  Enabling non-discrimination for end-users of recommender systems by
introducing consumer fairness is a key problem, widely studied in both academia
and industry. Current research has led to a variety of notions, metrics, and
unfairness mitigation procedures. The evaluation of each procedure has been
heterogeneous and limited to a mere comparison with models not accounting for
fairness. It is hence hard to contextualize the impact of each mitigation
procedure w.r.t. the others. In this paper, we conduct a systematic analysis of
mitigation procedures against consumer unfairness in rating prediction and
top-n recommendation tasks. To this end, we collected 15 procedures proposed in
recent top-tier conferences and journals. Only 8 of them could be reproduced.
Under a common evaluation protocol, based on two public data sets, we then
studied the extent to which recommendation utility and consumer fairness are
impacted by these procedures, the interplay between two primary fairness
notions based on equity and independence, and the demographic groups harmed by
the disparate impact. Our study finally highlights open challenges and future
directions in this field. The source code is available at
https://github.com/jackmedda/C-Fairness-RecSys.
