificial Intelligence and 38 papers in Computer Vision and Pattern Recognition. Recurrent topics in Jun Ma's work include Recommender Systems and Techniques (30 papers), Advanced Image and Video Retrieval Techniques (16 papers) and Image Retrieval and Classification Techniques (15 papers). Jun Ma is often cited by papers focused on Recommender Systems and Techniques (30 papers), Advanced Image and Video Retrieval Techniques (16 papers) and Image Retrieval and Classification Techniques (15 papers). Jun Ma collaborates with scholars based in China, Netherlands and United States. Jun Ma's co-authors include Zhumin Chen, Pengjie Ren, Zhaochun Ren, Maarten de Rijke, Tao Lian, Jing Li, Imran Qureshi, Qaisar Abbas, Shuaiqiang Wang and Liqiang Nie and has published in prestigious journals such as Expert Systems with Applications, IEEE Access and Information Sciences.

In The Last Decade

Jun Ma

93 papers receiving 3.0k citations

Hit Papers

Neural Attentive Session-based Recommendation 2017 2026 2020 2023 2017 2021 250 500 750
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. Neural Attentive Session-based Recommendation
    2017 851 citations Pengjie Ren, Zhumin Chen et al. profile →
  2. Diabetic retinopathy detection and stage classification in eye fundus images using active deep learning
    2021 125 citations Jun Ma 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
Jun Ma China 28 1.9k 1.8k 899 467 261 104 3.1k
Kenji Yamanishi Japan 24 460 0.2× 1.8k 1.0× 213 0.2× 108 0.2× 533 2.0× 128 2.6k
Parham Moradi Iran 28 1.1k 0.6× 1.9k 1.1× 941 1.0× 175 0.4× 389 1.5× 108 3.2k
Thomas Lukasiewicz United Kingdom 33 591 0.3× 2.9k 1.6× 524 0.6× 236 0.5× 811 3.1× 216 3.6k
Shoujin Wang China 22 1.1k 0.6× 1.2k 0.7× 404 0.4× 270 0.6× 203 0.8× 103 1.9k
Fangzhao Wu China 37 2.1k 1.1× 3.5k 1.9× 644 0.7× 192 0.4× 306 1.2× 118 4.2k
Zhiting Hu United States 27 747 0.4× 1.4k 0.8× 842 0.9× 105 0.2× 194 0.7× 54 2.4k
Jason D. M. Rennie United States 12 993 0.5× 1.8k 1.0× 478 0.5× 167 0.4× 295 1.1× 19 2.6k
Xiuqiang He China 27 1.7k 0.9× 1.6k 0.9× 744 0.8× 463 1.0× 238 0.9× 99 2.5k
Ruiming Tang China 26 1.6k 0.8× 1.4k 0.8× 548 0.6× 483 1.0× 205 0.8× 150 2.2k

Countries citing papers authored by Jun Ma

Since Specialization
Citations

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

Fields of papers citing papers by Jun Ma

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Jun Ma

This figure shows the co-authorship network connecting the top 25 collaborators of Jun Ma. A scholar is included among the top collaborators of Jun Ma 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 Jun Ma. Jun Ma 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.
2.
Ma, Jun, et al.. (2025). HCVT-Net: A hybrid CNN-Transformer network for self-supervised 3D seismic data interpolation. Journal of Applied Geophysics. 242. 105873–105873.
3.
4.
Yu, Haiyang, et al.. (2025). Enhancing head and neck cancer detection accuracy in digitized whole-slide histology with the HNSC-classifier: a deep learning approach. Frontiers in Molecular Biosciences. 12. 1652144–1652144. 1 indexed citations
5.
Li, Ang, Peter Kok-Yiu Wong, Xingyu Tao, Jun Ma, & Jack C.P. Cheng. (2025). An interactive system for 3D spatial relationship query by integrating tree-based element indexing and LLM-based agent. Advanced Engineering Informatics. 66. 103375–103375. 3 indexed citations
6.
Zhang, Mengqi, Zhumin Chen, Jun Ma, et al.. (2024). Generative Retrieval as Multi-Vector Dense Retrieval. Leiden Repository (Leiden University). 1828–1838. 3 indexed citations
7.
Chen, Zhumin, et al.. (2023). Center-retained fine-tuning for conversational question ranking through unsupervised center identification. Information Processing & Management. 61(2). 103578–103578. 1 indexed citations
8.
Ren, Pengjie, Zhumin Chen, Zhaochun Ren, et al.. (2022). Mixed Information Flow for Cross-Domain Sequential Recommendations. ACM Transactions on Knowledge Discovery from Data. 16(4). 1–32. 32 indexed citations
9.
Ren, Pengjie, et al.. (2022). Improving Transformer-based Sequential Recommenders through Preference Editing. ACM Transactions on Information Systems. 41(3). 1–24. 15 indexed citations
10.
Wang, Shanshan, Pengjie Ren, Zhumin Chen, et al.. (2020). Coding Electronic Health Records with Adversarial Reinforcement Path Generation. UvA-DARE (University of Amsterdam). 801–810. 11 indexed citations
11.
Lin, Yujie, et al.. (2019). Explainable Outfit Recommendation with Joint Outfit Matching and Comment Generation. IEEE Transactions on Knowledge and Data Engineering. 32(8). 1502–1516. 89 indexed citations
12.
Ma, Jun, et al.. (2015). Signal‐walking‐driven active contour model. IET Image Processing. 9(12). 1101–1106. 1 indexed citations
13.
Cui, Chaoran, Jialie Shen, Jun Ma, & Tao Lian. (2015). Social Tag Relevance Estimation via Ranking-Oriented Neighbour Voting. 895–898. 3 indexed citations
14.
Gao, Shuai, Jun Ma, & Zhumin Chen. (2014). Effective and effortless features for popularity prediction in microblogging network. 269–270. 32 indexed citations
15.
Ma, Jun. (2010). Automatic structured data extraction from Web forums. Journal of Shandong University. 1 indexed citations
16.
Ma, Jun. (2009). PageRank Based Algorithm for Scientific Literature Quality Evaluation. Journal of Guangxi Normal University. 1 indexed citations
17.
Ma, Jun, et al.. (2008). Clustering deep web databases semantically. 365–376. 2 indexed citations
18.
Chen, Zhumin, Jun Ma, Xiaohui Han, & Dongmei Zhang. (2008). An effective relevance prediction algorithm based on hierarchical taxonomy for focused crawling. 613–619. 3 indexed citations
19.
Zhou, Linhua, et al.. (2008). Ontology-based Scientific Data Service Composition: A Query Rewriting-based Approach.. National Conference on Artificial Intelligence. 116–121. 2 indexed citations
20.

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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