Title: New Fairness Metrics for Recommendation that Embrace Differences,
Abstract: 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 against
minority groups of users. 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.