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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HaNoRec dynamically weights harder preference samples and applies Gaussian perturbations to output distributions to improve multimodal LLM performance on sequential recommendation tasks.
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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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Multimodal Large Language Models with Adaptive Preference Optimization for Sequential Recommendation
HaNoRec dynamically weights harder preference samples and applies Gaussian perturbations to output distributions to improve multimodal LLM performance on sequential recommendation tasks.