  For personalized ranking models, the well-calibrated probability of an item
being preferred by a user has great practical value. While existing work shows
promising results in image classification, probability calibration has not been
much explored for personalized ranking. In this paper, we aim to estimate the
calibrated probability of how likely a user will prefer an item. We investigate
various parametric distributions and propose two parametric calibration
methods, namely Gaussian calibration and Gamma calibration. Each proposed
method can be seen as a post-processing function that maps the ranking scores
of pre-trained models to well-calibrated preference probabilities, without
affecting the recommendation performance. We also design the unbiased empirical
risk minimization framework that guides the calibration methods to learning of
true preference probability from the biased user-item interaction dataset.
Extensive evaluations with various personalized ranking models on real-world
datasets show that both the proposed calibration methods and the unbiased
empirical risk minimization significantly improve the calibration performance.
