Recommendation Fairness

Recommendation fairness focuses on mitigating biases in recommender systems that lead to unfair or discriminatory outcomes for certain user groups. Current research emphasizes developing methods to incorporate multiple, potentially conflicting, fairness definitions, often using multi-agent frameworks or graph neural networks to model complex relationships between users, items, and sensitive attributes. These efforts aim to improve the equity and accuracy of recommendations, impacting both the user experience and the broader societal implications of algorithmic decision-making. The field is actively exploring techniques like counterfactual explanations, data augmentation, and fairness-aware model training to achieve this goal.