In multi-seller online marketplaces, centrally aggregating user interaction data to drive personalized recommendations often leads to cross-seller privacy leakage, which results in the potential reconstruction of sensitive preferences and unintended disclosure of sellers’ strategic signals. Privacy-preserving mechanisms that rely only on public, shareable signals can enable personalization in these settings by augmenting local marketplace feedback …
Day: December 26, 2025
GNN’s FAME: Fairness-Aware MEssages for Graph Neural Networks
In graph-based prediction settings, standard message passing in Graph Neural Networks often propagates correlations between neighborhoods and sensitive attributes, which results in biased node representations and unfair classification outcomes. In-processing mechanisms that modulate messages using protected-attribute relationships can enable fairness-aware representation learning by attenuating bias amplification during aggregation, as instantiated in this study through fairness-aware …