  Accurate predictions in session-based recommendations have progressed, but a
few studies have focused on skewed recommendation lists caused by popularity
bias. Existing models for mitigating popularity bias have attempted to reduce
the overconcentration of popular items by amplifying scores of less popular
items. However, they normally ignore the users' different preferences toward
long-tail items. Thus, we incorporate calibration, where calibrated
recommendations reflect the users' interests in recommendation lists with
appropriate proportions, to mitigate the popularity bias from the user's
perspective. Specifically, we propose a calibration module to predict the ratio
of tail items in the recommendation list from the session representation, and
align it to the ongoing session. Additionally, we utilize a two-stage
curriculum training strategy to improve prediction in the calibration module.
Experiments on benchmark datasets show that our model can both achieve the
competitive accuracy of recommendation and provide more tail items.
