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Found 5 papers, 4 papers with code
Robustness in Fairness against Edge-level Perturbations in GNN-based Recommendation
Efforts in the recommendation community are shifting from the sole emphasis on utility to considering beyond-utility factors, such as fairness and robustness.
Counterfactual Graph Augmentation for Consumer Unfairness Mitigation in Recommender Systems
In recommendation literature, explainability and fairness are becoming two prominent perspectives to consider.
GNNUERS: Fairness Explanation in GNNs for Recommendation via Counterfactual Reasoning
Moreover, an empirical evaluation of the perturbed network uncovered relevant patterns that justify the nature of the unfairness discovered by the generated explanations.
Consumer Fairness in Recommender Systems: Contextualizing Definitions and Mitigations
In this paper, we conduct a systematic analysis of mitigation procedures against consumer unfairness in rating prediction and top-n recommendation tasks.
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