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| Autores principales: | , , , |
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| Formato: | Preprint |
| Publicado: |
2026
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| Materias: | |
| Acceso en línea: | https://arxiv.org/abs/2603.06589 |
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| _version_ | 1866911624805220352 |
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| author | Cheng, Hailing Yang, Yafang Tao, Hemeng Zhang, Fengyu |
| author_facet | Cheng, Hailing Yang, Yafang Tao, Hemeng Zhang, Fengyu |
| contents | Model calibration and debiasing are fundamental yet operationally expensive challenges in large-scale recommendation systems. Existing approaches treat them as separate problems requiring distinct infrastructure: post-hoc calibration pipelines, propensity estimation workflows, and per-segment model farms. We introduce the Isotonic Layer, a differentiable piecewise linear module that unifies both problems within a single, lightweight architectural component - requiring no additional data preprocessing, no propensity estimation, and no separate calibration pipelines.
The core insight is elegant: by parameterizing non-negative bucket weights as learnable context embeddings, the model automatically learns all calibration and debiasing functions end-to-end from standard training data. Swapping in a different embedding (position, device type, advertiser ID, or any combination) instantly yields calibration tailored to that sub-segment at arbitrary granularity in any high-dimensional feature space, with no engineering changes beyond a single embedding lookup. The same layer handles post-hoc calibration, position debiasing, and heterogeneous multi-task bias correction within one unified framework. This paper offers a principled, practical simplification: a plug-and-play solution that replaces fragmented, high-maintenance calibration infrastructure with a single end-to-end trainable component. Extensive production A/B tests confirm significant improvements in predictive accuracy, calibration fidelity, and ranking consistency. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_06589 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Isotonic Layer: A Unified Framework for Recommendation Calibration and Debiasing Cheng, Hailing Yang, Yafang Tao, Hemeng Zhang, Fengyu Information Retrieval Artificial Intelligence Machine Learning Model calibration and debiasing are fundamental yet operationally expensive challenges in large-scale recommendation systems. Existing approaches treat them as separate problems requiring distinct infrastructure: post-hoc calibration pipelines, propensity estimation workflows, and per-segment model farms. We introduce the Isotonic Layer, a differentiable piecewise linear module that unifies both problems within a single, lightweight architectural component - requiring no additional data preprocessing, no propensity estimation, and no separate calibration pipelines. The core insight is elegant: by parameterizing non-negative bucket weights as learnable context embeddings, the model automatically learns all calibration and debiasing functions end-to-end from standard training data. Swapping in a different embedding (position, device type, advertiser ID, or any combination) instantly yields calibration tailored to that sub-segment at arbitrary granularity in any high-dimensional feature space, with no engineering changes beyond a single embedding lookup. The same layer handles post-hoc calibration, position debiasing, and heterogeneous multi-task bias correction within one unified framework. This paper offers a principled, practical simplification: a plug-and-play solution that replaces fragmented, high-maintenance calibration infrastructure with a single end-to-end trainable component. Extensive production A/B tests confirm significant improvements in predictive accuracy, calibration fidelity, and ranking consistency. |
| title | Isotonic Layer: A Unified Framework for Recommendation Calibration and Debiasing |
| topic | Information Retrieval Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2603.06589 |