# Predicting Different Types of Conversions with Multi-Task Learning in Online Advertising

arXiv: https://arxiv.org/abs/1907.10235 · HTML: https://mlsanity.com/paper/1907.10235/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Jul 2019.

## Built on by (most cited first, 21 of 21)

- [Representation Surgery for Multi-Task Model Merging](https://mlsanity.com/paper/2402.02705/) (2024)
- [FM2: Field-matrixed Factorization Machines for Recommender Systems](https://mlsanity.com/paper/2102.12994/) (2021)
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- [STEM: Unleashing the Power of Embeddings for Multi-task Recommendation](https://mlsanity.com/paper/2308.13537/) (2023)
- [User Response Prediction in Online Advertising](https://mlsanity.com/paper/2101.02342/) (2021)
- [Asymptotically Unbiased Estimation for Delayed Feedback Modeling via Label Correction](https://mlsanity.com/paper/2202.06472/) (2022)
- [An Efficient Deep Distribution Network for Bid Shading in First-Price Auctions](https://mlsanity.com/paper/2107.06650/) (2021)
- [Ads Recommendation in a Collapsed and Entangled World](https://mlsanity.com/paper/2403.00793/) (2024)
- [Generalized Delayed Feedback Model with Post-Click Information in Recommender Systems](https://mlsanity.com/paper/2206.00407/) (2022)
- [Follow the Prophet: Accurate Online Conversion Rate Prediction in the Face of Delayed Feedback](https://mlsanity.com/paper/2108.06167/) (2021)
- [Boosting Factorization Machines via Saliency-Guided Mixup](https://mlsanity.com/paper/2206.08661/) (2022)
- [Masked Multi-Domain Network: Multi-Type and Multi-Scenario Conversion Rate Prediction with a Single Model](https://mlsanity.com/paper/2403.17425/) (2024)
- [AutoAttention: Automatic Field Pair Selection for Attention in User Behavior Modeling](https://mlsanity.com/paper/2210.15154/) (2022)
- [Contrastive Learning for Conversion Rate Prediction](https://mlsanity.com/paper/2307.05974/) (2023)
- [Multi-task Offline Reinforcement Learning for Online Advertising in Recommender Systems](https://mlsanity.com/paper/2506.23090/) (2025)
- [Practical Multi-Task Learning for Rare Conversions in Ad Tech](https://mlsanity.com/paper/2507.20161/) (2025)
- [Privacy Preserving Conversion Modeling in Data Clean Room](https://mlsanity.com/paper/2505.14959/) (2025)
- [Large Foundation Model for Ads Recommendation](https://mlsanity.com/paper/2508.14948/) (2025)
- [Conversion rate prediction in online advertising: modeling techniques, performance evaluation and future directions](https://mlsanity.com/paper/2512.01171/) (2025)
- [Follow the TRACE: Exploiting Post-Click Trajectories for Online Delayed Conversion Rate Prediction](https://mlsanity.com/paper/2604.23197/) (2026)

## Abstract (arXiv)

Conversion prediction plays an important role in online advertising since Cost-Per-Action (CPA) has become one of the primary campaign performance objectives in the industry. Unlike click prediction, conversions have different types in nature, and each type may be associated with different decisive factors. In this paper, we formulate conversion prediction as a multi-task learning problem, so that the prediction models for different types of conversions can be learned together. These models share feature representations, but have their specific parameters, providing the benefit of information-sharing across all tasks. We then propose Multi-Task Field-weighted Factorization Machine (MT-FwFM) to solve these tasks jointly. Our experiment results show that, compared with two state-of-the-art models, MT-FwFM improve the AUC by 0.74% and 0.84% on two conversion types, and the weighted AUC across all conversion types is also improved by 0.50%.