In-processing User Constrained Dominant Sets for User-Oriented Fairness in Recommender Systems

by   Zhongxuan Han, et al.
Zhejiang University

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.


Comprehensive Fair Meta-learned Recommender System

In recommender systems, one common challenge is the cold-start problem, ...

Ensuring User-side Fairness in Dynamic Recommender Systems

User-side group fairness is crucial for modern recommender systems, as i...

Experiments on Generalizability of User-Oriented Fairness in Recommender Systems

Recent work in recommender systems mainly focuses on fairness in recomme...

User-item matching for recommendation fairness: a view from item-providers

As we all know, users and item-providers are two main groups of particip...

Provider Fairness and Beyond-Accuracy Trade-offs in Recommender Systems

Recommender systems, while transformative in online user experiences, ha...

Recommender Systems Fairness Evaluation via Generalized Cross Entropy

Fairness in recommender systems has been considered with respect to sens...

Equal Experience in Recommender Systems

We explore the fairness issue that arises in recommender systems. Biased...

Please sign up or login with your details

Forgot password? Click here to reset