Improving recommendation fairness with dependency-based graph collaborative filtering

Abstract Collaborative filtering recommendation systems that utilize graph convolutional neural networks (GCNs) often emphasize on training speed and accuracy, while overlooking the critical issue of fairness. This paper introduces FairGCF, a novel graph-based collaborative filtering model designed to address fairness concerns in GCN-based recommendations. FairGCF improves the representational capacity of graph collaborative filtering models by identifying dependencies between nodes in the graph, resulting in fairer and more accurate recommendations. The model constructs a dependency graph between users and items to capture higher order interaction patterns, aggregating features from users and items with similar dependencies to enrich their representations. Additionally, a fairness loss factor is incorporated to minimize disparities between positive and negative samples, thereby promoting fairness. Experiments on three public datasets demonstrate that FairGCF improves the Recall metric by an average of 6%, NDCG by 8.79%, Precision by 3.68%, and F1 score by 3.62%. Notably, the model is carefully designed to avoid bias toward specific users or items.