  Recommender systems often suffer from popularity bias, where popular items
are overly recommended while sacrificing unpopular items. Existing researches
generally focus on ensuring the number of recommendations exposure of each item
is equal or proportional, using inverse propensity weighting, causal
intervention, or adversarial training. However, increasing the exposure of
unpopular items may not bring more clicks or interactions, resulting in skewed
benefits and failing in achieving real reasonable popularity debiasing. In this
paper, we propose a new criterion for popularity debiasing, i.e., in an
unbiased recommender system, both popular and unpopular items should receive
Interactions Proportional to the number of users who Like it, namely IPL
criterion. Under the guidance of the criterion, we then propose a debiasing
framework with IPL regularization term which is theoretically shown to achieve
a win-win situation of both popularity debiasing and recommendation
performance. Experiments conducted on four public datasets demonstrate that
when equipping two representative collaborative filtering models with our
framework, the popularity bias is effectively alleviated while maintaining the
recommendation performance.
