{"url":"/dataset/criteo-live-traffic-data","name":"Criteo Attribution Modeling Dataset","full_name":"Criteo Attribution Modeling Dataset","description_markdown":"Content of this dataset\r\nThis dataset includes following files:\r\n\r\nREADME.md\r\ncriteo_attribution_dataset.tsv.gz: the dataset itself (623M compressed)\r\nExperiments.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.\r\nData description\r\nThis 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.\r\n\r\nHere is a detailed description of the fields (they are tab-separated in the file):\r\n\r\ntimestamp: timestamp of the impression (starting from 0 for the first impression). The dataset is sorted according to timestamp.\r\nuid a unique user identifier\r\ncampaign a unique identifier for the campaign\r\nconversion 1 if there was a conversion in the 30 days after the impression (independently of whether this impression was last click or not)\r\nconversion_timestamp the timestamp of the conversion or -1 if no conversion was observed\r\nconversion_id a unique identifier for each conversion (so that timelines can be reconstructed if needed). -1 if there was no conversion\r\nattribution 1 if the conversion was attributed to Criteo, 0 otherwise\r\nclick 1 if the impression was clicked, 0 otherwise\r\nclick_pos the position of the click before a conversion (0 for first-click)\r\nclick_nb number of clicks. More than 1 if there was several clicks before a conversion\r\ncost the price paid by Criteo for this display (disclaimer: not the real price, only a transformed version of it)\r\ncpo the cost-per-order in case of attributed conversion (disclaimer: not the real price, only a transformed version of it)\r\ntime_since_last_click the time since the last click (in s) for the given impression\r\ncat[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).\r\nKey figures\r\n2,4Gb uncompressed\r\n16.5M impressions\r\n45K conversions\r\n700 campaigns\r\nTasks\r\nThis dataset can be used in a large scope of applications related to Real-Time-Bidding, including but not limited to:\r\n\r\nAttribution modeling: rule based, model based, etc…\r\nConversion modeling in display advertising: the data includes cost and value used for computing Utility metrics.\r\nOffline metrics for real-time bidding","description_withheld":null,"homepage":"http://ailab.criteo.com/criteo-attribution-modeling-bidding-dataset/","introduced_date":"2017-07-20","introduced_date_note":null,"introduced_by":{"paper":"/paper/attribution-modeling-increases-efficiency-of","title":"Attribution Modeling Increases Efficiency of Bidding in Display Advertising","first_author":"Eustache Diemert","url":null},"license":{"name":"CC-BY-NC-SA","url":null},"modalities":[],"tasks":[],"languages":[],"variants":["Criteo Attribution Modeling Dataset"],"data_loaders":[],"num_papers_in_archive":1,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[],"papers_with_a_benchmark_row":[],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":0,"samples_harvested":0,"samples_ran":0,"samples_unverified":0,"pointer_only_for_licence":0,"papers_with_no_sample_that_ran":0,"note":"the per-paper counts above, summed; not a rate"},"papers_note":"The archive never published its papers-using-dataset list; these are papers with a leaderboard row on this dataset's benchmarks."}