Practical Multi-Task Learning for Rare Conversions in Ad Tech
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Abstract (arXiv)
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).
Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 8 of its 17 references that are in the map. First posted on arXiv 27 Jul 2025.
Builds on
8 of 17 references.
References · 8
- 2017An Overview of Multi-Task Learning in Deep Neural Networkscited by 791
- 2017Deep & Cross Network for Ad Click Predictionscited by 392
- 2020DCN V2: Improved Deep & Cross Network and Practical Lessons for Web-scale Learning to Rank Systemscited by 326
- 2018Entire Space Multi-Task Model: An Effective Approach for Estimating Post-Click Conversion Ratecited by 172
- 2022ESCM2: Entire Space Counterfactual Multi-Task Model for Post-Click Conversion Rate Estimationcited by 47
- 2019Predicting Different Types of Conversions with Multi-Task Learning in Online Advertisingcited by 21
- 2023DCMT: A Direct Entire-Space Causal Multi-Task Framework for Post-Click Conversion Estimationcited by 12
- 2023Click-Conversion Multi-Task Model with Position Bias Mitigation for Sponsored Search in eCommercecited by 5
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Citing papers per year: 2025: 1; 2026: 1.
2 papers.