Fair Augmentation for Graph Collaborative Filtering
<|reference_start|>Fair Augmentation for Graph Collaborative Filtering: 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.<|reference_end|>
@article{boratto2024fair,
    title={Fair Augmentation for Graph Collaborative Filtering},
    author={Ludovico Boratto, Francesco Fabbri, Gianni Fenu, Mirko Marras, Giacomo
  Medda},
    journal={arXiv preprint arXiv:2408.12208},
    year={2024},
    doi={10.1145/3640457.3688064},
    archivePrefix={arXiv},
    eprint={2408.12208},
    primaryClass={cs.IR cs.LG}
}