Fair Augmentation for Graph Collaborative Filtering

TL;DR

This paper investigates fair graph augmentation techniques in graph neural network-based recommendation systems, demonstrating their effectiveness across multiple models and datasets, and highlighting transferability challenges.

Contribution

It reproduces and evaluates a recent fair augmentation method, providing insights into its effectiveness and transferability across diverse recommendation models and datasets.

Findings

Fair augmentation improves fairness in high-utility models

Effectiveness is consistent across large datasets

Transferability of fair graphs presents new challenges

Abstract

Recent developments in recommendation have harnessed the collaborative power of graph neural networks (GNNs) in learning users' preferences from user-item networks. Despite emerging regulations addressing fairness of automated systems, unfairness issues in graph collaborative filtering remain underexplored, especially from the consumer's perspective. Despite numerous contributions on consumer unfairness, only a few of these works have delved into GNNs. A notable gap exists in the formalization of the latest mitigation algorithms, as well as in their effectiveness and reliability on cutting-edge models. This paper serves as a solid response to recent research highlighting unfairness issues in graph collaborative filtering by reproducing one of the latest mitigation methods. The reproduced technique adjusts the system fairness level by learning a fair graph augmentation. Under an experimental setup based on 11 GNNs, 5 non-GNN models, and 5 real-world networks across diverse domains, our investigation reveals that fair graph augmentation is consistently effective on high-utility models and large datasets. Experiments on the transferability of the fair augmented graph open new issues for future recommendation studies. Source code: https://github.com/jackmedda/FA4GCF.