World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
56
Citations
12417
World Ranking
4080
National Ranking
1938

Yongfeng Zhang 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 Yongfeng Zhang 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: 198 publications — 46th percentile

46% of scientists in this discipline score the same or lower.

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

Yongfeng Zhang 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 Yongfeng Zhang 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: 56 D-Index — 72nd percentile

72% of scientists in this discipline score the same or lower.

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

Overview

Yongfeng Zhang is affiliated with Rutgers, The State University of New Jersey in the United States. Their research primarily falls within the field of Computer Science, with a focus on several subfields including Artificial Intelligence, Information Systems, Computer Vision and Pattern Recognition, as well as Management Science and Operations Research.

The main topics addressed in their work cover Recommender Systems and Techniques, Topic Modeling, Advanced Graph Neural Networks, Explainable Artificial Intelligence (XAI), Natural Language Processing Techniques, Advanced Bandit Algorithms Research, and Multimodal Machine Learning Applications.

Their recent publication record showcases contributions to various academic venues. Selected recent papers include:

  • CLIP-Adapter: Better Vision-Language Models with Feature Adapters, 2023, International Journal of Computer Vision
  • Efficient Neural Matrix Factorization without Sampling for Recommendation, 2020, ACM Transactions on Information Systems
  • Efficient Heterogeneous Collaborative Filtering without Negative Sampling for Recommendation, 2020, Proceedings of the AAAI Conference on Artificial Intelligence
  • Personalized Prompt Learning for Explainable Recommendation, 2023, ACM Transactions on Information Systems
  • CLIP-Adapter: Better Vision-Language Models with Feature Adapters, 2021, arXiv (Cornell University)

Yongfeng Zhang frequently publishes in several venues, including:

  • arXiv (Cornell University) with 69 publications
  • ACM Transactions on Information Systems with 6 publications
  • ACM Transactions on Recommender Systems with 6 publications
  • ACM Transactions on Intelligent Systems and Technology with 4 publications
  • Proceedings of the ACM Web Conference 2022 with 3 publications

Their collaborative network involves frequent co-authors such as Shuyuan Xu, Wenyue Hua, Juntao Tan, Yingqiang Ge, and Zuohui Fu, reflecting ongoing partnerships in research projects.

Best Publications

  • Explainable Recommendation: A Survey and New Perspectives

    Yongfeng Zhang;Xu Chen

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

    Yongfeng Zhang;Guokun Lai;Min Zhang;Yi Zhang

  • Reinforcement Knowledge Graph Reasoning for Explainable Recommendation

    Yikun Xian;Zuohui Fu;S. Muthukrishnan;Gerard de Melo

  • Sequential Recommendation with User Memory Networks

    Xu Chen;Hongteng Xu;Yongfeng Zhang;Jiaxi Tang

  • Learning heterogeneous knowledge base embeddings for explainable recommendation

    Qingyao Ai;Vahid Azizi;Xu Chen;Yongfeng Zhang

  • Towards Conversational Search and Recommendation: System Ask, User Respond

    Yongfeng Zhang;Xu Chen;Qingyao Ai;Liu Yang

  • BERT with History Answer Embedding for Conversational Question Answering

    Chen Qu;Liu Yang;Minghui Qiu;W. Bruce Croft

  • Joint Representation Learning for Top-N Recommendation with Heterogeneous Information Sources

    Yongfeng Zhang;Qingyao Ai;Xu Chen;W. Bruce Croft

  • Personalized Fashion Recommendation with Visual Explanations based on Multimodal Attention Network: Towards Visually Explainable Recommendation

    Xu Chen;Hanxiong Chen;Hongteng Xu;Yongfeng Zhang

  • Understanding Echo Chambers in E-commerce Recommender Systems

    Yingqiang Ge;Shuya Zhao;Honglu Zhou;Changhua Pei

  • Counterfactual Explainable Recommendation

    Juntao Tan;Shuyuan Xu;Yingqiang Ge;Yunqi Li

  • Fairness-Aware Explainable Recommendation over Knowledge Graphs

    Zuohui Fu;Yikun Xian;Ruoyuan Gao;Jieyu Zhao

  • Detecting Stress Based on Social Interactions in Social Networks

    Huijie Lin;Jia Jia;Jiezhong Qiu;Yongfeng Zhang

  • User-oriented Fairness in Recommendation

    Yunqi Li;Hanxiong Chen;Zuohui Fu;Yingqiang Ge

  • Efficient Neural Matrix Factorization without Sampling for Recommendation

    Chong Chen;Min Zhang;Yongfeng Zhang;Yiqun Liu

  • Personalized re-ranking for recommendation

    Changhua Pei;Yi Zhang;Yongfeng Zhang;Fei Sun

  • Towards Long-term Fairness in Recommendation

    Yingqiang Ge;Shuchang Liu;Ruoyuan Gao;Yikun Xian

  • Relational Collaborative Filtering: Modeling Multiple Item Relations for Recommendation

    Xin Xin;Xiangnan He;Yongfeng Zhang;Yongdong Zhang

  • Efficient Heterogeneous Collaborative Filtering without Negative Sampling for Recommendation

    Chong Chen;Min Zhang;Yongfeng Zhang;Weizhi Ma

  • Learning to Rank Features for Recommendation over Multiple Categories

    Xu Chen;Zheng Qin;Yongfeng Zhang;Tao Xu

  • Response Ranking with Deep Matching Networks and External Knowledge in Information-seeking Conversation Systems

    Liu Yang;Minghui Qiu;Chen Qu;Jiafeng Guo

  • Dynamic Explainable Recommendation Based on Neural Attentive Models

    Xu Chen;Yongfeng Zhang;Zheng Qin

  • Counterfactual Data-Augmented Sequential Recommendation

    Zhenlei Wang;Jingsen Zhang;Hongteng Xu;Xu Chen

Frequent Co-Authors

Min Zhang
Min Zhang Tsinghua University
W. Bruce Croft
W. Bruce Croft University of Massachusetts Amherst
Shaoping Ma
Shaoping Ma Tsinghua University
Fei Sun
Fei Sun Institute Of Computing Technology
Minghui Qiu
Minghui Qiu Alibaba Group (China)
Gerard de Melo
Gerard de Melo Hasso Plattner Institute
Jiafeng Guo
Jiafeng Guo Chinese Academy of Sciences
Li Chen
Li Chen Hong Kong Baptist University
Xiangnan He
Xiangnan He University of Science and Technology of China
Hongyuan Zha
Hongyuan Zha Chinese University of Hong Kong, Shenzhen

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