  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.
