# Efficient Click-Through Rate Prediction for Developing Countries via Tabular Learning

arXiv: https://arxiv.org/abs/2104.07553 · HTML: https://mlsanity.com/paper/2104.07553/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 44 references that are in the map.
- First posted on arXiv 15 Apr 2021.

## Builds on (most cited first, 17 of 17)

- [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 & Cross Network for Ad Click Predictions](https://mlsanity.com/paper/1708.05123/) (2017)
- [AutoInt: Automatic Feature Interaction Learning via Self-Attentive Neural Networks](https://mlsanity.com/paper/1810.11921/) (2018)
- [xDeepFM: Combining Explicit and Implicit Feature Interactions for Recommender Systems](https://mlsanity.com/paper/1803.05170/) (2018)
- [CatBoost: gradient boosting with categorical features support](https://mlsanity.com/paper/1810.11363/) (2018)
- [Neural Factorization Machines for Sparse Predictive Analytics](https://mlsanity.com/paper/1708.05027/) (2017)
- [Product-based Neural Networks for User Response Prediction](https://mlsanity.com/paper/1611.00144/) (2016)
- [Attentional Factorization Machines: Learning the Weight of Feature Interactions via Attention Networks](https://mlsanity.com/paper/1708.04617/) (2017)
- [MeLU: Meta-Learned User Preference Estimator for Cold-Start Recommendation](https://mlsanity.com/paper/1908.00413/) (2019)
- [Deep Neural Decision Trees](https://mlsanity.com/paper/1806.06988/) (2018)
- [Adaptive Factorization Network: Learning Adaptive-Order Feature Interactions](https://mlsanity.com/paper/1909.03276/) (2019)
- [Deep Forest](https://mlsanity.com/paper/1702.08835/) (2017)
- [Multi-Layered Gradient Boosting Decision Trees](https://mlsanity.com/paper/1806.00007/) (2018)
- [LambdaOpt: Learn to Regularize Recommender Models in Finer Levels](https://mlsanity.com/paper/1905.11596/) (2019)
- [Random Hinge Forest for Differentiable Learning](https://mlsanity.com/paper/1802.03882/) (2018)
- [Forward Thinking: Building Deep Random Forests](https://mlsanity.com/paper/1705.07366/) (2017)

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

- [On the Overlooked Significance of Underutilized Contextual Features in Recent News Recommendation Models](https://mlsanity.com/paper/2112.14370/) (2021)

## Abstract (arXiv)

Despite the rapid growth of online advertisement in developing countries, existing highly over-parameterized Click-Through Rate (CTR) prediction models are difficult to be deployed due to the limited computing resources. In this paper, by bridging the relationship between CTR prediction task and tabular learning, we present that tabular learning models are more efficient and effective in CTR prediction than over-parameterized CTR prediction models. Extensive experiments on eight public CTR prediction datasets show that tabular learning models outperform twelve state-of-the-art CTR prediction models. Furthermore, compared to over-parameterized CTR prediction models, tabular learning models can be fast trained without expensive computing resources including high-performance GPUs. Finally, through an A/B test on an actual online application, we show that tabular learning models improve not only offline performance but also the CTR of real users.