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criteo
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criteo-attribution-dataset

Tasks:
Tabular Classification
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csv
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10M - 100M
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criteo
advertising
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metadata
license: cc-by-nc-sa-4.0
tags:
  - criteo
  - advertising
pretty_name: criteo-attribution-dataset
size_categories:
  - 10M<n<100M

Criteo Attribution Modeling for Bidding Dataset

This dataset is released along with the paper:

"Attribution Modeling Increases Efficiency of Bidding in Display Advertising" Eustache Diemert*, Julien Meynet* (Criteo Research), Damien Lefortier (Facebook), Pierre Galland (Criteo) *authors contributed equally

This work was published in: 2017 AdKDD & TargetAd Workshop, in conjunction with The 23rd ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2017) (https://adkdd17.wixsite.com/adkddtargetad2017)

When using this dataset, please cite the paper with following bibtex(final ACM bibtex coming soon):

@inproceedings{DiemertMeynet2017,
author = {{Diemert Eustache, Meynet Julien} and Galland, Pierre and Lefortier, Damien},
title={Attribution Modeling Increases Efficiency of Bidding in Display Advertising},
publisher = {ACM},
pages={To appear},
booktitle = {Proceedings of the AdKDD and TargetAd Workshop, KDD, Halifax, NS, Canada, August, 14, 2017},
year = {2017}
}

We would love to hear from you if use this data or plan to use it. Refer to the Contact section below.

Content of this dataset

This dataset includes following files:

  • this README.md
  • criteo_attribution_dataset.tsv.gz: the dataset itself (623M compressed)
  • Experiments.ipynb: ipython notebook with code and utilities to reproduce the results in the paper. Can also be used as a starting point for further research on this data. It requires python 3.* and standard scientific libraries such as pandas, numpy and sklearn.

Data description

This dataset represent a sample of 30 days of Criteo live traffic data. Each line corresponds to one impression (a banner) that was displayed to a user. For each banner we have detailed information about the context, if it was clicked, if it led to a conversion and if it led to a conversion that was attributed to Criteo or not. Data has been sub-sampled and anonymized so as not to disclose proprietary elements.

Here is a detailed description of the fields (they are tab-separated in the file):

  • timestamp: timestamp of the impression (starting from 0 for the first impression). The dataset is sorted according to timestamp.
  • uid a unique user identifier
  • campaign a unique identifier for the campaign
  • conversion 1 if there was a conversion in the 30 days after the impression (independently of whether this impression was last click or not)
  • conversion_timestamp the timestamp of the conversion or -1 if no conversion was observed
  • conversion_id a unique identifier for each conversion (so that timelines can be reconstructed if needed). -1 if there was no conversion
  • attribution 1 if the conversion was attributed to Criteo, 0 otherwise
  • click 1 if the impression was clicked, 0 otherwise
  • click_pos the position of the click before a conversion (0 for first-click)
  • click_nb number of clicks. More than 1 if there was several clicks before a conversion
  • cost the price paid by Criteo for this display (disclaimer: not the real price, only a transformed version of it)
  • **cpo** the cost-per-order  in case of attributed conversion (**disclaimer:** not the real price, only a transformed version of it)
  • time_since_last_click the time since the last click (in s) for the given impression
  • **cat[1-9]** contextual features associated to the display. Can be used to learn the click/conversion models. We do not disclose the meaning of these features but it is not relevant for this study. Each column is a categorical variable. In the experiments, they are mapped to a fixed dimensionality space using the Hashing Trick (see paper for reference).

Key figures

  • 2,4Gb uncompressed
  • 16.5M impressions
  • 45K conversions
  • 700 campaigns

Tasks

This dataset can be used in a large scope of applications related to Real-Time-Bidding, including but not limited to:

  • Attribution modeling: rule based, model based, etc...
  • Conversion modeling in display advertising: the data includes cost and value used for computing Utility metrics.
  • Offline metrics for real-time bidding

Contact

For any question, feel free to contact: