A category-aware re-ranking method reduces group differences in recommended item categories by pulling each user's recommendations toward the historical category mix of users with a different sensitive attribute.
Bias Disparity in Collaborative Recommendation: Algorithmic Evaluation and Comparison
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abstract
Research on fairness in machine learning has been recently extended to recommender systems. One of the factors that may impact fairness is bias disparity, the degree to which a group's preferences on various item categories fail to be reflected in the recommendations they receive. In some cases biases in the original data may be amplified or reversed by the underlying recommendation algorithm. In this paper, we explore how different recommendation algorithms reflect the tradeoff between ranking quality and bias disparity. Our experiments include neighborhood-based, model-based, and trust-aware recommendation algorithms.
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cs.IR 1years
2025 1verdicts
CONDITIONAL 1representative citing papers
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Bias vs Bias -- Dawn of Justice: A Fair Fight in Recommendation Systems
A category-aware re-ranking method reduces group differences in recommended item categories by pulling each user's recommendations toward the historical category mix of users with a different sensitive attribute.