Heterophily-Aware Fair Recommendation using Graph Convolutional Networks

TL;DR

This paper introduces HetroFair, a novel GNN-based recommender system that enhances item fairness and reduces popularity bias by employing heterophily-aware attention and feature weighting, achieving better fairness and accuracy.

Contribution

HetroFair is the first GNN model to explicitly address item fairness and popularity bias through heterophily-aware mechanisms in recommendation systems.

Findings

Reduces unfairness and popularity bias on the item side.

Achieves superior accuracy on user preferences.

Effective across six real-world datasets.

Abstract

In recent years, graph neural networks (GNNs) have become a popular tool to improve the accuracy and performance of recommender systems. Modern recommender systems are not only designed to serve end users, but also to benefit other participants, such as items and item providers. These participants may have different or conflicting goals and interests, which raises the need for fairness and popularity bias considerations. GNN-based recommendation methods also face the challenges of unfairness and popularity bias, and their normalization and aggregation processes suffer from these challenges. In this paper, we propose a fair GNN-based recommender system, called HetroFair, to improve item-side fairness. HetroFair uses two separate components to generate fairness-aware embeddings: i) Fairness-aware attention, which incorporates the dot product in the normalization process of GNNs to decrease the effect of nodes' degrees. ii) Heterophily feature weighting, to assign distinct weights to different features during the aggregation process. To evaluate the effectiveness of HetroFair, we conduct extensive experiments over six real-world datasets. Our experimental results reveal that HetroFair not only alleviates unfairness and popularity bias on the item side but also achieves superior accuracy on the user side. Our implementation is publicly available at https://github.com/NematGH/HetroFair.