Criteo_x4
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 setting with the AutoInt work, we randomly split the data into 8:1:1 as the training set, validation set, and test set, respectively.
The dataset statistics are summarized as follows:
Dataset Split Total #Train #Validation #Test Criteo_x4 45,840,617 36,672,493 4,584,062 4,584,062 Criteo_x4_001
In this setting, we follow the winner's solution of the Criteo challenge to discretize each integer value x to ⌊log2(x)⌋, if x > 2; and x = 1 otherwise. For all categorical fields, we replace infrequent features with a default
<OOV>token by setting the threshold min_category_count=10. Note that we do not follow the exact preprocessing steps in AutoInt, because this preprocessing performs much better. We fix embedding_dim=16 as with AutoInt.Criteo_x4_002
In this setting, we follow the winner's solution of the Criteo challenge to discretize each integer value x to ⌊log2(x)⌋, if x > 2; and x = 1 otherwise. For all categorical fields, we replace infrequent features with a default
<OOV>token by setting the threshold min_category_count=2. We fix embedding_dim=40 in this setting.
Source: https://www.kaggle.com/c/criteo-display-ad-challenge/data
Download: https://huggingface.co/datasets/reczoo/Criteo_x4/tree/main
RecZoo Datasets: https://github.com/reczoo/Datasets
Used by papers:
- Weiping Song, Chence Shi, Zhiping Xiao, Zhijian Duan, Yewen Xu, Ming Zhang, Jian Tang. AutoInt: Automatic Feature Interaction Learning via Self-Attentive Neural Networks. In CIKM 2019.
- Jieming Zhu, Jinyang Liu, Shuai Yang, Qi Zhang, Xiuqiang He. BARS-CTR: Open Benchmarking for Click-Through Rate Prediction. In CIKM 2021.
Check the md5sum for data integrity:
$ md5sum train.csv valid.csv test.csv 4a53bb7cbc0e4ee25f9d6a73ed824b1a train.csv fba5428b22895016e790e2dec623cb56 valid.csv cfc37da0d75c4d2d8778e76997df2976 test.csv