Obtaining Calibrated Probabilities with Personalized Ranking Models

TL;DR

This paper introduces two calibration methods for personalized ranking models to produce well-calibrated preference probabilities, improving their practical utility without compromising recommendation accuracy.

Contribution

It proposes Gaussian and Gamma calibration methods as post-processing techniques and an unbiased empirical risk minimization framework for better probability calibration in ranking models.

Findings

Both calibration methods significantly improve calibration performance.

The unbiased empirical risk minimization enhances true preference probability estimation.

Calibration does not negatively impact recommendation performance.

Abstract

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.