Create README.md

README.md

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+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. We provide the reusable, processed dataset released by [the BARS benchmark](https://openbenchmark.github.io), which are randomly split into 7:2:1 as the training set, validation set, and test set, respectively.
+
+### Dataset Details
+
++ **Repository:** https://github.com/reczoo/BARS/tree/main/datasets/Criteo#criteo_x1
+
++ **Used by papers:** 
+    - 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.
+    - 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.
+
++ **Check the md5sum for data integrity:**
+    ```bash
+    $ md5sum train.csv valid.csv test.csv
+    30b89c1c7213013b92df52ec44f52dc5  train.csv
+    f73c71fb3c4f66b6ebdfa032646bea72  valid.csv
+    2c48b26e84c04a69b948082edae46f8c  test.csv
+    ```