Obtaining Calibrated Probabilities with Personalized Ranking Models


  • Wonbin Kweon POSTECH
  • SeongKu Kang POSTECH
  • Hwanjo Yu POSTECH




Data Mining & Knowledge Management (DMKM), Reasoning Under Uncertainty (RU), Machine Learning (ML)


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.




How to Cite

Kweon, W., Kang, S., & Yu, H. (2022). Obtaining Calibrated Probabilities with Personalized Ranking Models. Proceedings of the AAAI Conference on Artificial Intelligence, 36(4), 4083-4091. https://doi.org/10.1609/aaai.v36i4.20326



AAAI Technical Track on Data Mining and Knowledge Management