3.2k citations · 18.4k across the 142 of their papers we have counts for
23 papers · 1 filter
Learning Robust Recommender from Noisy Implicit Feedback
Wenjie Wang, Fuli Feng, Xiangnan He +2
The ubiquity of implicit feedback makes it indispensable for building recommender systems. However, it does not actually reflect the actual satisfaction of users. For example, in E…
Causal Attention for Interpretable and Generalizable Graph Classification
Yongduo Sui, Xiang Wang, Jiancan Wu +3
In graph classification, attention and pooling-based graph neural networks (GNNs) prevail to extract the critical features from the input graph and support the prediction. They mos…
GRCN: Graph-Refined Convolutional Network for Multimedia Recommendation with Implicit Feedback
Wei Yinwei, Wang Xiang, Nie Liqiang +2
Reorganizing implicit feedback of users as a user-item interaction graph facilitates the applications of graph convolutional networks (GCNs) in recommendation tasks. In the interac…
Hierarchical User Intent Graph Network forMultimedia Recommendation
Wei Yinwei, Wang Xiang, He Xiangnan +3
In this work, we aim to learn multi-level user intents from the co-interacted patterns of items, so as to obtain high-quality representations of users and items and further enhance…
Popularity Bias Is Not Always Evil: Disentangling Benign and Harmful Bias for Recommendation
Zihao Zhao, Jiawei Chen, Sheng Zhou +4
Recommender system usually suffers from severe popularity bias -- the collected interaction data usually exhibits quite imbalanced or even long-tailed distribution over items. Such…
A Survey of Graph Neural Networks for Recommender Systems: Challenges, Methods, and Directions
Chen Gao, Yu Zheng, Nian Li +8
Recommender system is one of the most important information services on today's Internet. Recently, graph neural networks have become the new state-of-the-art approach to recommend…
DisenKGAT: Knowledge Graph Embedding with Disentangled Graph Attention Network
Junkang Wu, Wentao Shi, Xuezhi Cao +5
Knowledge graph completion (KGC) has become a focus of attention across deep learning community owing to its excellent contribution to numerous downstream tasks. Although recently…
Causal Incremental Graph Convolution for Recommender System Retraining
Sihao Ding, Fuli Feng, Xiangnan He +3
Real-world recommender system needs to be regularly retrained to keep with the new data. In this work, we consider how to efficiently retrain graph convolution network (GCN) based…
Time-aware Path Reasoning on Knowledge Graph for Recommendation
Yuyue Zhao, Xiang Wang, Jiawei Chen +4
Reasoning on knowledge graph (KG) has been studied for explainable recommendation due to it's ability of providing explicit explanations. However, current KG-based explainable reco…
Exploring Lottery Ticket Hypothesis in Media Recommender Systems
Yanfang Wang, Yongduo Sui, Xiang Wang +2
Media recommender systems aim to capture users' preferences and provide precise personalized recommendation of media content. There are two critical components in the common paradi…
User-specific Adaptive Fine-tuning for Cross-domain Recommendations
Lei Chen, Fajie Yuan, Jiaxi Yang +3
Making accurate recommendations for cold-start users has been a longstanding and critical challenge for recommender systems (RS). Cross-domain recommendations (CDR) offer a solutio…
DebiasGAN: Eliminating Position Bias in News Recommendation with Adversarial Learning
Chuhan Wu, Fangzhao Wu, Yongfeng Huang
News recommendation is important for improving news reading experience of users. Users' news click behaviors are widely used for inferring user interests and predicting future clic…
Empowering Language Understanding with Counterfactual Reasoning
Fuli Feng, Jizhi Zhang, Xiangnan He +2
Present language understanding methods have demonstrated extraordinary ability of recognizing patterns in texts via machine learning. However, existing methods indiscriminately use…
Deconfounded Recommendation for Alleviating Bias Amplification
Wenjie Wang, Fuli Feng, Xiangnan He +2
Recommender systems usually amplify the biases in the data. The model learned from historical interactions with imbalanced item distribution will amplify the imbalance by over-reco…
Graph Learning based Recommender Systems: A Review
Shoujin Wang, Liang Hu, Yan Wang +6
Recent years have witnessed the fast development of the emerging topic of Graph Learning based Recommender Systems (GLRS). GLRS employ advanced graph learning approaches to model u…
Causal Intervention for Leveraging Popularity Bias in Recommendation
Yang Zhang, Fuli Feng, Xiangnan He +4
Recommender system usually faces popularity bias issues: from the data perspective, items exhibit uneven (long-tail) distribution on the interaction frequency; from the method pers…
Learning Robust Recommenders through Cross-Model Agreement
Yu Wang, Xin Xin, Zaiqiao Meng +3
Learning from implicit feedback is one of the most common cases in the application of recommender systems. Generally speaking, interacted examples are considered as positive while…
AutoDebias: Learning to Debias for Recommendation
Jiawei Chen, Hande Dong, Yang Qiu +5
Recommender systems rely on user behavior data like ratings and clicks to build personalization model. However, the collected data is observational rather than experimental, causin…
A-FMI: Learning Attributions from Deep Networks via Feature Map Importance
An Zhang, Xiang Wang, Chengfang Fang +3
Gradient-based attribution methods can aid in the understanding of convolutional neural networks (CNNs). However, the redundancy of attribution features and the gradient saturation…
A Survey on Accuracy-oriented Neural Recommendation: From Collaborative Filtering to Information-rich Recommendation
Le Wu, Xiangnan He, Xiang Wang +2
Influenced by the great success of deep learning in computer vision and language understanding, research in recommendation has shifted to inventing new recommender models based on…
Structure-Enhanced Meta-Learning For Few-Shot Graph Classification
Shunyu Jiang, Fuli Feng, Weijian Chen +2
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 predicti…
Learning Intents behind Interactions with Knowledge Graph for Recommendation
Xiang Wang, Tinglin Huang, Dingxian Wang +4
Knowledge graph (KG) plays an increasingly important role in recommender systems. A recent technical trend is to develop end-to-end models founded on graph neural networks (GNNs).…
Advances and Challenges in Conversational Recommender Systems: A Survey
Chongming Gao, Wenqiang Lei, Xiangnan He +2
Recommender systems exploit interaction history to estimate user preference, having been heavily used in a wide range of industry applications. However, static recommendation model…