FairDgcl: Fairness-aware Recommendation with Dynamic Graph Contrastive Learning

TL;DR

FairDgcl introduces a dynamic graph contrastive learning framework that generates fair data augmentations to improve recommendation fairness without compromising accuracy, validated through experiments on real datasets.

Contribution

It proposes a novel adversarial contrastive learning approach with dynamic, learnable augmentation strategies for fairness-aware recommendation systems.

Findings

Improves fairness in recommendations without sacrificing accuracy.

Effective across four real-world datasets.

Outperforms existing fairness-aware methods.

Abstract

As trustworthy AI continues to advance, the fairness issue in recommendations has received increasing attention. A recommender system is considered unfair when it produces unequal outcomes for different user groups based on user-sensitive attributes (e.g., age, gender). Some researchers have proposed data augmentation-based methods aiming at alleviating user-level unfairness by altering the skewed distribution of training data among various user groups. Despite yielding promising results, they often rely on fairness-related assumptions that may not align with reality, potentially reducing the data quality and negatively affecting model effectiveness. To tackle this issue, in this paper, we study how to implement high-quality data augmentation to improve recommendation fairness. Specifically, we propose FairDgcl, a dynamic graph adversarial contrastive learning framework aiming at improving fairness in recommender system. First, FairDgcl develops an adversarial contrastive network with a view generator and a view discriminator to learn generating fair augmentation strategies in an adversarial style. Then, we propose two dynamic, learnable models to generate contrastive views within contrastive learning framework, which automatically fine-tune the augmentation strategies. Meanwhile, we theoretically show that FairDgcl can simultaneously generate enhanced representations that possess both fairness and accuracy. Lastly, comprehensive experiments conducted on four real-world datasets demonstrate the effectiveness of the proposed FairDgcl.