# A General Framework for Debiasing in CTR Prediction

arXiv: https://arxiv.org/abs/2112.02767 · HTML: https://mlsanity.com/paper/2112.02767/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 19 references that are in the map.
- First posted on arXiv 6 Dec 2021.

## Builds on (most cited first, 10 of 10)

- [Wide & Deep Learning for Recommender Systems](https://mlsanity.com/paper/1606.07792/) (2016)
- [DeepFM: A Factorization-Machine based Neural Network for CTR Prediction](https://mlsanity.com/paper/1703.04247/) (2017)
- [Deep Interest Network for Click-Through Rate Prediction](https://mlsanity.com/paper/1706.06978/) (2017)
- [xDeepFM: Combining Explicit and Implicit Feature Interactions for Recommender Systems](https://mlsanity.com/paper/1803.05170/) (2018)
- [Introducing LETOR 4.0 Datasets](https://mlsanity.com/paper/1306.2597/) (2013)
- [Optimized Cost per Click in Taobao Display Advertising](https://mlsanity.com/paper/1703.02091/) (2017)
- [Joint Optimization of Tree-based Index and Deep Model for Recommender Systems](https://mlsanity.com/paper/1902.07565/) (2019)
- [Improving Deep Learning For Airbnb Search](https://mlsanity.com/paper/2002.05515/) (2020)
- [Deep Position-wise Interaction Network for CTR Prediction](https://mlsanity.com/paper/2106.05482/) (2021)
- [R4: A Framework for Route Representation and Route Recommendation](https://mlsanity.com/paper/2110.10474/) (2021)

## Built on by (most cited first, 1 of 1)

- [Towards Disentangling Relevance and Bias in Unbiased Learning to Rank](https://mlsanity.com/paper/2212.13937/) (2022)

## Abstract (arXiv)

Most of the existing methods for debaising in click-through rate (CTR) prediction depend on an oversimplified assumption, i.e., the click probability is the product of observation probability and relevance probability. However, since there is a complicated interplay between these two probabilities, these methods cannot be applied to other scenarios, e.g. query auto completion (QAC) and route recommendation. We propose a general debiasing framework without simplifying the relationships between variables, which can handle all scenarios in CTR prediction. Simulation experiments show that: under the simplest scenario, our method maintains a similar AUC with the state-of-the-art methods; in other scenarios, our method achieves considerable improvements compared with existing methods. Meanwhile, in online experiments, the framework also gains significant improvements consistently.