# XCrossNet: Feature Structure-Oriented Learning for Click-Through Rate Prediction

arXiv: https://arxiv.org/abs/2104.10907 · HTML: https://mlsanity.com/paper/2104.10907/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 27 references that are in the map.
- First posted on arXiv 22 Apr 2021.

## Builds on (most cited first, 13 of 13)

- [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)
- [xDeepFM: Combining Explicit and Implicit Feature Interactions for Recommender Systems](https://mlsanity.com/paper/1803.05170/) (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)
- [Deep Learning over Multi-field Categorical Data: A Case Study on User Response Prediction](https://mlsanity.com/paper/1601.02376/) (2016)
- [Higher-Order Factorization Machines](https://mlsanity.com/paper/1607.07195/) (2016)
- [Product-based Neural Networks for User Response Prediction over Multi-field Categorical Data](https://mlsanity.com/paper/1807.00311/) (2018)
- [AutoCross: Automatic Feature Crossing for Tabular Data in Real-World Applications](https://mlsanity.com/paper/1904.12857/) (2019)
- [Feature Generation by Convolutional Neural Network for Click-Through Rate Prediction](https://mlsanity.com/paper/1904.04447/) (2019)
- [Developing a Recommendation Benchmark for MLPerf Training and Inference](https://mlsanity.com/paper/2003.07336/) (2020)

## Built on by (most cited first, 6 of 6)

- [Diffusion-based Multi-modal Synergy Interest Network for Click-through Rate Prediction](https://mlsanity.com/paper/2508.21460/) (2025)
- [Evolution-based Feature Selection for Predicting Dissolved Oxygen Concentrations in Lakes](https://mlsanity.com/paper/2403.18923/) (2024)
- [Contrastive Learning for Implicit Social Factors in Social Media Popularity Prediction](https://mlsanity.com/paper/2410.09345/) (2024)
- [Cognitive Evolutionary Learning to Select Feature Interactions for Recommender Systems](https://mlsanity.com/paper/2405.18708/) (2024)
- [Breaker: Removing Shortcut Cues with User Clustering for Single-slot Recommendation System](https://mlsanity.com/paper/2506.00828/) (2025)
- [Distribution-Aware End-to-End Embedding for Streaming Numerical Features in Click-Through Rate Prediction](https://mlsanity.com/paper/2602.03223/) (2026)

## Abstract (arXiv)

Click-Through Rate (CTR) prediction is a core task in nowadays commercial recommender systems. Feature crossing, as the mainline of research on CTR prediction, has shown a promising way to enhance predictive performance. Even though various models are able to learn feature interactions without manual feature engineering, they rarely attempt to individually learn representations for different feature structures. In particular, they mainly focus on the modeling of cross sparse features but neglect to specifically represent cross dense features. Motivated by this, we propose a novel Extreme Cross Network, abbreviated XCrossNet, which aims at learning dense and sparse feature interactions in an explicit manner. XCrossNet as a feature structure-oriented model leads to a more expressive representation and a more precise CTR prediction, which is not only explicit and interpretable, but also time-efficient and easy to implement. Experimental studies on Criteo Kaggle dataset show significant improvement of XCrossNet over state-of-the-art models on both effectiveness and efficiency.