World's Best Scientists 2026 revealed!
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Rising Stars
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
5944
National Ranking
788

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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