Quantifying Fairness Disparities in Graph-Based Neural Network Recommender Systems for Protected Groups

Nikzad Chizari, Keywan Tajfar, Niloufar Shoeibi, María N. Moreno-García

2023

Abstract

The wide acceptance of Recommender Systems (RS) among users for product and service suggestions has led to the proposal of multiple recommendation methods that have contributed to solving the problems presented by these systems. However, the focus on bias problems is much more limited. Some of the most successful and recent methods, such as Graph Neural Networks (GNNs), present problems of bias amplification and unfairness that need to be detected, measured, and addressed. In this study, an analysis of RS fairness is conducted, focusing on measuring unfairness toward protected groups, including gender and age. We quantify fairness disparities within these groups and evaluate recommendation quality for item lists using a metric based on Normalized Discounted Cumulative Gain (NDCG). Most bias assessment metrics in the literature are only valid for the rating prediction approach, but RS usually provide recommendations in the form of item lists. The metric for lists enhances the understanding of fairness dynamics in GNN-based RS, providing a more comprehensive perspective on the quality and equity of recommendations among different user groups.

Paper Citation

in Harvard Style

Chizari N., Tajfar K., Shoeibi N. and N. Moreno-García M. (2023). Quantifying Fairness Disparities in Graph-Based Neural Network Recommender Systems for Protected Groups. In Proceedings of the 19th International Conference on Web Information Systems and Technologies - Volume 1: WEBIST; ISBN 978-989-758-672-9, SciTePress, pages 176-187. DOI: 10.5220/0012258700003584

in Bibtex Style

@conference{webist23,
author={Nikzad Chizari and Keywan Tajfar and Niloufar Shoeibi and María N. Moreno-García},
title={Quantifying Fairness Disparities in Graph-Based Neural Network Recommender Systems for Protected Groups},
booktitle={Proceedings of the 19th International Conference on Web Information Systems and Technologies - Volume 1: WEBIST},
year={2023},
pages={176-187},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012258700003584},
isbn={978-989-758-672-9},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 19th International Conference on Web Information Systems and Technologies - Volume 1: WEBIST
TI - Quantifying Fairness Disparities in Graph-Based Neural Network Recommender Systems for Protected Groups
SN - 978-989-758-672-9
AU - Chizari N.
AU - Tajfar K.
AU - Shoeibi N.
AU - N. Moreno-García M.
PY - 2023
SP - 176
EP - 187
DO - 10.5220/0012258700003584
PB - SciTePress