  As a highly data-driven application, recommender systems could be affected by
data bias, resulting in unfair results for different data groups, which could
be a reason that affects the system performance. Therefore, it is important to
identify and solve the unfairness issues in recommendation scenarios. In this
paper, we address the unfairness problem in recommender systems from the user
perspective. We group users into advantaged and disadvantaged groups according
to their level of activity, and conduct experiments to show that current
recommender systems will behave unfairly between two groups of users.
Specifically, the advantaged users (active) who only account for a small
proportion in data enjoy much higher recommendation quality than those
disadvantaged users (inactive). Such bias can also affect the overall
performance since the disadvantaged users are the majority. To solve this
problem, we provide a re-ranking approach to mitigate this unfairness problem
by adding constraints over evaluation metrics. The experiments we conducted on
several real-world datasets with various recommendation algorithms show that
our approach can not only improve group fairness of users in recommender
systems, but also achieve better overall recommendation performance.
