  Recommender systems learn from historical users' feedback that is often
non-uniformly distributed across items. As a consequence, these systems may end
up suggesting popular items more than niche items progressively, even when the
latter would be of interest for users. This can hamper several core qualities
of the recommended lists (e.g., novelty, coverage, diversity), impacting on the
future success of the underlying platform itself. In this paper, we formalize
two novel metrics that quantify how much a recommender system equally treats
items along the popularity tail. The first one encourages equal probability of
being recommended across items, while the second one encourages true positive
rates for items to be equal. We characterize the recommendations of
representative algorithms by means of the proposed metrics, and we show that
the item probability of being recommended and the item true positive rate are
biased against the item popularity. To promote a more equal treatment of items
along the popularity tail, we propose an in-processing approach aimed at
minimizing the biased correlation between user-item relevance and item
popularity. Extensive experiments show that, with small losses in accuracy, our
popularity-mitigation approach leads to important gains in beyond-accuracy
recommendation quality.
