12 citations · 18 across the 8 of their papers we have counts for
8 papers
Inductive Subgraphs as Shortcuts: Causal Disentanglement for Heterophilic Graph Learning
Xiangmeng Wang, Qian Li, Haiyang Xia +3
Heterophily is a prevalent property of real-world graphs and is well known to impair the performance of homophilic Graph Neural Networks (GNNs). Prior work has attempted to adapt G…
Multi-Agent Causal Reasoning for Suicide Ideation Detection Through Online Conversations
Jun Li, Xiangmeng Wang, Haoyang Li +6
Suicide remains a pressing global public health concern. While social media platforms offer opportunities for early risk detection through online conversation trees, existing appro…
Protective Factor-Aware Dynamic Influence Learning for Suicide Risk Prediction on Social Media
Jun Li, Xiangmeng Wang, Haoyang Li +5
Suicide is a critical global health issue that requires urgent attention. Even though prior work has revealed valuable insights into detecting current suicide risk on social media,…
Counterfactual Explanation for Fairness in Recommendation
Xiangmeng Wang, Qian Li, Dianer Yu +2
Fairness-aware recommendation eliminates discrimination issues to build trustworthy recommendation systems.Explaining the causes of unfair recommendations is critical, as it promot…
Neural Causal Graph Collaborative Filtering
Xiangmeng Wang, Qian Li, Dianer Yu +2
Graph collaborative filtering (GCF) has gained considerable attention in recommendation systems by leveraging graph learning techniques to enhance collaborative filtering (CF). One…
Reinforced Path Reasoning for Counterfactual Explainable Recommendation
Xiangmeng Wang, Qian Li, Dianer Yu +1
Counterfactual explanations interpret the recommendation mechanism via exploring how minimal alterations on items or users affect the recommendation decisions. Existing counterfact…
Causal Disentanglement for Semantics-Aware Intent Learning in Recommendation
Xiangmeng Wang, Qian Li, Dianer Yu +3
Traditional recommendation models trained on observational interaction data have generated large impacts in a wide range of applications, it faces bias problems that cover users' t…
Be Causal: De-biasing Social Network Confounding in Recommendation
Qian Li, Xiangmeng Wang, Guandong Xu
In recommendation systems, the existence of the missing-not-at-random (MNAR) problem results in the selection bias issue, degrading the recommendation performance ultimately. A com…