# On the Practice of Deep Hierarchical Ensemble Network for Ad Conversion Rate Prediction

arXiv: https://arxiv.org/abs/2504.08169 · HTML: https://mlsanity.com/paper/2504.08169/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 26 of its 44 references that are in the map.
- First posted on arXiv 10 Apr 2025.

## Builds on (most cited first, 26 of 26)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Representation Learning with Contrastive Predictive Coding](https://mlsanity.com/paper/1807.03748/) (2018)
- [Inductive Representation Learning on Large Graphs](https://mlsanity.com/paper/1706.02216/) (2017)
- [Squeeze-and-Excitation Networks](https://mlsanity.com/paper/1709.01507/) (2017)
- [Graph Convolutional Neural Networks for Web-Scale Recommender Systems](https://mlsanity.com/paper/1806.01973/) (2018)
- [DeepFM: A Factorization-Machine based Neural Network for CTR Prediction](https://mlsanity.com/paper/1703.04247/) (2017)
- [A Survey on Contrastive Self-supervised Learning](https://mlsanity.com/paper/2011.00362/) (2020)
- [Deep Interest Network for Click-Through Rate Prediction](https://mlsanity.com/paper/1706.06978/) (2017)
- [Deep & Cross Network for Ad Click Predictions](https://mlsanity.com/paper/1708.05123/) (2017)
- [Deep Interest Evolution Network for Click-Through Rate Prediction](https://mlsanity.com/paper/1809.03672/) (2018)
- [DCN V2: Improved Deep & Cross Network and Practical Lessons for Web-scale Learning to Rank Systems](https://mlsanity.com/paper/2008.13535/) (2020)
- [Search-based User Interest Modeling with Lifelong Sequential Behavior Data for Click-Through Rate Prediction](https://mlsanity.com/paper/2006.05639/) (2020)
- [Practice on Long Sequential User Behavior Modeling for Click-Through Rate Prediction](https://mlsanity.com/paper/1905.09248/) (2019)
- [Self-Supervised Learning for Recommender Systems: A Survey](https://mlsanity.com/paper/2203.15876/) (2022)
- [FiBiNET: Combining Feature Importance and Bilinear feature Interaction for Click-Through Rate Prediction](https://mlsanity.com/paper/1905.09433/) (2019)
- [PinnerSage: Multi-Modal User Embedding Framework for Recommendations at Pinterest](https://mlsanity.com/paper/2007.03634/) (2020)
- [Wukong: Towards a Scaling Law for Large-Scale Recommendation](https://mlsanity.com/paper/2403.02545/) (2024)
- [Deep Session Interest Network for Click-Through Rate Prediction](https://mlsanity.com/paper/1905.06482/) (2019)
- [PinnerFormer: Sequence Modeling for User Representation at Pinterest](https://mlsanity.com/paper/2205.04507/) (2022)
- [MaskNet: Introducing Feature-Wise Multiplication to CTR Ranking Models by Instance-Guided Mask](https://mlsanity.com/paper/2102.07619/) (2021)
- [Learning a Unified Embedding for Visual Search at Pinterest](https://mlsanity.com/paper/1908.01707/) (2019)
- [Towards Deeper, Lighter and Interpretable Cross Network for CTR Prediction](https://mlsanity.com/paper/2311.04635/) (2023)
- [Rethinking Personalized Ranking at Pinterest: An End-to-End Approach](https://mlsanity.com/paper/2209.08435/) (2022)
- [Modeling Users' Contextualized Page-wise Feedback for Click-Through Rate Prediction in E-commerce Search](https://mlsanity.com/paper/2203.15542/) (2022)
- [FiBiNet++: Reducing Model Size by Low Rank Feature Interaction Layer for CTR Prediction](https://mlsanity.com/paper/2209.05016/) (2022)
- [Triangle Graph Interest Network for Click-through Rate Prediction](https://mlsanity.com/paper/2202.02698/) (2022)

## Built on by (most cited first, 3 of 3)

- [See Beyond a Single View: Multi-Attribution Learning Leads to Better Conversion Rate Prediction](https://mlsanity.com/paper/2508.15217/) (2025)
- [Decoupled Entity Representation Learning for Pinterest Ads Ranking](https://mlsanity.com/paper/2509.04337/) (2025)
- [Conversion rate prediction in online advertising: modeling techniques, performance evaluation and future directions](https://mlsanity.com/paper/2512.01171/) (2025)

## Abstract (arXiv)

The predictions of click through rate (CTR) and conversion rate (CVR) play a crucial role in the success of ad-recommendation systems. A Deep Hierarchical Ensemble Network (DHEN) has been proposed to integrate multiple feature crossing modules and has achieved great success in CTR prediction. However, its performance for CVR prediction is unclear in the conversion ads setting, where an ad bids for the probability of a user's off-site actions on a third party website or app, including purchase, add to cart, sign up, etc. A few challenges in DHEN: 1) What feature-crossing modules (MLP, DCN, Transformer, to name a few) should be included in DHEN? 2) How deep and wide should DHEN be to achieve the best trade-off between efficiency and efficacy? 3) What hyper-parameters to choose in each feature-crossing module? Orthogonal to the model architecture, the input personalization features also significantly impact model performance with a high degree of freedom. In this paper, we attack this problem and present our contributions biased to the applied data science side, including: First, we propose a multitask learning framework with DHEN as the single backbone model architecture to predict all CVR tasks, with a detailed study on how to make DHEN work effectively in practice; Second, we build both on-site real-time user behavior sequences and off-site conversion event sequences for CVR prediction purposes, and conduct ablation study on its importance; Last but not least, we propose a self-supervised auxiliary loss to predict future actions in the input sequence, to help resolve the label sparseness issue in CVR prediction. Our method achieves state-of-the-art performance compared to previous single feature crossing modules with pre-trained user personalization features.