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