31 citations · 78 across the 15 of their papers we have counts for
4 papers · 1 filter
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…
CeFlow: A Robust and Efficient Counterfactual Explanation Framework for Tabular Data using Normalizing Flows
Tri Dung Duong, Qian Li, Guandong Xu
Counterfactual explanation is a form of interpretable machine learning that generates perturbations on a sample to achieve the desired outcome. The generated samples can act as ins…
Achieving Counterfactual Fairness with Imperfect Structural Causal Model
Tri Dung Duong, Qian Li, Guandong Xu
Counterfactual fairness alleviates the discrimination between the model prediction toward an individual in the actual world (observational data) and that in counterfactual world (i…