  As recommender systems have become more widespread and moved into areas with
greater social impact, such as employment and housing, researchers have begun
to seek ways to ensure fairness in the results that such systems produce. This
work has primarily focused on developing recommendation approaches in which
fairness metrics are jointly optimized along with recommendation accuracy.
However, the previous work had largely ignored how individual preferences may
limit the ability of an algorithm to produce fair recommendations. Furthermore,
with few exceptions, researchers have only considered scenarios in which
fairness is measured relative to a single sensitive feature or attribute (such
as race or gender). In this paper, we present a re-ranking approach to
fairness-aware recommendation that learns individual preferences across
multiple fairness dimensions and uses them to enhance provider fairness in
recommendation results. Specifically, we show that our opportunistic and
metric-agnostic approach achieves a better trade-off between accuracy and
fairness than prior re-ranking approaches and does so across multiple fairness
dimensions.
