Consumer Fairness Benchmark in Recommendation

Several mitigation procedures have emerged to address consumer unfairness in personalized rankings. However, evaluating their performance is difficult due to variations in experimental protocols, such as differing fairness definitions, data sets, evaluation metrics, and sensitive attributes. This makes it challenging for scientists to choose a suitable procedure for their practical setting. In this paper, we summarize our previous work on investigating the properties a given mitigation procedure against consumer unfairness should be evaluated on. To this end, we defined eight technical properties and leveraged two public datasets to evaluate the extent to which existing mitigation procedures against consumer unfairness met these properties. Source code and data: https://github.com/jackmedda/Perspective-C-Fairness-RecSys.