  We study fairness in collaborative-filtering recommender systems, which are
sensitive to discrimination that exists in historical data. Biased data can
lead collaborative-filtering methods to make unfair predictions for users from
minority groups. We identify the insufficiency of existing fairness metrics and
propose four new metrics that address different forms of unfairness. These
fairness metrics can be optimized by adding fairness terms to the learning
objective. Experiments on synthetic and real data show that our new metrics can
better measure fairness than the baseline, and that the fairness objectives
effectively help reduce unfairness.
