3.2k citations · 18.4k across the 142 of their papers we have counts for
33 papers · 1 filter
On Disambiguating Authors: Collaboration Network Reconstruction in a Bottom-up Manner
Na Li, Renyu Zhu, Xiaoxu Zhou +4
Author disambiguation arises when different authors share the same name, which is a critical task in digital libraries, such as DBLP, CiteULike, CiteSeerX, etc. While the state-of-…
CoSam: An Efficient Collaborative Adaptive Sampler for Recommendation
Jiawei Chen, Chengquan Jiang, Can Wang +5
Sampling strategies have been widely applied in many recommendation systems to accelerate model learning from implicit feedback data. A typical strategy is to draw negative instanc…
Model-Agnostic Counterfactual Reasoning for Eliminating Popularity Bias in Recommender System
Tianxin Wei, Fuli Feng, Jiawei Chen +3
The general aim of the recommender system is to provide personalized suggestions to users, which is opposed to suggesting popular items. However, the normal training paradigm, i.e.…
On the Equivalence of Decoupled Graph Convolution Network and Label Propagation
Hande Dong, Jiawei Chen, Fuli Feng +4
The original design of Graph Convolution Network (GCN) couples feature transformation and neighborhood aggregation for node representation learning. Recently, some work shows that…
Should Graph Convolution Trust Neighbors? A Simple Causal Inference Method
Fuli Feng, Weiran Huang, Xiangnan He +3
Graph Convolutional Network (GCN) is an emerging technique for information retrieval (IR) applications. While GCN assumes the homophily property of a graph, real-world graphs are n…
Self-supervised Graph Learning for Recommendation
Jiancan Wu, Xiang Wang, Fuli Feng +4
Representation learning on user-item graph for recommendation has evolved from using single ID or interaction history to exploiting higher-order neighbors. This leads to the succes…
Bias and Debias in Recommender System: A Survey and Future Directions
Jiawei Chen, Hande Dong, Xiang Wang +3
While recent years have witnessed a rapid growth of research papers on recommender system (RS), most of the papers focus on inventing machine learning models to better fit user beh…
Clicks can be Cheating: Counterfactual Recommendation for Mitigating Clickbait Issue
Wenjie Wang, Fuli Feng, Xiangnan He +2
Recommendation is a prevalent and critical service in information systems. To provide personalized suggestions to users, industry players embrace machine learning, more specificall…
CatGCN: Graph Convolutional Networks with Categorical Node Features
Weijian Chen, Fuli Feng, Qifan Wang +4
Recent studies on Graph Convolutional Networks (GCNs) reveal that the initial node representations (i.e., the node representations before the first-time graph convolution) largely…
Adversarial Attack on Large Scale Graph
Jintang Li, Tao Xie, Liang Chen +3
Recent studies have shown that graph neural networks (GNNs) are vulnerable against perturbations due to lack of robustness and can therefore be easily fooled. Currently, most works…
A Survey on Large-scale Machine Learning
Meng Wang, Weijie Fu, Xiangnan He +2
Machine learning can provide deep insights into data, allowing machines to make high-quality predictions and having been widely used in real-world applications, such as text mining…
Disentangled Graph Collaborative Filtering
Xiang Wang, Hongye Jin, An Zhang +3
Learning informative representations of users and items from the interaction data is of crucial importance to collaborative filtering (CF). Present embedding functions exploit user…
Interactive Path Reasoning on Graph for Conversational Recommendation
Wenqiang Lei, Gangyi Zhang, Xiangnan He +4
Traditional recommendation systems estimate user preference on items from past interaction history, thus suffering from the limitations of obtaining fine-grained and dynamic user p…
Data Augmentation View on Graph Convolutional Network and the Proposal of Monte Carlo Graph Learning
Hande Dong, Zhaolin Ding, Xiangnan He +2
Today, there are two major understandings for graph convolutional networks, i.e., in the spectral and spatial domain. But both lack transparency. In this work, we introduce a new u…
Disentangling User Interest and Conformity for Recommendation with Causal Embedding
Yu Zheng, Chen Gao, Xiang Li +3
Recommendation models are usually trained on observational interaction data. However, observational interaction data could result from users' conformity towards popular items, whic…
Denoising Implicit Feedback for Recommendation
Wenjie Wang, Fuli Feng, Xiangnan He +2
The ubiquity of implicit feedback makes them the default choice to build online recommender systems. While the large volume of implicit feedback alleviates the data sparsity issue,…
Modeling Personalized Item Frequency Information for Next-basket Recommendation
Haoji Hu, Xiangnan He, Jinyang Gao +1
Next-basket recommendation (NBR) is prevalent in e-commerce and retail industry. In this scenario, a user purchases a set of items (a basket) at a time. NBR performs sequential mod…
How to Retrain Recommender System? A Sequential Meta-Learning Method
Yang Zhang, Fuli Feng, Chenxu Wang +4
Practical recommender systems need be periodically retrained to refresh the model with new interaction data. To pursue high model fidelity, it is usually desirable to retrain the m…
Hierarchical Fashion Graph Network for Personalized Outfit Recommendation
Xingchen Li, Xiang Wang, Xiangnan He +3
Fashion outfit recommendation has attracted increasing attentions from online shopping services and fashion communities.Distinct from other scenarios (e.g., social networking or co…
Bundle Recommendation with Graph Convolutional Networks
Jianxin Chang, Chen Gao, Xiangnan He +2
Bundle recommendation aims to recommend a bundle of items for a user to consume as a whole. Existing solutions integrate user-item interaction modeling into bundle recommendation b…
Seamlessly Unifying Attributes and Items: Conversational Recommendation for Cold-Start Users
Shijun Li, Wenqiang Lei, Qingyun Wu +3
Static recommendation methods like collaborative filtering suffer from the inherent limitation of performing real-time personalization for cold-start users. Online recommendation,…
Graph Learning Approaches to Recommender Systems: A Review
Shoujin Wang, Liang Hu, Yan Wang +7
Recent years have witnessed the fast development of the emerging topic of Graph Learning based Recommender Systems (GLRS). GLRS mainly employ the advanced graph learning approaches…
Modelling High-Order Social Relations for Item Recommendation
Yang Liu, Liang Chen, Xiangnan He +3
The prevalence of online social network makes it compulsory to study how social relations affect user choice. However, most existing methods leverage only first-order social relati…
Reinforced Negative Sampling over Knowledge Graph for Recommendation
Xiang Wang, Yaokun Xu, Xiangnan He +3
Properly handling missing data is a fundamental challenge in recommendation. Most present works perform negative sampling from unobserved data to supply the training of recommender…