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2025

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Rising Stars

D-Index
48
Citations
10888
World Ranking
358
National Ranking
119

Computer Science

D-Index
49
Citations
8511
World Ranking
5945
National Ranking
788

Fuli Feng 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 Fuli Feng 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: 260 publications — 65th percentile

65% of scientists in this discipline score the same or lower.

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

Fuli Feng 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 Fuli Feng 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: 49 D-Index — 60th percentile

60% 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

  • 2025 - Research.com Rising Stars Award

Overview

Fuli Feng is affiliated with the University of Science and Technology of China. Their research primarily focuses on computer science with significant contributions in artificial intelligence, information systems, and computer vision and pattern recognition. Additional subfields include management science and operations research as well as molecular biology.

The scientist's work spans several key topics, notably recommender systems and techniques, topic modeling, advanced graph neural networks, and natural language processing techniques. Other research areas include advanced bandit algorithms research, machine learning in healthcare, and multimodal machine learning applications.

Fuli Feng has published extensively, with recent papers including:

  • Bias and Debias in Recommender System: A Survey and Future Directions, 2022, ACM Transactions on Information Systems
  • Bias and Debias in Recommender System: A Survey and Future Directions, 2020, arXiv (Cornell University)
  • Causal Representation Learning for Out-of-Distribution Recommendation, 2022, Proceedings of the ACM Web Conference 2022
  • Causal Inference in Recommender Systems: A Survey and Future Directions, 2024, ACM Transactions on Information Systems
  • Information Retrieval meets Large Language Models: A strategic report from Chinese IR community, 2023, AI Open

The scientist frequently collaborates with a network of coauthors including Xiangnan He, Tat-Seng Chua, Wenjie Wang, Yang Zhang, and Jizhi Zhang. These collaborations have led to numerous publications and contributions across various related venues.

Fuli Feng's work appears regularly in key academic venues with multiple publications in:

  • arXiv (Cornell University)
  • ACM Transactions on Information Systems
  • IEEE Transactions on Knowledge and Data Engineering
  • Proceedings of the AAAI Conference on Artificial Intelligence
  • Proceedings of the ACM Web Conference 2022

Best Publications

  • Neural Graph Collaborative Filtering

    Xiang Wang;Xiangnan He;Meng Wang;Fuli Feng

  • Self-supervised Graph Learning for Recommendation

    Jiancan Wu;Xiang Wang;Fuli Feng;Xiangnan He

  • Temporal Relational Ranking for Stock Prediction

    Fuli Feng;Xiangnan He;Xiang Wang;Cheng Luo

  • Bias and Debias in Recommender System: A Survey and Future Directions

    Jiawei Chen;Hande Dong;Xiang Wang;Fuli Feng

  • Model-Agnostic Counterfactual Reasoning for Eliminating Popularity Bias in Recommender System

    Tianxin Wei;Fuli Feng;Jiawei Chen;Ziwei Wu

  • Causal Intervention for Leveraging Popularity Bias in Recommendation

    Yang Zhang;Fuli Feng;Xiangnan He;Tianxin Wei

  • Depression detection via harvesting social media: a multimodal dictionary learning solution

    Guangyao Shen;Jiang Jia;Liqiang Nie;Fuli Feng

  • TALLRec: An Effective and Efficient Tuning Framework to Align Large Language Model with Recommendation

    Unknown

  • Enhancing Stock Movement Prediction with Adversarial Training

    Fuli Feng;Huimin Chen;Xiangnan He;Ji Ding

  • Denoising Implicit Feedback for Recommendation

    Wenjie Wang;Fuli Feng;Xiangnan He;Liqiang Nie

  • TEM: Tree-enhanced Embedding Model for Explainable Recommendation

    Xiang Wang;Xiangnan He;Fuli Feng;Liqiang Nie

  • Graph Adversarial Training: Dynamically Regularizing Based on Graph Structure

    Fuli Feng;Xiangnan He;Jie Tang;Tat-Seng Chua

  • Diffusion Recommender Model

    Unknown

  • NeuroStylist: Neural Compatibility Modeling for Clothing Matching

    Xuemeng Song;Fuli Feng;Jinhuan Liu;Zekun Li

  • Deconfounded Video Moment Retrieval with Causal Intervention

    Xun Yang;Fuli Feng;Wei Ji;Meng Wang

  • Neural Multi-task Recommendation from Multi-behavior Data

    Chen Gao;Xiangnan He;Dahua Gan;Xiangning Chen

  • Neural Compatibility Modeling with Attentive Knowledge Distillation

    Xuemeng Song;Fuli Feng;Xianjing Han;Xin Yang

  • Is ChatGPT Fair for Recommendation? Evaluating Fairness in Large Language Model Recommendation

    Unknown

  • TAT-QA: A Question Answering Benchmark on a Hybrid of Tabular and Textual Content in Finance

    Fengbin Zhu;Wenqiang Lei;Youcheng Huang;Chao Wang

  • Deconfounded Recommendation for Alleviating Bias Amplification

    Wenjie Wang;Fuli Feng;Xiangnan He;Xiang Wang

  • Deep Understanding of Cooking Procedure for Cross-modal Recipe Retrieval

    Jing-Jing Chen;Chong-Wah Ngo;Fu-Li Feng;Tat-Seng Chua

  • Hierarchical Attention Network for Visually-Aware Food Recommendation

    Xiaoyan Gao;Fuli Feng;Xiangnan He;Heyan Huang

  • Bilinear Graph Neural Network with Neighbor Interactions

    Hongmin Zhu;Fuli Feng;Xiangnan He;Xiang Wang

  • Explicit Interaction Model towards Text Classification

    Cunxiao Du;Zhaozheng Chen;Fuli Feng;Lei Zhu

  • Cross-domain Recommendation Without Sharing User-relevant Data

    Chen Gao;Xiangning Chen;Fuli Feng;Kai Zhao

  • Data-efficient Fine-tuning for LLM-based Recommendation

    Unknown

  • How to Retrain Recommender System?: A Sequential Meta-Learning Method

    Yang Zhang;Fuli Feng;Chenxu Wang;Xiangnan He

  • Clicks can be Cheating: Counterfactual Recommendation for Mitigating Clickbait Issue

    Wenjie Wang;Fuli Feng;Xiangnan He;Hanwang Zhang

Frequent Co-Authors

Xiangnan He
Xiangnan He University of Science and Technology of China
Tat-Seng Chua
Tat-Seng Chua National University of Singapore
Liqiang Nie
Liqiang Nie Shandong University
Yongdong Zhang
Yongdong Zhang University of Science and Technology of China
Yong Li
Yong Li Tsinghua University
Hanwang Zhang
Hanwang Zhang Nanyang Technological University
Meng Wang
Meng Wang Hefei University of Technology
Maosong Sun
Maosong Sun Tsinghua University
Jie Tang
Jie Tang Tsinghua University
Depeng Jin
Depeng Jin Tsinghua University

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