Update README.md

README.md

@@ -1,3 +1,85 @@
 ---
 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&ast;, Julien Meynet&ast; (Criteo Research), Damien Lefortier (Facebook), Pierre Galland (Criteo) &ast;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):
+
+```json
+@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:
+ 
+ * The authors of the paper directly (emails in the paper)
+ * Criteo Research team: http://ailab.criteo.com/contact-us/
+ * Criteo Research twitter account: @CriteoResearch