World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
51
Citations
10715
World Ranking
5325
National Ranking
713

Shaoping Ma publication distribution in Computer Science in 2026

The chart shows the distribution of publications by all Research.com ranked scientists in the field of Computer Science in 2026. The highlighted bar marks where Shaoping Ma sits on this spectrum.

32–41 publications: 7 scientists 42–51 publications: 22 scientists 52–61 publications: 82 scientists 62–71 publications: 134 scientists 72–81 publications: 249 scientists 82–91 publications: 324 scientists 92–101 publications: 421 scientists 102–111 publications: 420 scientists 112–121 publications: 497 scientists 122–131 publications: 544 scientists 132–141 publications: 555 scientists 142–151 publications: 609 scientists 152–161 publications: 559 scientists 162–171 publications: 534 scientists 172–181 publications: 556 scientists 182–191 publications: 583 scientists 192–201 publications: 519 scientists 202–211 publications: 508 scientists 212–221 publications: 490 scientists 222–231 publications: 437 scientists 232–241 publications: 423 scientists 242–251 publications: 408 scientists 252–261 publications: 377 scientists 262–271 publications: 301 scientists 272–281 publications: 335 scientists 282–291 publications: 320 scientists 292–301 publications: 293 scientists 302–311 publications: 250 scientists 312–321 publications: 238 scientists 322–331 publications: 206 scientists 332–341 publications: 209 scientists 342–351 publications: 208 scientists 352–361 publications: 162 scientists 362–371 publications: 176 scientists 372–381 publications: 127 scientists 382–391 publications: 158 scientists 392–401 publications: 128 scientists 402–411 publications: 104 scientists 412–421 publications: 94 scientists 422–431 publications: 99 scientists 432–441 publications: 83 scientists 442–451 publications: 108 scientists 452–461 publications: 73 scientists 462–471 publications: 77 scientists 472–481 publications: 69 scientists 482–491 publications: 84 scientists 492–501 publications: 62 scientists 502–511 publications: 54 scientists 512–521 publications: 57 scientists 522–531 publications: 51 scientists 532–541 publications: 51 scientists 542–551 publications: 32 scientists 552–561 publications: 38 scientists 562–571 publications: 28 scientists 572–581 publications: 43 scientists 582–591 publications: 33 scientists 592–601 publications: 41 scientists 602–611 publications: 32 scientists 612–621 publications: 28 scientists 622–631 publications: 25 scientists 632–641 publications: 27 scientists 642–651 publications: 17 scientists 652–661 publications: 20 scientists 662–671 publications: 17 scientists 672–681 publications: 15 scientists 682–691 publications: 14 scientists 692–701 publications: 21 scientists 702–711 publications: 13 scientists 712–721 publications: 12 scientists 722–731 publications: 19 scientists 732–741 publications: 14 scientists 742–751 publications: 12 scientists 752–761 publications: 10 scientists 762–771 publications: 10 scientists 772–781 publications: 11 scientists 782–791 publications: 10 scientists 792–801 publications: 11 scientists 802–811 publications: 8 scientists 812–821 publications: 8 scientists 822–831 publications: 7 scientists 832–841 publications: 11 scientists 842–851 publications: 10 scientists 852–861 publications: 5 scientists 862–871 publications: 9 scientists 872–881 publications: 4 scientists 882–891 publications: 6 scientists 892–901 publications: 3 scientists 902–911 publications: 6 scientists 912–921 publications: 3 scientists 922–931 publications: 2 scientists 932–941 publications: 2 scientists 942–951 publications: 2 scientists 952–961 publications: 3 scientists 962–971 publications: 3 scientists 972–981 publications: 3 scientists 982–990 publications: 5 scientists 991+ publications: 100 scientists
32 publications 991+

This scientist: 343 publications — 81st percentile

81% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 991 publications or more.

Shaoping Ma D-index placement in Computer Science in 2026

The chart shows the D-index (discipline H-index) distribution of Computer Science scientists ranked by Research.com in 2026. The highlighted bar marks where Shaoping Ma sits on this spectrum.

30–31 D-Index: 879 scientists 32–33 D-Index: 983 scientists 34–35 D-Index: 918 scientists 36–37 D-Index: 990 scientists 38–39 D-Index: 968 scientists 40–41 D-Index: 907 scientists 42–43 D-Index: 821 scientists 44–45 D-Index: 763 scientists 46–47 D-Index: 689 scientists 48–49 D-Index: 543 scientists 50–51 D-Index: 543 scientists 52–53 D-Index: 518 scientists 54–55 D-Index: 500 scientists 56–57 D-Index: 458 scientists 58–59 D-Index: 400 scientists 60–61 D-Index: 337 scientists 62–63 D-Index: 308 scientists 64–65 D-Index: 292 scientists 66–67 D-Index: 249 scientists 68–69 D-Index: 213 scientists 70–71 D-Index: 192 scientists 72–73 D-Index: 189 scientists 74–75 D-Index: 165 scientists 76–77 D-Index: 139 scientists 78–79 D-Index: 119 scientists 80–81 D-Index: 121 scientists 82–83 D-Index: 113 scientists 84–85 D-Index: 88 scientists 86–87 D-Index: 87 scientists 88–89 D-Index: 75 scientists 90–91 D-Index: 69 scientists 92–93 D-Index: 57 scientists 94–95 D-Index: 46 scientists 96–97 D-Index: 38 scientists 98–99 D-Index: 34 scientists 100–101 D-Index: 36 scientists 102–103 D-Index: 27 scientists 104–105 D-Index: 37 scientists 106–107 D-Index: 18 scientists 108–109 D-Index: 31 scientists 110–111 D-Index: 19 scientists 112–113 D-Index: 16 scientists 114–115 D-Index: 12 scientists 116–117 D-Index: 20 scientists 118–119 D-Index: 15 scientists 120–121 D-Index: 5 scientists 122–123 D-Index: 20 scientists 124–125 D-Index: 8 scientists 126–127 D-Index: 5 scientists 128–129 D-Index: 7 scientists 130 D-Index: 3 scientists 131+ D-Index: 98 scientists
30 D-Index 131+

This scientist: 51 D-Index — 63rd percentile

63% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 131 D-Index or more.

Overview

Shaoping Ma is affiliated with Tsinghua University in China and conducts research primarily in computer science, with a strong focus on artificial intelligence and information systems. Their work spans several subfields, including computer vision and pattern recognition, management science and operations research, and cognitive neuroscience.

The researcher has contributed to various topics within their field, notably topic modeling, recommender systems and techniques, domain adaptation and few-shot learning, advanced bandit algorithms research, information retrieval and search behavior, advanced graph neural networks, and studies related to head and neck cancer.

Among the recent papers authored or co-authored by Shaoping Ma are:

  • A Survey on the Fairness of Recommender Systems, 2022, ACM Transactions on Information Systems
  • Efficient Neural Matrix Factorization without Sampling for Recommendation, 2020, ACM Transactions on Information Systems
  • Graph Heterogeneous Multi-Relational Recommendation, 2021, Proceedings of the AAAI Conference on Artificial Intelligence
  • Towards Representation Alignment and Uniformity in Collaborative Filtering, 2022, Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining
  • Efficient Heterogeneous Collaborative Filtering without Negative Sampling for Recommendation, 2020, Proceedings of the AAAI Conference on Artificial Intelligence

The scientist frequently publishes in journals and conferences such as arXiv (Cornell University), International Journal of Radiation Oncology*Biology*Physics, ACM Transactions on Information Systems, Proceedings of the Fifteenth ACM International Conference on Web Search and Data Mining, and the Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval.

Shaoping Ma often collaborates with other researchers including Yiqun Liu, Jiaxin Mao, Weizhi Ma, Min Zhang, and Jingtao Zhan. Collaboration counts indicate ongoing joint work with these colleagues across multiple publications.

Best Publications

  • Explicit factor models for explainable recommendation based on phrase-level sentiment analysis

    Yongfeng Zhang;Guokun Lai;Min Zhang;Yi Zhang

  • Neural Attentional Rating Regression with Review-level Explanations

    Chong Chen;Min Zhang;Yiqun Liu;Shaoping Ma

  • How good your recommender system is? A survey on evaluations in recommendation

    Thiago Silveira;Min Zhang;Xiao Lin;Yiqun Liu

  • Optimizing Dense Retrieval Model Training with Hard Negatives

    Jingtao Zhan;Jiaxin Mao;Yiqun Liu;Jiafeng Guo

  • Jointly Learning Explainable Rules for Recommendation with Knowledge Graph

    Weizhi Ma;Min Zhang;Yue Cao;Woojeong Jin

  • Towards Representation Alignment and Uniformity in Collaborative Filtering

    Unknown

  • Efficient Neural Matrix Factorization without Sampling for Recommendation

    Chong Chen;Min Zhang;Yongfeng Zhang;Yiqun Liu

  • Graph Heterogeneous Multi-Relational Recommendation

    Chong Chen;Weizhi Ma;Min Zhang;Zhaowei Wang

  • Efficient Heterogeneous Collaborative Filtering without Negative Sampling for Recommendation

    Chong Chen;Min Zhang;Yongfeng Zhang;Weizhi Ma

  • An Efficient Adaptive Transfer Neural Network for Social-aware Recommendation

    Chong Chen;Min Zhang;Chenyang Wang;Weizhi Ma

  • Rating-boosted latent topics: understanding users and items with ratings and reviews

    Yunzhi Tan;Min Zhang;Yiqun Liu;Shaoping Ma

  • Discover breaking events with popular hashtags in twitter

    Anqi Cui;Min Zhang;Yiqun Liu;Shaoping Ma

  • Automatic online news issue construction in web environment

    Canhui Wang;Min Zhang;Shaoping Ma;Liyun Ru

  • BERT-PLI: Modeling Paragraph-Level Interactions for Legal Case Retrieval

    Yunqiu Shao;Jiaxin Mao;Yiqun Liu;Weizhi Ma

  • Social Attentional Memory Network: Modeling Aspect- and Friend-Level Differences in Recommendation

    Chong Chen;Min Zhang;Yiqun Liu;Shaoping Ma

  • Make It a Chorus: Knowledge- and Time-aware Item Modeling for Sequential Recommendation

    Chenyang Wang;Min Zhang;Weizhi Ma;Yiqun Liu

  • Automatic query type identification based on click through information

    Yiqun Liu;Min Zhang;Liyun Ru;Shaoping Ma

  • Do users rate or review?: boost phrase-level sentiment labeling with review-level sentiment classification

    Yongfeng Zhang;Haochen Zhang;Min Zhang;Yiqun Liu

  • Automatic online news topic ranking using media focus and user attention based on aging theory

    Canhui Wang;Min Zhang;Liyun Ru;Shaoping Ma

  • When does Relevance Mean Usefulness and User Satisfaction in Web Search

    Jiaxin Mao;Yiqun Liu;Ke Zhou;Jian-Yun Nie

  • Finding Experts Using Social Network Analysis

    Yupeng Fu;Rongjing Xiang;Yiqun Liu;Min Zhang

  • Emotion tokens: bridging the gap among multilingual twitter sentiment analysis

    Anqi Cui;Min Zhang;Yiqun Liu;Shaoping Ma

  • Jointly Learning Explainable Rules for Recommendation with Knowledge Graph

    Weizhi Ma;Min Zhang;Yue Cao;Woojeong

Frequent Co-Authors

Min Zhang
Min Zhang Tsinghua University
Yongfeng Zhang
Yongfeng Zhang Rutgers, The State University of New Jersey
Xiaohui Xie
Xiaohui Xie University of California, Irvine
Jian-Yun Nie
Jian-Yun Nie University of Montreal
Maarten de Rijke
Maarten de Rijke University of Amsterdam
Min-Yen Kan
Min-Yen Kan National University of Singapore
Meng Wang
Meng Wang Hefei University of Technology
Xiang Ren
Xiang Ren University of Southern California
Xiuqiang He
Xiuqiang He Huawei Technologies (China)
Qi Tian
Qi Tian Huawei Technologies (China)

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