Criteo_x1
Dataset description:
The Criteo dataset is a widely-used benchmark dataset for CTR prediction, which contains about one week of click-through data for display advertising. It has 13 numerical feature fields and 26 categorical feature fields. Following the AFN work, we randomly split the data into 7:2:1* as the training set, validation set, and test set, respectively.
The dataset statistics are summarized as follows:
Dataset Split Total #Train #Validation #Test Criteo_x1 45,840,617 33,003,326 8,250,124 4,587,167 Source: https://www.kaggle.com/c/criteo-display-ad-challenge/data
Download: https://huggingface.co/datasets/reczoo/Criteo_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. 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. 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. In SIGIR 2023.
Check the md5sum for data integrity:
$ md5sum train.csv valid.csv test.csv 30b89c1c7213013b92df52ec44f52dc5 train.csv f73c71fb3c4f66b6ebdfa032646bea72 valid.csv 2c48b26e84c04a69b948082edae46f8c test.csv