dorsal/arxiv
View SchemaMLPlatt: Simple Calibration Framework for Ranking Models
| Authors | Piotr Bajger, Roman Dusek, Krzysztof Galias, Paweł Młyniec, Aleksander Wawer, Paweł Zawistowski |
|---|---|
| Categories | |
| ArXiv ID | 2601.08345vv1 |
| URL | https://arxiv.org/abs/2601.08345 |
| License | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ |
Abstract
Ranking models are extensively used in e-commerce for relevance estimation. These models often suffer from poor interpretability and no scale calibration, particularly when trained with typical ranking loss functions. This paper addresses the problem of post-hoc calibration of ranking models. We introduce MLPlatt: a simple yet effective ranking model calibration method that preserves the item ordering and converts ranker outputs to interpretable click-through rate (CTR) probabilities usable in downstream tasks. The method is context-aware by design and achieves good calibration metrics globally, and within strata corresponding to different values of a selected categorical field (such as user country or device), which is often important from a business perspective of an E-commerce platform. We demonstrate the superiority of MLPlatt over existing approaches on two datasets, achieving an improvement of over 10\% in F-ECE (Field Expected Calibration Error) compared to other methods. Most importantly, we show that high-quality calibration can be achieved without compromising the ranking quality.
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"abstract": "Ranking models are extensively used in e-commerce for relevance estimation. These models often suffer from poor interpretability and no scale calibration, particularly when trained with typical ranking loss functions. This paper addresses the problem of post-hoc calibration of ranking models. We introduce MLPlatt: a simple yet effective ranking model calibration method that preserves the item ordering and converts ranker outputs to interpretable click-through rate (CTR) probabilities usable in downstream tasks. The method is context-aware by design and achieves good calibration metrics globally, and within strata corresponding to different values of a selected categorical field (such as user country or device), which is often important from a business perspective of an E-commerce platform. We demonstrate the superiority of MLPlatt over existing approaches on two datasets, achieving an improvement of over 10\\% in F-ECE (Field Expected Calibration Error) compared to other methods. Most importantly, we show that high-quality calibration can be achieved without compromising the ranking quality.",
"arxiv_id": "2601.08345",
"authors": [
"Piotr Bajger",
"Roman Dusek",
"Krzysztof Galias",
"Pawe\u0142 M\u0142yniec",
"Aleksander Wawer",
"Pawe\u0142 Zawistowski"
],
"categories": [
"cs.IR",
"cs.LG"
],
"license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
"title": "MLPlatt: Simple Calibration Framework for Ranking Models",
"url": "https://arxiv.org/abs/2601.08345",
"version": "v1"
},
"schema_id": "dorsal/arxiv",
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"type": "Model",
"variant": "snapshot-2026-01-17",
"version": "0.1.0"
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