| # 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](https://arxiv.org/abs/1810.11921), 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](https://arxiv.org/abs/1810.11921). In CIKM 2019. | |
| - Jieming Zhu, Jinyang Liu, Shuai Yang, Qi Zhang, Xiuqiang He. [BARS-CTR: Open Benchmarking for Click-Through Rate Prediction](https://arxiv.org/abs/2009.05794). In CIKM 2021. | |
| + **Check the md5sum for data integrity:** | |
| ```bash | |
| $ md5sum train.csv valid.csv test.csv | |
| 4a53bb7cbc0e4ee25f9d6a73ed824b1a train.csv | |
| fba5428b22895016e790e2dec623cb56 valid.csv | |
| cfc37da0d75c4d2d8778e76997df2976 test.csv | |
| ``` | |