World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
76
Citations
24708
World Ranking
1336
National Ranking
178

Ji-Rong Wen 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 Ji-Rong Wen 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: 382 publications — 85th percentile

85% of scientists in this discipline score the same or lower.

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

Ji-Rong Wen 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 Ji-Rong Wen 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: 76 D-Index — 91st percentile

91% of scientists in this discipline score the same or lower.

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

Research.com Recognitions

  • 2010 - ACM Senior Member

Overview

Ji-Rong Wen is affiliated with Renmin University of China and maintains an extensive research portfolio primarily within the field of Computer Science. Their scholarly work spans multiple subfields including Artificial Intelligence, Computer Vision and Pattern Recognition, Information Systems, Management Science and Operations Research, and Signal Processing.

The scientist's main research topics include Topic Modeling, Natural Language Processing Techniques, Recommender Systems and Techniques, Advanced Graph Neural Networks, Multimodal Machine Learning Applications, Domain Adaptation and Few-Shot Learning, and Information Retrieval and Search Behavior.

Ji-Rong Wen's recent papers demonstrate a focus on cutting-edge topics in language models and artificial intelligence. Notable publications include:

  • A Survey of Large Language Models, 2023, arXiv (Cornell University)
  • Pre-trained models: Past, present and future, 2021, AI Open
  • A survey on large language model based autonomous agents, 2024, Frontiers of Computer Science
  • Towards artificial general intelligence via a multimodal foundation model, 2022, Nature Communications
  • Towards Universal Sequence Representation Learning for Recommender Systems, 2022, Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining

The scientist frequently collaborates with other researchers in the field, with key co-authors including Wayne Xin Zhao, Zhicheng Dou, Jun Xu, Yupeng Hou, and Kun Zhou.

Ji-Rong Wen also publishes regularly in prominent venues where they have multiple contributions. These venues include:

  • arXiv (Cornell University)
  • ACM Transactions on Information Systems
  • IEEE Transactions on Knowledge and Data Engineering
  • Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining
  • Proceedings of the AAAI Conference on Artificial Intelligence

Their work has received recognition in the form of the ACM Senior Member award awarded in 2010.

Best Publications

  • VIPS: a Vision-based Page Segmentation Algorithm

    Deng Cai;Shipeng Yu;Ji-Rong Wen;Wei-Ying Ma

  • Pre-Trained Models: Past, Present and Future

    Xu Han;Zhengyan Zhang;Ning Ding;Yuxian Gu

  • A large-scale evaluation and analysis of personalized search strategies

    Zhicheng Dou;Ruihua Song;Ji-Rong Wen

  • S^3-Rec: Self-Supervised Learning for Sequential Recommendation with Mutual Information Maximization

    Kun Zhou;Hui Wang;Wayne Xin Zhao;Yutao Zhu

  • Probabilistic query expansion using query logs

    Hang Cui;Ji-Rong Wen;Jian-Yun Nie;Wei-Ying Ma

  • Clustering user queries of a search engine

    Ji-Rong Wen;Jian-Yun Nie;Hong-Jiang Zhang

  • S3-Rec: Self-Supervised Learning for Sequential Recommendation with Mutual Information Maximization

    Kun Zhou;Hui Wang;Wayne Xin Zhao;Yutao Zhu

  • Extracting content structure for web pages based on visual representation

    Deng Cai;Shipeng Yu;Ji-Rong Wen;Wei-Ying Ma

  • Query Clustering Using User Logs

    Ji-Rong Wen;Jian-Yun Nie;HongJiang Zhang

  • Hierarchical clustering of WWW image search results using visual, textual and link information

    Deng Cai;Xiaofei He;Zhiwei Li;Wei-Ying Ma

  • Improving Sequential Recommendation with Knowledge-Enhanced Memory Networks

    Jin Huang;Wayne Xin Zhao;Hongjian Dou;Ji-Rong Wen

  • Query expansion by mining user logs

    Hang Cui;Ji-Rong Wen;Jian-Yun Nie;Wei-Ying Ma

  • Improving pseudo-relevance feedback in web information retrieval using web page segmentation

    Shipeng Yu;Deng Cai;Ji-Rong Wen;Wei-Ying Ma

  • Learning block importance models for web pages

    Ruihua Song;Haifeng Liu;Ji-Rong Wen;Wei-Ying Ma

  • Object-level ranking: bringing order to Web objects

    Zaiqing Nie;Yuanzhi Zhang;Ji-Rong Wen;Wei-Ying Ma

  • StatSnowball: a statistical approach to extracting entity relationships

    Jun Zhu;Zaiqing Nie;Xiaojiang Liu;Bo Zhang

  • Counterfactual VQA: A Cause-Effect Look at Language Bias

    Yulei Niu;Kaihua Tang;Hanwang Zhang;Zhiwu Lu

  • RecBole: Towards a Unified, Comprehensive and Efficient Framework for Recommendation Algorithms

    Wayne Xin Zhao;Shanlei Mu;Yupeng Hou;Zihan Lin

  • Improving Conversational Recommender Systems via Knowledge Graph based Semantic Fusion

    Kun Zhou;Wayne Xin Zhao;Shuqing Bian;Yuanhang Zhou

  • Semi-Supervised Learning

    Xueyuan Zhou;Mikhail Belkin

  • Block-based web search

    Deng Cai;Shipeng Yu;Ji-Rong Wen;Wei-Ying Ma

  • Hierarchical Clustering of WWW Image Search Results Using Visual

    Deng Cai;Xiaofei He;Wei-Ying Ma;Ji-Rong Wen

  • RecBole: Towards a Unified, Comprehensive and Efficient Framework for Recommendation Algorithms

    Wayne Xin Zhao;Shanlei Mu;Yupeng Hou;Zihan Lin

Frequent Co-Authors

Wei-Ying Ma
Wei-Ying Ma Tsinghua University
Shuming Shi
Shuming Shi Tencent (China)
Zhicheng Dou
Zhicheng Dou Renmin University of China
Deng Cai
Deng Cai Zhejiang University
Jian-Yun Nie
Jian-Yun Nie University of Montreal
Xiaofei He
Xiaofei He Zhejiang University
Wayne Xin Zhao
Wayne Xin Zhao Renmin University of China
Shipeng Yu
Shipeng Yu Pinterest
Yi-Min Wang
Yi-Min Wang Microsoft (United States)

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