Dual path fairness optimization for graph neural network based recommendation.

Area of Science:

• Artificial Intelligence
• Machine Learning
• Recommender Systems

Background:

• Graph Neural Networks (GNNs) excel at modeling complex user-item interactions.
• However, GNNs can amplify inherent data biases, leading to unfair recommendations.
• Existing fairness methods struggle to address biases embedded in graph topology.

Purpose of the Study:

• To develop a novel method, Fair Dual-path Alignment (FairDA), to mitigate group unfairness in GNN-based recommender systems.
• To address biases originating from both node attributes and graph topology.
• To enhance recommendation fairness without compromising predictive accuracy.

Main Methods:

• FairDA aligns user embeddings from original data with fair embeddings derived from data excluding sensitive attributes.
• An information bottleneck mutual information constraint is employed to retain collaborative filtering signals while removing sensitive information.
• Dynamic adjustment of loss weights for similar item pairs regulates inter-group item relationships to reduce bias.

Main Results:

• FairDA effectively reduces biases present in both node attributes and graph topology.
• The method produces low-bias user representations conducive to fair recommendations.
• Experiments demonstrate FairDA achieves a superior balance between recommendation accuracy and fairness.

Conclusions:

• FairDA offers a robust solution for achieving fairness in GNN-based recommender systems.
• The proposed approach successfully tackles multifaceted biases within graph structures.
• FairDA demonstrates significant improvements in fairness metrics while maintaining high recommendation performance.