Avazu_x4

Dataset description:

This dataset contains about 10 days of labeled click-through data on mobile advertisements. It has 22 feature fields including user features and advertisement attributes. Following the same setting with the AutoInt work, we split the data randomly into 8:1:1 as the training set, validation set, and test set, respectively.

The dataset statistics are summarized as follows:

Dataset Total #Train #Validation #Test

Avazu_x4 40,428,967 32,343,172 4,042,897 4,042,898
- Avazu_x4_001
  
  In this setting, we preprocess the data split by removing the id field that is useless for CTR prediction. In addition, we transform the timestamp field into three fields: hour, weekday, and is_weekend. For all categorical fields, we filter infrequent features by setting the threshold min_category_count=2 (performs well) and replace them with a default <OOV> token. Note that we do not follow the exact preprocessing steps in AutoInt, because the authors neither remove the useless id field nor specially preprocess the timestamp field. We fix embedding_dim=16 following the existing AutoInt work.
- Avazu_x4_002
  
  In this setting, we preprocess the data split by removing the id field that is useless for CTR prediction. In addition, we transform the timestamp field into three fields: hour, weekday, and is_weekend. For all categorical fields, we filter infrequent features by setting the threshold min_category_count=1 and replace them with a default <OOV> token. Note that we found that min_category_count=1 performs the best, which is surprising. We fix embedding_dim=40 following the existing FGCNN work.
Source: https://www.kaggle.com/c/avazu-ctr-prediction/data
Download: https://huggingface.co/datasets/reczoo/Avazu_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.

Dataset	Total	#Train	#Validation	#Test
Avazu_x4	40,428,967	32,343,172	4,042,897	4,042,898

Check the md5sum for data integrity:

$ md5sum train.csv valid.csv test.csv
de3a27264cdabf66adf09df82328ccaa  train.csv
33232931d84d6452d3f956e936cab2c9  valid.csv
3ebb774a9ca74d05919b84a3d402986d  test.csv