Get our free extension to see links to code for papers anywhere online!

Chrome logo Add to Chrome

Firefox logo Add to Firefox
Picture for Fuli Feng

Fuli Feng

Causal Incremental Graph Convolution for Recommender System Retraining


Aug 16, 2021
Sihao Ding, Fuli Feng, Xiangnan He, Yong Liao, Jun Shi, Yongdong Zhang

Figure 1 for Causal Incremental Graph Convolution for Recommender System Retraining
Figure 2 for Causal Incremental Graph Convolution for Recommender System Retraining
Figure 3 for Causal Incremental Graph Convolution for Recommender System Retraining
Figure 4 for Causal Incremental Graph Convolution for Recommender System Retraining
* submitted to TNNLS 
  Access Paper or Ask Questions

Empowering Language Understanding with Counterfactual Reasoning


Jun 06, 2021
Fuli Feng, Jizhi Zhang, Xiangnan He, Hanwang Zhang, Tat-Seng Chua

Figure 1 for Empowering Language Understanding with Counterfactual Reasoning
Figure 2 for Empowering Language Understanding with Counterfactual Reasoning
Figure 3 for Empowering Language Understanding with Counterfactual Reasoning
Figure 4 for Empowering Language Understanding with Counterfactual Reasoning
* Accepted by Findings of ACL'21 
  Access Paper or Ask Questions

Deconfounded Video Moment Retrieval with Causal Intervention


Jun 03, 2021
Xun Yang, Fuli Feng, Wei Ji, Meng Wang, Tat-Seng Chua

Figure 1 for Deconfounded Video Moment Retrieval with Causal Intervention
Figure 2 for Deconfounded Video Moment Retrieval with Causal Intervention
Figure 3 for Deconfounded Video Moment Retrieval with Causal Intervention
Figure 4 for Deconfounded Video Moment Retrieval with Causal Intervention
* This work has been accepted by SIGIR 2021 
  Access Paper or Ask Questions

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


Jun 01, 2021
Fengbin Zhu, Wenqiang Lei, Youcheng Huang, Chao Wang, Shuo Zhang, Jiancheng Lv, Fuli Feng, Tat-Seng Chua

Figure 1 for TAT-QA: A Question Answering Benchmark on a Hybrid of Tabular and Textual Content in Finance
Figure 2 for TAT-QA: A Question Answering Benchmark on a Hybrid of Tabular and Textual Content in Finance
Figure 3 for TAT-QA: A Question Answering Benchmark on a Hybrid of Tabular and Textual Content in Finance
Figure 4 for TAT-QA: A Question Answering Benchmark on a Hybrid of Tabular and Textual Content in Finance
* Accepted by ACL 2021 
  Access Paper or Ask Questions

Deconfounded Recommendation for Alleviating Bias Amplification


May 22, 2021
Wenjie Wang, Fuli Feng, Xiangnan He, Xiang Wang, Tat-Seng Chua

Figure 1 for Deconfounded Recommendation for Alleviating Bias Amplification
Figure 2 for Deconfounded Recommendation for Alleviating Bias Amplification
Figure 3 for Deconfounded Recommendation for Alleviating Bias Amplification
Figure 4 for Deconfounded Recommendation for Alleviating Bias Amplification
* Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discoveryand Data Mining (KDD 2021) 
  Access Paper or Ask Questions

Probabilistic and Variational Recommendation Denoising


May 20, 2021
Yu Wang, Xin Xin, Zaiqiao Meng, Xiangnan He, Joemon Jose, Fuli Feng

Figure 1 for Probabilistic and Variational Recommendation Denoising
Figure 2 for Probabilistic and Variational Recommendation Denoising
Figure 3 for Probabilistic and Variational Recommendation Denoising
Figure 4 for Probabilistic and Variational Recommendation Denoising
* 13 pages, 17 figures 
  Access Paper or Ask Questions

Causal Intervention for Leveraging Popularity Bias in Recommendation


May 13, 2021
Yang Zhang, Fuli Feng, Xiangnan He, Tianxin Wei, Chonggang Song, Guohui Ling, Yongdong Zhang

Figure 1 for Causal Intervention for Leveraging Popularity Bias in Recommendation
Figure 2 for Causal Intervention for Leveraging Popularity Bias in Recommendation
Figure 3 for Causal Intervention for Leveraging Popularity Bias in Recommendation
Figure 4 for Causal Intervention for Leveraging Popularity Bias in Recommendation
* Appear in SIGIR 2021 
  Access Paper or Ask Questions

Structure-Enhanced Meta-Learning For Few-Shot Graph Classification


Mar 11, 2021
Shunyu Jiang, Fuli Feng, Weijian Chen, Xiang Li, Xiangnan He

Figure 1 for Structure-Enhanced Meta-Learning For Few-Shot Graph Classification
Figure 2 for Structure-Enhanced Meta-Learning For Few-Shot Graph Classification
Figure 3 for Structure-Enhanced Meta-Learning For Few-Shot Graph Classification
Figure 4 for Structure-Enhanced Meta-Learning For Few-Shot Graph Classification
* Submitted to AI Open 
  Access Paper or Ask Questions

On the Equivalence of Decoupled Graph Convolution Network and Label Propagation


Oct 23, 2020
Hande Dong, Jiawei Chen, Fuli Feng, Xiangnan He, Shuxian Bi, Zhaolin Ding, Peng Cui

Figure 1 for On the Equivalence of Decoupled Graph Convolution Network and Label Propagation
Figure 2 for On the Equivalence of Decoupled Graph Convolution Network and Label Propagation
Figure 3 for On the Equivalence of Decoupled Graph Convolution Network and Label Propagation
Figure 4 for On the Equivalence of Decoupled Graph Convolution Network and Label Propagation

  Access Paper or Ask Questions

Should Graph Convolution Trust Neighbors? A Simple Causal Inference Method


Oct 22, 2020
Fuli Feng, Weiran Huang, Xin Xin, Xiangnan He, Tat-Seng Chua

Figure 1 for Should Graph Convolution Trust Neighbors? A Simple Causal Inference Method
Figure 2 for Should Graph Convolution Trust Neighbors? A Simple Causal Inference Method
Figure 3 for Should Graph Convolution Trust Neighbors? A Simple Causal Inference Method
Figure 4 for Should Graph Convolution Trust Neighbors? A Simple Causal Inference Method

  Access Paper or Ask Questions

Self-supervised Graph Learning for Recommendation


Oct 21, 2020
Jiancan Wu, Xiang Wang, Fuli Feng, Xiangnan He, Liang Chen, Jianxun Lian, Xing Xie

Figure 1 for Self-supervised Graph Learning for Recommendation
Figure 2 for Self-supervised Graph Learning for Recommendation
Figure 3 for Self-supervised Graph Learning for Recommendation
Figure 4 for Self-supervised Graph Learning for Recommendation
* 11 pages, 7 pages, 5 tables 
  Access Paper or Ask Questions

CatGCN: Graph Convolutional Networks with Categorical Node Features


Sep 17, 2020
Weijian Chen, Fuli Feng, Qifan Wang, Xiangnan He, Chonggang Song, Guohui Ling, Yongdong Zhang


  Access Paper or Ask Questions

Data Augmentation View on Graph Convolutional Network and the Proposal of Monte Carlo Graph Learning


Jun 23, 2020
Hande Dong, Zhaolin Ding, Xiangnan He, Fuli Feng, Shuxian Bi

Figure 1 for Data Augmentation View on Graph Convolutional Network and the Proposal of Monte Carlo Graph Learning
Figure 2 for Data Augmentation View on Graph Convolutional Network and the Proposal of Monte Carlo Graph Learning
Figure 3 for Data Augmentation View on Graph Convolutional Network and the Proposal of Monte Carlo Graph Learning
Figure 4 for Data Augmentation View on Graph Convolutional Network and the Proposal of Monte Carlo Graph Learning

  Access Paper or Ask Questions

Cross-GCN: Enhancing Graph Convolutional Network with $k$-Order Feature Interactions


Mar 05, 2020
Fuli Feng, Xiangnan He, Hanwang Zhang, Tat-Seng Chua

Figure 1 for Cross-GCN: Enhancing Graph Convolutional Network with $k$-Order Feature Interactions
Figure 2 for Cross-GCN: Enhancing Graph Convolutional Network with $k$-Order Feature Interactions
Figure 3 for Cross-GCN: Enhancing Graph Convolutional Network with $k$-Order Feature Interactions
Figure 4 for Cross-GCN: Enhancing Graph Convolutional Network with $k$-Order Feature Interactions
* Submitted to TKDE 
  Access Paper or Ask Questions

Bilinear Graph Neural Network with Node Interactions


Feb 20, 2020
Hongmin Zhu, Fuli Feng, Xiangnan He, Xiang Wang, Yan Li, Kai Zheng, Yongdong Zhang

Figure 1 for Bilinear Graph Neural Network with Node Interactions
Figure 2 for Bilinear Graph Neural Network with Node Interactions
Figure 3 for Bilinear Graph Neural Network with Node Interactions
Figure 4 for Bilinear Graph Neural Network with Node Interactions

  Access Paper or Ask Questions

Neural Graph Collaborative Filtering


May 20, 2019
Xiang Wang, Xiangnan He, Meng Wang, Fuli Feng, Tat-Seng Chua

Figure 1 for Neural Graph Collaborative Filtering
Figure 2 for Neural Graph Collaborative Filtering
Figure 3 for Neural Graph Collaborative Filtering
Figure 4 for Neural Graph Collaborative Filtering
* SIGIR 2019 
  Access Paper or Ask Questions

Graph Adversarial Training: Dynamically Regularizing Based on Graph Structure


Feb 20, 2019
Fuli Feng, Xiangnan He, Jie Tang, Tat-Seng Chua

Figure 1 for Graph Adversarial Training: Dynamically Regularizing Based on Graph Structure
Figure 2 for Graph Adversarial Training: Dynamically Regularizing Based on Graph Structure
Figure 3 for Graph Adversarial Training: Dynamically Regularizing Based on Graph Structure
Figure 4 for Graph Adversarial Training: Dynamically Regularizing Based on Graph Structure

  Access Paper or Ask Questions

Explicit Interaction Model towards Text Classification


Nov 23, 2018
Cunxiao Du, Zhaozheng Chin, Fuli Feng, Lei Zhu, Tian Gan, Liqiang Nie

Figure 1 for Explicit Interaction Model towards Text Classification
Figure 2 for Explicit Interaction Model towards Text Classification
Figure 3 for Explicit Interaction Model towards Text Classification
Figure 4 for Explicit Interaction Model towards Text Classification
* AAAI 2019 
 * 8 pages 
  Access Paper or Ask Questions

Improving Stock Movement Prediction with Adversarial Training


Oct 13, 2018
Fuli Feng, Huimin Chen, Xiangnan He, Ji Ding, Maosong Sun, Tat-Seng Chua

Figure 1 for Improving Stock Movement Prediction with Adversarial Training
Figure 2 for Improving Stock Movement Prediction with Adversarial Training
Figure 3 for Improving Stock Movement Prediction with Adversarial Training
Figure 4 for Improving Stock Movement Prediction with Adversarial Training

  Access Paper or Ask Questions

Discrete Factorization Machines for Fast Feature-based Recommendation


Sep 19, 2018
Han Liu, Xiangnan He, Fuli Feng, Liqiang Nie, Rui Liu, Hanwang Zhang

Figure 1 for Discrete Factorization Machines for Fast Feature-based Recommendation
Figure 2 for Discrete Factorization Machines for Fast Feature-based Recommendation
Figure 3 for Discrete Factorization Machines for Fast Feature-based Recommendation
* Appeared in IJCAI 2018 
  Access Paper or Ask Questions

Neural Compatibility Modeling with Attentive Knowledge Distillation


Apr 17, 2018
Xuemeng Song, Fuli Feng, Xianjing Han, Xin Yang, Wei Liu, Liqiang Nie

Figure 1 for Neural Compatibility Modeling with Attentive Knowledge Distillation
Figure 2 for Neural Compatibility Modeling with Attentive Knowledge Distillation
Figure 3 for Neural Compatibility Modeling with Attentive Knowledge Distillation
Figure 4 for Neural Compatibility Modeling with Attentive Knowledge Distillation

  Access Paper or Ask Questions