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# Avazu_x1
+ **Dataset description:**
This dataset contains about 10 days of labeled click-through data on mobile advertisements. It has 22 feature fields including user features and advertisement attributes. The preprocessed data are randomly split into 7:1:2\* as the training set, validation set, and test set, respectively.
The dataset statistics are summarized as follows:
| Dataset | Total | #Train | #Validation | #Test |
| :--------: | :-----: |:-----: | :----------: | :----: |
| Avazu_x1 | 40,428,967 | 28,300,276 | 4,042,897 | 8,085,794 |
+ **Source:** https://www.kaggle.com/c/avazu-ctr-prediction/data
+ **Download:** https://huggingface.co./datasets/reczoo/Avazu_x1/tree/main
+ **Repository:** https://github.com/reczoo/Datasets
+ **Used by papers:**
- Weiyu Cheng, Yanyan Shen, Linpeng Huang. [Adaptive Factorization Network: Learning Adaptive-Order Feature Interactions](https://ojs.aaai.org/index.php/AAAI/article/view/5768). In AAAI 2020.
- Kelong Mao, Jieming Zhu, Liangcai Su, Guohao Cai, Yuru Li, Zhenhua Dong. [FinalMLP: An Enhanced Two-Stream MLP Model for CTR Prediction](https://arxiv.org/abs/2304.00902). In AAAI 2023.
- Jieming Zhu, Qinglin Jia, Guohao Cai, Quanyu Dai, Jingjie Li, Zhenhua Dong, Ruiming Tang, Rui Zhang. [FINAL: Factorized Interaction Layer for CTR Prediction](https://dl.acm.org/doi/10.1145/3539618.3591988). In SIGIR 2023.
+ **Check the md5sum for data integrity:**
```bash
$ md5sum train.csv valid.csv test.csv
f1114a07aea9e996842c71648e0f6395 train.csv
d9568f246357d156c4b8030fadb8b623 valid.csv
9e2fe9c48705c9315ae7a0953eb57acf test.csv
```
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