Towards Fairness Exploration and Optimization for Digital Service Networks
Zhongxuan Han, Li Zhang, Chaochao Chen, Xiaolin Zheng, Yuyuan Li, Shuiguang Deng, Guanjie Cheng, Schahram Dustdar · IEEE Transactions on Services Computing · 2025
Digital service networks often face the challenge ofService-OrientedFairness (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 asUser-OrientedFairness (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 novelIndividualReweighting forUser-OrientedFairness framework, namely IR-UOF, to address all the aforementioned limitations. The motivation behind IR-UOF is tointroduce 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.