Criteo Attribution Modeling Dataset

Content of this dataset This dataset includes following files:

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 represents 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