Towards Fairness Exploration and Optimization for Digital Service Networks

Abstract

Digital service networks often face the challenge of Service-Oriented Fairness (SOF), where service nodes with varying levels of activity may receive unequal treatment. This article takes the recommendation service system as a representative case to explore and mitigate the impact of SOF. The SOF issue in the recommendation service system can be abstracted as User-Oriented Fairness (UOF), where service models often exhibit bias toward a small group of users, resulting in significant unfairness in the quality of recommendations. Existing research on UOF faces three major limitations, and no single approach effectively addresses all of them. Limitation 1: Post-processing methods fail to address the root cause of the UOF issue. Limitation 2: Some in-processing methods rely heavily on unstable user similarity calculations under severe data sparsity problems. Limitation 3: Other in-processing methods overlook the disparate treatment of individual users within user groups. In this article, we propose a novel Individual Reweighting for User-Oriented Fairness framework, namely IR-UOF, to address all the aforementioned limitations. The motivation behind IR-UOF is to introduce an in-processing strategy that addresses the UOF issue at the individual level without the need to explore user similarities. We first conduct extensive experiments on three real-world recommendation service datasets using four backbone recommendation models to demonstrate the effectiveness of IR-UOF in mitigating UOF and improving recommendation fairness. Furthermore, we select two general digital service datasets to prove that IR-UOF can be extended to tackle the general SOF issue in other types of digital service networks. In summary, the IR-UOF framework achieves optimal model performance across all datasets, while improving fairness by at least 3.8% in recommendation systems and 24.7% in general service systems.