Datasetpublic

reczoo/Avazu_x2

Avazu_x2 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 in the AutoGroup work, we randomly split 80% of the data for training and validation, and the remaining 20% for testing, respectively. For all categorical fields, we filter infrequent features by setting the threshold min_category_count=20 and replace… See the full description on the dataset page: https://huggingface.co/datasets/reczoo/Avazu_x2.

sourceHugging Faceupdated 3y agoView on Hugging Face

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Dataset Card

Avazu_x2

—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 in the AutoGroup work, we randomly split 80% of the data for training and validation, and the remaining 20% for testing, respectively. For all categorical fields, we filter infrequent features by setting the threshold mincategorycount=20 and replace them with a default `<OOV>` token.

The dataset statistics are summarized as follows:

Dataset	Total	#Train	#Validation	#Test
Avazu_x2	40,428,967	32,343,173		8,085,794

—Source: https://www.kaggle.com/c/avazu-ctr-prediction/data
—Download: https://huggingface.co/datasets/reczoo/Avazu_x2/tree/main
—Repository: https://github.com/reczoo/Datasets

—Used by papers:
—Bin Liu, Niannan Xue, Huifeng Guo, Ruiming Tang, Stefanos Zafeiriou, Xiuqiang He, Zhenguo Li. AutoGroup: Automatic Feature Grouping for Modelling Explicit High-Order Feature Interactions in CTR Prediction. In SIGIR 2020.

—Check the md5sum for data integrity:

bash

    $ md5sum train.csv test.csv
    c41d786896e2ebe68e08a022199f0ce8  train.csv
    e641ea94c72cdc99b49656d3404f536e  test.csv