Search Results for author: Fuli Feng

Found 42 papers, 28 papers with code

Mitigating Hidden Confounding Effects for Causal Recommendation

no code implementations16 May 2022 Xinyuan Zhu, Yang Zhang, Fuli Feng, Xun Yang, Dingxian Wang, Xiangnan He

Towards this goal, we propose a Hidden Confounder Removal (HCR) framework that leverages front-door adjustment to decompose the causal effect into two partial effects, according to the mediators between item features and user feedback.

Multi-Task Learning Recommendation Systems

Addressing Confounding Feature Issue for Causal Recommendation

no code implementations13 May 2022 Xiangnan He, Yang Zhang, Fuli Feng, Chonggang Song, Lingling Yi, Guohui Ling, Yongdong Zhang

We demonstrate DCR on the backbone model of neural factorization machine (NFM), showing that DCR leads to more accurate prediction of user preference with small inference time cost.

Recommendation Systems

Copy Motion From One to Another: Fake Motion Video Generation

no code implementations3 May 2022 Zhenguang Liu, Sifan Wu, Chejian Xu, Xiang Wang, Lei Zhu, Shuang Wu, Fuli Feng

Furthermore, current methods typically employ GANs with a L2 loss to assess the authenticity of the generated videos, inherently requiring a large amount of training samples to learn the texture details for adequate video generation.

Video Generation

Reinforced Causal Explainer for Graph Neural Networks

1 code implementation23 Apr 2022 Xiang Wang, Yingxin Wu, An Zhang, Fuli Feng, Xiangnan He, Tat-Seng Chua

Such reward accounts for the dependency of the newly-added edge and the previously-added edges, thus reflecting whether they collaborate together and form a coalition to pursue better explanations.

Graph Classification

WebFormer: The Web-page Transformer for Structure Information Extraction

no code implementations1 Feb 2022 Qifan Wang, Yi Fang, Anirudh Ravula, Fuli Feng, Xiaojun Quan, Dongfang Liu

Structure information extraction refers to the task of extracting structured text fields from web pages, such as extracting a product offer from a shopping page including product title, description, brand and price.

Deep Attention Graph Attention

Deconfounding to Explanation Evaluation in Graph Neural Networks

no code implementations21 Jan 2022 Ying-Xin Wu, Xiang Wang, An Zhang, Xia Hu, Fuli Feng, Xiangnan He, Tat-Seng Chua

In this work, we propose Deconfounded Subgraph Evaluation (DSE) which assesses the causal effect of an explanatory subgraph on the model prediction.

Training Free Graph Neural Networks for Graph Matching

1 code implementation14 Jan 2022 Zhiyuan Liu, Yixin Cao, Fuli Feng, Xiang Wang, Jie Tang, Kenji Kawaguchi, Tat-Seng Chua

We present a framework of Training Free Graph Matching (TFGM) to boost the performance of Graph Neural Networks (GNNs) based graph matching, providing a fast promising solution without training (training-free).

Entity Alignment Graph Matching +1

Learning Robust Recommender from Noisy Implicit Feedback

1 code implementation2 Dec 2021 Wenjie Wang, Fuli Feng, Xiangnan He, Liqiang Nie, Tat-Seng Chua

Inspired by this observation, we propose a new training strategy named Adaptive Denoising Training (ADT), which adaptively prunes the noisy interactions by two paradigms (i. e., Truncated Loss and Reweighted Loss).

Denoising Recommendation Systems

Decoupling Strategy and Surface Realization for Task-oriented Dialogues

no code implementations29 Sep 2021 Chenchen Ye, Lizi Liao, Fuli Feng, Wei Ji, Tat-Seng Chua

The core is to construct a latent content space for strategy optimization and disentangle the surface style from it.

Style Transfer Task-Oriented Dialogue Systems

Causal Incremental Graph Convolution for Recommender System Retraining

1 code implementation16 Aug 2021 Sihao Ding, Fuli Feng, Xiangnan He, Yong Liao, Jun Shi, Yongdong Zhang

Towards the goal, we propose a \textit{Causal Incremental Graph Convolution} approach, which consists of two new operators named \textit{Incremental Graph Convolution} (IGC) and \textit{Colliding Effect Distillation} (CED) to estimate the output of full graph convolution.

Causal Inference Recommendation Systems

Counterfactual Inference for Text Classification Debiasing

1 code implementation ACL 2021 Chen Qian, Fuli Feng, Lijie Wen, Chunping Ma, Pengjun Xie

In inference, given a factual input document, Corsair imagines its two counterfactual counterparts to distill and mitigate the two biases captured by the poisonous model.

Classification Counterfactual Inference +2

Empowering Language Understanding with Counterfactual Reasoning

1 code implementation Findings (ACL) 2021 Fuli Feng, Jizhi Zhang, Xiangnan He, Hanwang Zhang, Tat-Seng Chua

Present language understanding methods have demonstrated extraordinary ability of recognizing patterns in texts via machine learning.

Natural Language Inference Sentiment Analysis

Deconfounded Video Moment Retrieval with Causal Intervention

1 code implementation3 Jun 2021 Xun Yang, Fuli Feng, Wei Ji, Meng Wang, Tat-Seng Chua

To fill the research gap, we propose a causality-inspired VMR framework that builds structural causal model to capture the true effect of query and video content on the prediction.

Moment Retrieval

Deconfounded Recommendation for Alleviating Bias Amplification

1 code implementation22 May 2021 Wenjie Wang, Fuli Feng, Xiangnan He, Xiang Wang, Tat-Seng Chua

In this work, we scrutinize the cause-effect factors for bias amplification, identifying the main reason lies in the confounder effect of imbalanced item distribution on user representation and prediction score.

Fairness Recommendation Systems

Learning Robust Recommenders through Cross-Model Agreement

no code implementations20 May 2021 Yu Wang, Xin Xin, Zaiqiao Meng, Xiangnan He, Joemon Jose, Fuli Feng

A noisy negative example which is uninteracted because of unawareness of the user could also denote potential positive user preference.

Denoising Recommendation Systems

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

1 code implementation ACL 2021 Fengbin Zhu, Wenqiang Lei, Youcheng Huang, Chao Wang, Shuo Zhang, Jiancheng Lv, Fuli Feng, Tat-Seng Chua

In this work, we extract samples from real financial reports to build a new large-scale QA dataset containing both Tabular And Textual data, named TAT-QA, where numerical reasoning is usually required to infer the answer, such as addition, subtraction, multiplication, division, counting, comparison/sorting, and the compositions.

Question Answering

Causal Intervention for Leveraging Popularity Bias in Recommendation

1 code implementation13 May 2021 Yang Zhang, Fuli Feng, Xiangnan He, Tianxin Wei, Chonggang Song, Guohui Ling, Yongdong Zhang

This work studies an unexplored problem in recommendation -- how to leverage popularity bias to improve the recommendation accuracy.

Collaborative Filtering Recommendation Systems

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

1 code implementation5 Mar 2021 Shunyu Jiang, Fuli Feng, Weijian Chen, Xiang Li, Xiangnan He

Graph classification is a highly impactful task that plays a crucial role in a myriad of real-world applications such as molecular property prediction and protein function prediction. Aiming to handle the new classes with limited labeled graphs, few-shot graph classification has become a bridge of existing graph classification solutions and practical usage. This work explores the potential of metric-based meta-learning for solving few-shot graph classification. We highlight the importance of considering structural characteristics in the solution and propose a novel framework which explicitly considers global structure and local structure of the input graph.

Classification General Classification +4

DyHCN: Dynamic Hypergraph Convolutional Networks

no code implementations1 Jan 2021 Nan Yin, Zhigang Luo, Wenjie Wang, Fuli Feng, Xiang Zhang

In general, DyHCN consists of a Hypergraph Convolution (HC) to encode the hypergraph structure at a time point and a Temporal Evolution module (TE) to capture the varying of the relations.

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

1 code implementation29 Oct 2020 Tianxin Wei, Fuli Feng, Jiawei Chen, Ziwei Wu, JinFeng Yi, Xiangnan He

Existing work addresses this issue with Inverse Propensity Weighting (IPW), which decreases the impact of popular items on the training and increases the impact of long-tail items.

Counterfactual Inference Multi-Task Learning +1

On the Equivalence of Decoupled Graph Convolution Network and Label Propagation

1 code implementation23 Oct 2020 Hande Dong, Jiawei Chen, Fuli Feng, Xiangnan He, Shuxian Bi, Zhaolin Ding, Peng Cui

The original design of Graph Convolution Network (GCN) couples feature transformation and neighborhood aggregation for node representation learning.

Node Classification Representation Learning

Should Graph Convolution Trust Neighbors? A Simple Causal Inference Method

1 code implementation22 Oct 2020 Fuli Feng, Weiran Huang, Xiangnan He, Xin Xin, Qifan Wang, Tat-Seng Chua

To this end, we analyze the working mechanism of GCN with causal graph, estimating the causal effect of a node's local structure for the prediction.

Causal Inference Graph Attention +2

Self-supervised Graph Learning for Recommendation

1 code implementation21 Oct 2020 Jiancan Wu, Xiang Wang, Fuli Feng, Xiangnan He, Liang Chen, Jianxun Lian, Xing Xie

In this work, we explore self-supervised learning on user-item graph, so as to improve the accuracy and robustness of GCNs for recommendation.

Graph Learning Representation Learning +1

Clicks can be Cheating: Counterfactual Recommendation for Mitigating Clickbait Issue

no code implementations21 Sep 2020 Wenjie Wang, Fuli Feng, Xiangnan He, Hanwang Zhang, Tat-Seng Chua

However, we argue that there is a significant gap between clicks and user satisfaction -- it is common that a user is "cheated" to click an item by the attractive title/cover of the item.

Click-Through Rate Prediction Counterfactual Inference

CatGCN: Graph Convolutional Networks with Categorical Node Features

1 code implementation11 Sep 2020 Weijian Chen, Fuli Feng, Qifan Wang, Xiangnan He, Chonggang Song, Guohui Ling, Yongdong Zhang

In this paper, we propose a new GCN model named CatGCN, which is tailored for graph learning when the node features are categorical.

Graph Learning Node Classification +1

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

1 code implementation23 Jun 2020 Hande Dong, Zhaolin Ding, Xiangnan He, Fuli Feng, Shuxian Bi

In this work, we introduce a new understanding for it -- data augmentation, which is more transparent than the previous understandings.

Data Augmentation Graph Learning

Denoising Implicit Feedback for Recommendation

1 code implementation7 Jun 2020 Wenjie Wang, Fuli Feng, Xiangnan He, Liqiang Nie, Tat-Seng Chua

In this work, we explore the central theme of denoising implicit feedback for recommender training.

Denoising Recommendation Systems

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

1 code implementation27 May 2020 Yang Zhang, Fuli Feng, Chenxu Wang, Xiangnan He, Meng Wang, Yan Li, Yongdong Zhang

Nevertheless, normal training on new data only may easily cause overfitting and forgetting issues, since the new data is of a smaller scale and contains fewer information on long-term user preference.

Meta-Learning Recommendation Systems

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

no code implementations5 Mar 2020 Fuli Feng, Xiangnan He, Hanwang Zhang, Tat-Seng Chua

Graph Convolutional Network (GCN) is an emerging technique that performs learning and reasoning on graph data.

Document Classification

Bilinear Graph Neural Network with Neighbor Interactions

1 code implementation10 Feb 2020 Hongmin Zhu, Fuli Feng, Xiangnan He, Xiang Wang, Yan Li, Kai Zheng, Yongdong Zhang

We term this framework as Bilinear Graph Neural Network (BGNN), which improves GNN representation ability with bilinear interactions between neighbor nodes.

General Classification Node Classification

Neural Graph Collaborative Filtering

14 code implementations20 May 2019 Xiang Wang, Xiangnan He, Meng Wang, Fuli Feng, Tat-Seng Chua

Further analysis verifies the importance of embedding propagation for learning better user and item representations, justifying the rationality and effectiveness of NGCF.

Collaborative Filtering Link Prediction +1

Graph Adversarial Training: Dynamically Regularizing Based on Graph Structure

1 code implementation20 Feb 2019 Fuli Feng, Xiangnan He, Jie Tang, Tat-Seng Chua

Adversarial Training (AT), a dynamic regularization technique, can resist the worst-case perturbations on input features and is a promising choice to improve model robustness and generalization.

General Classification Node Classification

Explicit Interaction Model towards Text Classification

1 code implementation23 Nov 2018 Cunxiao Du, Zhaozheng Chin, Fuli Feng, Lei Zhu, Tian Gan, Liqiang Nie

To address this problem, we introduce the interaction mechanism to incorporate word-level matching signals into the text classification task.

Classification General Classification +3

Enhancing Stock Movement Prediction with Adversarial Training

1 code implementation13 Oct 2018 Fuli Feng, Huimin Chen, Xiangnan He, Ji Ding, Maosong Sun, Tat-Seng Chua

The key novelty is that we propose to employ adversarial training to improve the generalization of a neural network prediction model.

Stock Prediction

Temporal Relational Ranking for Stock Prediction

2 code implementations25 Sep 2018 Fuli Feng, Xiangnan He, Xiang Wang, Cheng Luo, Yiqun Liu, Tat-Seng Chua

Our RSR method advances existing solutions in two major aspects: 1) tailoring the deep learning models for stock ranking, and 2) capturing the stock relations in a time-sensitive manner.

Stock Prediction Time Series

Learning to Recommend with Multiple Cascading Behaviors

no code implementations21 Sep 2018 Chen Gao, Xiangnan He, Dahua Gan, Xiangning Chen, Fuli Feng, Yong Li, Tat-Seng Chua, Lina Yao, Yang song, Depeng Jin

To fully exploit the signal in the data of multiple types of behaviors, we perform a joint optimization based on the multi-task learning framework, where the optimization on a behavior is treated as a task.

Multi-Task Learning Recommendation Systems

Discrete Factorization Machines for Fast Feature-based Recommendation

1 code implementation6 May 2018 Han Liu, Xiangnan He, Fuli Feng, Liqiang Nie, Rui Liu, Hanwang Zhang

In this paper, we develop a generic feature-based recommendation model, called Discrete Factorization Machine (DFM), for fast and accurate recommendation.

Binarization Quantization

Neural Compatibility Modeling with Attentive Knowledge Distillation

no code implementations17 Apr 2018 Xuemeng Song, Fuli Feng, Xianjing Han, Xin Yang, Wei Liu, Liqiang Nie

Nevertheless, existing studies overlook the rich valuable knowledge (rules) accumulated in fashion domain, especially the rules regarding clothing matching.

Knowledge Distillation Speech Recognition

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