Hypergraph Convolutional Network for User-Oriented Fairness in Recommender Systems

The service system involves multiple stakeholders, making it crucial to ensure fairness. In this paper, we take the example of a typical service system, the recommender system, to investigate how to identify and tackle fairness issues within the service system. Recommender systems often exhibit bias towards a small user group, resulting in pronounced unfairness in recommendation performance, specifically the User-Oriented Fairness (UOF) issue. Existing research on UOF faces limitations in addressing two pivotal challenges: CH1: Current methods fall short in addressing the root cause of the UOF issue, stemming from an unfair training process between advantaged and disadvantaged users. CH2: Current methods struggle to unveil compelling correlations among users in sparse datasets. In this paper, we propose a novel Hypergraph Convolutional Network for User-Oriented Fairness, namely HyperUOF, to address the aforementioned challenges. HyperUOF serves as a versatile framework applicable to various backbone recommendation models for achieving UOF. To address CH1, HyperUOF employs an in-processing method that enhances the training process of disadvantaged users during model training. To addressCH2, HyperUOF incorporates a hypergraph-based approach, proven effective in sparse datasets, to explore high-order correlations among users. We conduct extensive experiments on three real-world datasets based on four backbone recommendation models to prove the effectiveness of our proposed HyperUOF.