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Adversarial regularized diffusion model for fair recommendations
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AI
DOI：10.1016/j.neunet.2025.107695.png)
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Abstract
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With the widespread deployment of recommendation systems, concerns have grown over algorithmic fairness and representation bias in recommendation outcomes. Existing debiasing methods primarily suffer from two critical limitations: (1) Explicit feature removal strategies risk eliminating semantic signals entangled with sensitive attributes, inevitably degrading recommendation performance. (2) Conventional adversarial learning frameworks impose rigid gradient reversal to enforce independence from sensitive attributes, yet cause semantic distortion in latent representations through uncontrolled adversarial conflicts between fairness objectives and recommendation goals.
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