In recent years, personalization research has been delving into issues of
explainability and fairness. While some techniques have emerged to provide
post-hoc and self-explanatory individual recommendations, there is still a lack
of methods aimed at uncovering unfairness in recommendation systems beyond
identifying biased user and item features. This paper proposes a new algorithm,
GNNUERS, which uses counterfactuals to pinpoint user unfairness explanations in
terms of user-item interactions within a bi-partite graph. By perturbing the
graph topology, GNNUERS reduces differences in utility between protected and
unprotected demographic groups. The paper evaluates the approach using four
real-world graphs from different domains and demonstrates its ability to
systematically explain user unfairness in three state-of-the-art GNN-based
recommendation models. This perturbed network analysis reveals insightful
patterns that confirm the nature of the unfairness underlying the explanations.
The source code and preprocessed datasets are available at
https://github.com/jackmedda/RS-BGExplaine