Disentangling Popularity and Quality: An Edge Classification Approach for Fair Recommendation

TL;DR

This paper introduces a GNN-based recommendation model that disentangles popularity and quality using edge classification and cost-sensitive learning to improve fairness without sacrificing accuracy.

Contribution

It proposes a novel edge classification approach combined with cost-sensitive learning to address popularity bias in recommender systems.

Findings

Fairness metrics improved by approximately 32% on average.

Maintains competitive accuracy with only minor variations.

Effectively differentiates between popularity bias and genuine quality.

Abstract

Graph neural networks (GNNs) have proven to be an effective tool for enhancing the performance of recommender systems. However, these systems often suffer from popularity bias, leading to an unfair advantage for frequently interacted items, while overlooking high-quality but less popular items. In this paper, we propose a GNN-based recommendation model that disentangles popularity and quality to address this issue. Unlike existing methods that treat all long-tail items uniformly, our approach introduces an edge classification technique to differentiate between popularity bias and genuine quality disparities among items. Furthermore, it uses cost-sensitive learning to adjust the misclassification penalties, ensuring that underrepresented yet relevant items are not unfairly disregarded. Experimental results demonstrate improvements in fairness metrics by approximately $32\%$ on average across different scenarios while maintaining competitive accuracy, with only minor variations compared to state-of-the-art methods.