Abstract
Ranking models primarily focus on modeling the relative order of predictions while often neglecting the significance of the accuracy of their absolute values. However, accurate absolute values are essential for certain downstream tasks, necessitating the calibration of the original predictions. To address this, existing calibration approaches typically employ predefined transformation functions with order-preserving properties to adjust the original predictions. Unfortunately, these functions often adhere to fixed forms, such as piece-wise linear functions, which exhibit limited expressiveness and flexibility, thereby constraining their effectiveness in complex calibration scenarios. To mitigate this issue, we propose implementing a calibrator using an Unconstrained Monotonic Neural Network (UMNN), which can learn arbitrary monotonic functions with great modeling power. This approach significantly relaxes the constraints on the calibrator, improving its flexibility and expressiveness while avoiding excessively distorting the original predictions by requiring monotonicity. Furthermore, to optimize this highly flexible network for calibration, we introduce a novel additional loss function termed Smooth Calibration Loss (SCLoss), which aims to fulfill a necessary condition for achieving the ideal calibration state. Extensive offline experiments confirm the effectiveness of our method in achieving superior calibration performance. Moreover, deployment in Kuaishou’s large-scale online video ranking system demonstrates that the method’s calibration improvements translate into enhanced business metrics. The source code is available at https://github.com/baiyimeng/UMC.
Citation:
@inproceedings{UMC,
author = {Bai, Yimeng and Zhang, Shunyu and Zhang, Yang and Liu, Hu and Bao, Wentian and Yu, Enyun and Feng, Fuli and Ou, Wenwu},
title = {Unconstrained Monotonic Calibration of Predictions in Deep Ranking Systems},
year = {2025},
isbn = {9798400715921},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3726302.3730105},
doi = {10.1145/3726302.3730105},
booktitle = {Proceedings of the 48th International ACM SIGIR Conference on Research and Development in Information Retrieval},
numpages = {11},
keywords = {calibrator modeling, unconstrained monotonic neural network,
ranking system},
location = {Padua, Italy},
series = {SIGIR '25}
}
Yimeng Bai
白移梦
