no code implementations • ACL 2022 • Moxin Li, Fuli Feng, Hanwang Zhang, Xiangnan He, Fengbin Zhu, Tat-Seng Chua
Neural discrete reasoning (NDR) has shown remarkable progress in combining deep models with discrete reasoning.
no code implementations • 16 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.
no code implementations • 13 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.
no code implementations • 3 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.
1 code implementation • 29 Apr 2022 • Wenjie Wang, Fuli Feng, Liqiang Nie, Tat-Seng Chua
both accuracy and diversity.
1 code implementation • 23 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.
no code implementations • 5 Feb 2022 • Xiaohe Li, Lijie Wen, Yawen Deng, Fuli Feng, Xuming Hu, Lei Wang, Zide Fan
Graph Neural Network (GNN) is an emerging technique for graph-based learning tasks such as node classification.
no code implementations • 1 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.
no code implementations • 21 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.
1 code implementation • 14 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).
1 code implementation • 2 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).
no code implementations • 29 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.
1 code implementation • 16 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.
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.
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.
1 code implementation • 3 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.
1 code implementation • 22 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.
no code implementations • 20 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.
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.
1 code implementation • 13 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.
1 code implementation • 5 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.
no code implementations • 1 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.
1 code implementation • 29 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.
1 code implementation • 23 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.
1 code implementation • 22 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.
1 code implementation • 21 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.
1 code implementation • 7 Oct 2020 • Jiawei Chen, Hande Dong, Xiang Wang, Fuli Feng, Meng Wang, Xiangnan He
This motivates us to provide a systematic survey of existing work on RS biases.
no code implementations • 21 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.
1 code implementation • 11 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.
1 code implementation • 23 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.
1 code implementation • 7 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.
1 code implementation • 27 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.
no code implementations • 5 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.
1 code implementation • 10 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.
14 code implementations • 20 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.
Ranked #6 on
Link Prediction
on MovieLens 25M
1 code implementation • 20 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.
Ranked #3 on
Node Classification
on NELL
1 code implementation • 23 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.
Ranked #4 on
Text Classification
on Yahoo! Answers
1 code implementation • 13 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.
2 code implementations • 25 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.
no code implementations • 21 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.
1 code implementation • 6 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.
no code implementations • 17 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.