Bias Disparity in Collaborative Recommendation: Algorithmic Evaluation and Comparison
This addresses fairness concerns in recommender systems for users, though it appears incremental as it evaluates existing algorithms rather than proposing new ones.
The paper investigates how different collaborative filtering algorithms balance ranking quality against bias disparity, which measures how well group preferences are reflected in recommendations, finding that algorithms vary in their tradeoffs between these metrics.
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.