Consumer-side fairness in recommender systems: a systematic survey of methods and evaluation

In the current landscape of ever-increasing levels of digitalization, we are facing major challenges pertaining to data volume. Recommender systems have become irreplaceable both for helping users navigate the increasing amounts of data and, conversely, aiding providers in marketing products to interested users. Data-driven models are susceptible to data bias, materializing in the bias influencing the models' decision-making. For recommender systems, such issues are well exemplified by occupation recommendation, where biases in historical data may lead to recommender systems relating one gender to lower wages or to the propagation of stereotypes. In particular, consumer-side fairness, which focuses on mitigating discrimination experienced by users of recommender systems, has seen a vast number of diverse approaches. The approaches are further diversified through differing ideas on what constitutes fair and, conversely, discriminatory recommendations. This survey serves as a systematic overview and discussion of the current research on consumer-side fairness in recommender systems. To that end, a novel taxonomy based on high-level fairness definitions is proposed and used to categorize the research and the proposed fairness evaluation metrics. Finally, we highlight some suggestions for the future direction of the field.