PFA adds a trainable fairness adapter to frozen recommenders and uses hierarchical exposure alignment to balance inter- and intra-group provider visibility, delivering substantial fairness gains with negligible accuracy loss on three public datasets.
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2026 2verdicts
UNVERDICTED 2representative citing papers
Fair re-ranking is equivalent to gradient descent on a ranking manifold under Walrasian equilibrium in an attention market, yielding the ManifoldRank algorithm that adjusts gradients for supply-side fairness costs and demand-side score predictions.
citing papers explorer
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Post-hoc Provider Fairness Adaptation via Hierarchical Exposure Alignment
PFA adds a trainable fairness adapter to frozen recommenders and uses hierarchical exposure alignment to balance inter- and intra-group provider visibility, delivering substantial fairness gains with negligible accuracy loss on three public datasets.
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The Attention Market: Interpreting Online Fair Re-ranking as Manifold Optimization under Walrasian Equilibrium
Fair re-ranking is equivalent to gradient descent on a ranking manifold under Walrasian equilibrium in an attention market, yielding the ManifoldRank algorithm that adjusts gradients for supply-side fairness costs and demand-side score predictions.