  Due to the extensive growth of information available online, recommender
systems play a more significant role in serving people's interests. Traditional
recommender systems mostly use an accuracy-focused approach to produce
recommendations. Today's research suggests that this single-dimension approach
can lead the system to be biased against a series of items with certain
attributes. Biased recommendations across groups of items can endanger the
interests of item providers along with causing user dissatisfaction with the
system. This study aims to manage a new type of intersectional bias regarding
the geographical origin and popularity of items in the output of
state-of-the-art collaborative filtering recommender algorithms. We introduce
an algorithm called MFAIR, a multi-facet post-processing bias mitigation
algorithm to alleviate these biases. Extensive experiments on two real-world
datasets of movies and books, enriched with the items' continents of
production, show that the proposed algorithm strikes a reasonable balance
between accuracy and both types of the mentioned biases. According to the
results, our proposed approach outperforms a well-known competitor with no or
only a slight loss of efficiency.
