In-processing User Constrained Dominant Sets for User-Oriented Fairness
  in Recommender Systems
Zhongxuan Han Chaochao Chen Xiaolin Zheng Weiming Liu Jun Wang Wenjie Cheng Yuyuan Li
Recommender systems are typically biased toward a small group of users,
leading to severe unfairness in recommendation performance, i.e., User-Oriented
Fairness (UOF) issue. The existing research on UOF is limited and fails to deal
with the root cause of the UOF issue: the learning process between advantaged
and disadvantaged users is unfair. To tackle this issue, we propose an
In-processing User Constrained Dominant Sets (In-UCDS) framework, which is a
general framework that can be applied to any backbone recommendation model to
achieve user-oriented fairness. We split In-UCDS into two stages, i.e., the
UCDS modeling stage and the in-processing training stage. In the UCDS modeling
stage, for each disadvantaged user, we extract a constrained dominant set (a
user cluster) containing some advantaged users that are similar to it. In the
in-processing training stage, we move the representations of disadvantaged
users closer to their corresponding cluster by calculating a fairness loss. By
combining the fairness loss with the original backbone model loss, we address
the UOF issue and maintain the overall recommendation performance
simultaneously. Comprehensive experiments on three real-world datasets
demonstrate that In-UCDS outperforms the state-of-the-art methods, leading to a
fairer model with better overall recommendation performance.