Adversarial regularized diffusion model for fair recommendations.

Area of Science:

• Artificial Intelligence
• Machine Learning
• Data Science

Background:

• Recommendation systems face challenges with algorithmic fairness and representation bias.
• Existing debiasing methods often degrade performance by removing semantic signals or distort latent representations through adversarial learning.

Purpose of the Study:

• To propose a novel fairness-aware recommendation framework that addresses limitations of existing debiasing methods.
• To leverage the dynamic equilibrium of diffusion models for improved fairness and recommendation accuracy.

Main Methods:

• Introduced adaptive gradient-aware noise injection during the forward diffusion process, guided by fairness discriminators.
• Employed adversarial regularization with sensitivity-aware gradient constraints in the reverse denoising process.
• Designed an interest fusion mechanism and a bias-controlled rounding function to enhance fairness-utility tradeoffs.

Main Results:

• The proposed model significantly outperforms state-of-the-art methods on three real-world datasets.
• Demonstrated improved recommendation accuracy and fairness compared to existing approaches.
• Successfully achieved feature-aware bias dissociation while preserving user interest semantics.

Conclusions:

• The diffusion model-based framework offers an effective solution for achieving fairness in recommendation systems.
• The method balances recommendation utility and fairness objectives, outperforming conventional debiasing techniques.
• The framework provides a promising direction for developing more equitable and performant recommendation systems.