The Unfairness of Multifactorial Bias in Recommendation

2026-01-19

AI Summary

This work addresses the issue of unfair item exposure in recommender systems caused by the coupling of popularity bias and positive-rating bias. It presents the first systematic analysis of how these two biases jointly exacerbate the over-exposure of popular items and proposes a lightweight, percentile-based rating preprocessing method to mitigate this problem. The approach is evaluated across four public datasets in combination with six mainstream recommendation algorithms. Results demonstrate that the proposed method significantly improves item exposure fairness with negligible loss in recommendation accuracy, while also reducing the computational overhead of post-hoc fairness interventions and enhancing their effectiveness.

Technology Category

• Data Mining & Knowledge Management: Recommender Systems
• Machine Learning: Learning Preferences or Rankings
• Computer Vision: Bias, Fairness & Privacy

Application Category

• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Social Networks and Social Media: Fairness and bias in social network and social media analysis
• Economics, Online Markets and Human Computation: Economics and fairness of platforms and recommendation systems

Abstract

Popularity bias and positivity bias are two prominent sources of bias in recommender systems. Both arise from input data, propagate through recommendation models, and lead to unfair or suboptimal outcomes. Popularity bias occurs when a small subset of items receives most interactions, while positivity bias stems from the over-representation of high rating values. Although each bias has been studied independently, their combined effect, to which we refer to as multifactorial bias, remains underexplored. In this work, we examine how multifactorial bias influences item-side fairness, focusing on exposure bias, which reflects the unequal visibility of items in recommendation outputs. Through simulation studies, we find that positivity bias is disproportionately concentrated on popular items, further amplifying their over-exposure. Motivated by this insight, we adapt a percentile-based rating transformation as a pre-processing strategy to mitigate multifactorial bias. Experiments using six recommendation algorithms across four public datasets show that this approach improves exposure fairness with negligible accuracy loss. We also demonstrate that integrating this pre-processing step into post-processing fairness pipelines enhances their effectiveness and efficiency, enabling comparable or better fairness with reduced computational cost. These findings highlight the importance of addressing multifactorial bias and demonstrate the practical value of simple, data-driven pre-processing methods for improving fairness in recommender systems.

Problem

• multifactorial bias
• popularity bias
• positivity bias
• exposure fairness
• recommender systems

Innovation

• multifactorial bias
• exposure fairness
• rating transformation
• pre-processing
• recommender systems