Obtaining Calibrated Probabilities with Personalized Ranking Models
This work addresses the need for reliable probability estimates in recommendation systems, representing an incremental advance by adapting calibration techniques from image classification to personalized ranking.
The paper tackles the problem of obtaining calibrated probabilities for personalized ranking models, proposing Gaussian and Gamma calibration methods along with an unbiased empirical risk minimization framework, which significantly improve calibration performance on real-world datasets.
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.