613 citations · 1.1k across the 36 of their papers we have counts for
7 papers · 1 filter
Incorporating Bias-aware Margins into Contrastive Loss for Collaborative Filtering
An Zhang, Wenchang Ma, Xiang Wang +1
Collaborative filtering (CF) models easily suffer from popularity bias, which makes recommendation deviate from users' actual preferences. However, most current debiasing strategie…
Let Invariant Rationale Discovery Inspire Graph Contrastive Learning
Sihang Li, Xiang Wang, An zhang +3
Leading graph contrastive learning (GCL) methods perform graph augmentations in two fashions: (1) randomly corrupting the anchor graph, which could cause the loss of semantic infor…
CrossCBR: Cross-view Contrastive Learning for Bundle Recommendation
Yunshan Ma, Yingzhi He, An Zhang +2
Bundle recommendation aims to recommend a bundle of related items to users, which can satisfy the users' various needs with one-stop convenience. Recent methods usually take advant…
Differentiable Invariant Causal Discovery
Yu Wang, An Zhang, Xiang Wang +3
Learning causal structure from observational data is a fundamental challenge in machine learning. However, the majority of commonly used differentiable causal discovery methods are…
Reinforced Causal Explainer for Graph Neural Networks
Xiang Wang, Yingxin Wu, An Zhang +3
Explainability is crucial for probing graph neural networks (GNNs), answering questions like "Why the GNN model makes a certain prediction?". Feature attribution is a prevalent tec…
Deconfounding to Explanation Evaluation in Graph Neural Networks
Ying-Xin Wu, Xiang Wang, An Zhang +4
Explainability of graph neural networks (GNNs) aims to answer "Why the GNN made a certain prediction?", which is crucial to interpret the model prediction. The feature attribution…
Discovering Invariant Rationales for Graph Neural Networks
Ying-Xin Wu, Xiang Wang, An Zhang +2
Intrinsic interpretability of graph neural networks (GNNs) is to find a small subset of the input graph's features -- rationale -- which guides the model prediction. Unfortunately,…