MLPlatt: Simple Calibration Framework for Ranking Models
Fuente:
arXiv
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| Autori principali: | , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2026
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| _version_ | 1866908761481805824 |
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| author | Bajger, Piotr Dusek, Roman Galias, Krzysztof Młyniec, Paweł Wawer, Aleksander Zawistowski, Paweł |
| author_facet | Bajger, Piotr Dusek, Roman Galias, Krzysztof Młyniec, Paweł Wawer, Aleksander Zawistowski, Paweł |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_08345 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | MLPlatt: Simple Calibration Framework for Ranking Models Bajger, Piotr Dusek, Roman Galias, Krzysztof Młyniec, Paweł Wawer, Aleksander Zawistowski, Paweł Information Retrieval Machine Learning 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. |
| title | MLPlatt: Simple Calibration Framework for Ranking Models |
| topic | Information Retrieval Machine Learning |
| url | https://arxiv.org/abs/2601.08345 |