  The observed ratings in most recommender systems are subjected to popularity
bias and are thus not randomly missing. Due to this, only a few popular items
are recommended, and a vast number of non-popular items are hardly recommended.
Not suggesting the non-popular items lead to fewer products dominating the
market and thus offering fewer opportunities for creativity and innovation. In
the literature, several fair algorithms have been proposed which mainly focused
on improving the accuracy of the recommendation system. However, a typical
accuracy measure is biased towards popular items, i.e., it promotes better
accuracy for popular items compared to non-popular items. This paper considers
a metric that measures the popularity bias as the difference in error on
popular items and non-popular items. Motivated by the fair boosting algorithm
on classification, we propose an algorithm that reduces the popularity bias
present in the data while maintaining accuracy within acceptable limits. The
main idea of our algorithm is that it lifts the weights of the non-popular
items, which are generally underrepresented in the data. With the help of
comprehensive experiments on real-world datasets, we show that our proposed
algorithm outperforms the existing algorithms on the proposed popularity bias
metric.
