Practical Multi-Task Learning for Rare Conversions in Ad Tech
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
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| _version_ | 1866916866907176960 |
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| author | Dishi, Yuval Friedler, Ophir Karni, Yonatan Silberstein, Natalia Stolin, Yulia |
| author_facet | Dishi, Yuval Friedler, Ophir Karni, Yonatan Silberstein, Natalia Stolin, Yulia |
| contents | We present a Multi-Task Learning (MTL) approach for improving predictions for rare (e.g., <1%) conversion events in online advertising. The conversions are classified into "rare" or "frequent" types based on historical statistics. The model learns shared representations across all signals while specializing through separate task towers for each type. The approach was tested and fully deployed to production, demonstrating consistent improvements in both offline (0.69% AUC lift) and online KPI performance metric (2% Cost per Action reduction). |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_20161 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Practical Multi-Task Learning for Rare Conversions in Ad Tech Dishi, Yuval Friedler, Ophir Karni, Yonatan Silberstein, Natalia Stolin, Yulia Information Retrieval Machine Learning We present a Multi-Task Learning (MTL) approach for improving predictions for rare (e.g., <1%) conversion events in online advertising. The conversions are classified into "rare" or "frequent" types based on historical statistics. The model learns shared representations across all signals while specializing through separate task towers for each type. The approach was tested and fully deployed to production, demonstrating consistent improvements in both offline (0.69% AUC lift) and online KPI performance metric (2% Cost per Action reduction). |
| title | Practical Multi-Task Learning for Rare Conversions in Ad Tech |
| topic | Information Retrieval Machine Learning |
| url | https://arxiv.org/abs/2507.20161 |