add read_me and display histograms

README.md

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----
-license: cc-by-sa-4.0
----
+---
+license: cc-by-sa-4.0
+size_categories:
+- 10M<n<100M
+---
+# Dataset Documentation
+
+## Private Bidding Optimisation {#private-conversion-optimisation}
+
+The advertising industry lacks a common benchmark to assess the privacy
+/ utility trade-off in private advertising systems. To fill this gap, we
+are open-sourcing CriteoPrivateAd, the largest real-world anonymised
+bidding dataset, in terms of number of features. This dataset enables
+engineers and researchers to:
+
+-   assess the impact of removing cross-domain user signals,
+    highlighting the effects of third-party cookie deprecation;
+-   design and test private bidding optimisation approaches using
+    contextual signals and user features;
+-   evaluate the relevancy of answers provided by aggregation APIs for
+    bidding model learning.
+
+## Summary
+
+This dataset is released by Criteo to foster research and industrial
+innovation on privacy-preserving machine learning applied to a major
+advertising use-case, namely bid optimisation under user signal loss /
+obfuscation.
+
+This use-case is inspired by challenges both browser vendors and AdTech
+companies are facing due to third-party cookie deprecation, such as
+ensuring a viable cookie-less advertising business via a pragmatic
+performance / privacy trade-off. In particular, we are expecting to see
+improvements of Google Chrome Privacy Sandbox and Microsoft Ad Selection
+APIs via offline benchmarks based on this dataset.
+
+The dataset contains an anonymised log aiming to mimic production
+performance of AdTech bidding engines, so that offline results based on
+this dataset could be taken as ground truth to improve online
+advertising performance under privacy constraints. Features are grouped
+into several groups depending on their nature, envisioned privacy
+constraints and availability at inference time.
+
+Based on this dataset, the intended objective is to implement privacy
+constraints (e.g. by aggregating labels or by adding differential
+privacy to features and/or labels) and then learn click and conversion
+(e.g. sales) prediction models.
+
+The associated paper is available [here](https://arxiv.org/abs/2502.12103)
+
+As a leading AdTech company that drives commerce outcomes for media
+owners and marketers, Criteo is committed to evaluating proposals that
+might affect the way we will perform attribution, reporting and campaign
+optimisation in the future. Criteo has already participated in testing
+and providing feedback on browser proposals such as the Privacy Sandbox
+one; see all our [Medium articles](https://techblog.criteo.com) Back in 2021, we also
+organised a public challenge aiming to assess bidding performance when
+learning on aggregated data: our learnings are available [here](https://arxiv.org/abs/2201.13123).
+
+
+
+## Dataset Description
+
+
+This dataset represents a 100M anonymised sample of 30 days of Criteo
+live data retrieved from third-party cookie traffic on Chrome. Each line corresponds to one impression (a banner)
+that was displayed to a user. For each impression, we are providing:
+
+- campaign x publisher x (user x day) granularity with respective ids, to match Chrome Privacy Sandbox scenarios and both
+display and user-level privacy.
+
+-   4 labels (click, click leading to a landing on an advertiser
+    website, click leading to a visit on an advertiser website -
+    i.e. landing followed by one advertiser event, number of sales
+    attributed to the clicked display).
+
+-   more than 100 features grouped in 5 buckets with respect to their
+    logging and inference constraints in Protected Audience API from
+    Chrome Privacy Sandbox (note that these buckets are generic enough
+    to cover other private advertising frameworks as we are mainly
+    providing a split between ad campaign features, single-domain &
+    cross-domain user features, and contextual features) :
+
+    -   Features available in the key-value server with 12-bit logging
+        constraint (i.e. derived from current version of modelingSignals
+        and standing for single-domain user features).
+    -   Features available in the key-value server with no logging
+        constraint (i.e. derived from Interest Group name / renderURL).
+    -   Features available in browser with 12-bit constraint
+        (i.e. cross-domain features available in generateBid).
+    -   Features from contextual call with no logging constraint
+        (i.e. contextual features).
+    -   Features not available (i.e. cross-device and cross-domain
+        ones).
+
+-   `day_int` enabling (1) splitting the log into training, validation
+    and testing sets; (2) performing relevant model seeding.
+
+-   Information about conversion delay to simulate the way Privacy Sandbox APIs are working.
+
+-   `time_between_request_timestamp_and_post_display_event` (column name
+    in clear): time delta (in minutes) between the request timestamp and the
+    click or sale event. All displays are considered starting the day of
+    the event at 00:00 to avoid providing complete timelines.
+
+-   We include a display order from 1 to K for display on the same day
+    for the same user.
+
+CriteoPrivateAd is split into 30 parquets (one per day from 1 to 30) in day_int={i} directory.
+The displays-per-user histograms can be deduced from event_per_user_contribution.csv,< it is useful to build importance sampling ratios and user-level DP. 
+Please, see the companion paper for more details.
+
+
+A precise description of the dataset and each column is available in [the
+companion paper](https://arxiv.org/abs/2502.12103)
+
+
+
+## Metrics
+
+The metrics best suited to the click and conversion estimation problems
+are:
+
+-   the log-likelihood (LLH), and preferably a rescaled version named LLH-CompVN defined
+    as the relative log-likelihood uplift compared to the naive model
+    always predicting the average label in the training dataset;
+-   calibration, defined as the ratio between the sum of the predictions
+    and the sum of the validation labels. It must be close to 1 for a
+    bidding application;
+
+We would like to point out that conventional classification measures
+such as area under the curve (AUC) are less relevant for comparing
+auction models.
+
+The click-through rate is higher than the one encountered in real-world
+advertising systems on the open internet. To design realistic bidding
+applications, one must use a weighted loss for validation. We defer the
+interested readers to the [associated companion paper](https://arxiv.org/abs/2502.12103) for more details
+
+## Baselines
+
+The Training period has been fixed to 1->25 and Validation period to 26->30. The chosen loss is the LLH-CompVN with weighting as defined above. The Sales | Display is a product of the Landed Click | Display and the Sales | Landed Click. 
+
+| Task/CTR                | 0.1%  | 0.5%  | 1%    |
+|-------------------------|-------|-------|-------|
+| Landed Click \| Display  | 0.170 | 0.186 | 0.234 |
+| Sales \| Landed Click   | 0.218 | 0.218 | 0.218 |
+| Sales \| Display        | 0.171 | 0.187 | 0.237 |
+
+Note that our baseline results might be difficult to achieve because of the anonymisation of the dataset.
+
+## License
+
+The data is released under the  license. You are free to
+Share and Adapt this data provided that you respect the Attribution and
+ShareAlike conditions. Please read carefully the full license before
+using.
+
+## Citation
+
+If you use the dataset in your research please cite it using the
+following Bibtex excerpt:
+
+@misc{sebbar2025criteoprivateadrealworldbiddingdataset,
+      title={CriteoPrivateAd: A Real-World Bidding Dataset to Design Private Advertising Systems}, 
+      author={Mehdi Sebbar and Corentin Odic and Mathieu Léchine and Aloïs Bissuel and Nicolas Chrysanthos and Anthony D'Amato and Alexandre Gilotte and Fabian Höring and Sarah Nogueira and Maxime Vono},
+      year={2025},
+      eprint={2502.12103},
+      archivePrefix={arXiv},
+      primaryClass={cs.CR},
+      url={https://arxiv.org/abs/2502.12103}, 
+}
+
+
+## Acknowledgment
+
+We would like to thank:
+
+-   Google Chrome Privacy Sandbox team, especially Charlie Harrisson,
+    for feedbacks on the usefulness of this dataset.
+-   W3C PATCG group, notably for their public data requests to foster
+    work on the future of attribution and reporting.
+-   Criteo stakeholders who took part of this dataset release: Anthony
+    D'Amato, Mathieu Léchine, Mehdi Sebbar, Corentin Odic, Maxime Vono,
+    Camille Jandot, Fatma Moalla, Nicolas Chrysanthos, Romain Lerallut,
+    Alexandre Gilotte, Aloïs Bissuel, Lionel Basdevant, Henry Jantet.

event_per_user_correction.csv

@@ -0,0 +1,21 @@
+cnt_displays;nb_users_CriteoPrivateAd;nb_users_original;sampling_weight
+1;81.59017519438582;31.461234309327686;0.00018312306581380486
+2;12.627252670118407;16.94631036619179;0.000637340393948341
+3;3.3731253041235503;11.037908922733743;0.001554030330413786
+4;1.2091565351024356;8.042709640813122;0.003158821037283753
+5;0.5263210799756821;6.192651422222794;0.0055876736015271124
+6;0.26267937088171844;4.817314665760953;0.008709319179265464
+7;0.1452702128816801;3.7343443553809625;0.012207955829026505
+8;0.0859987504408444;3.0199377298955095;0.016676734172371088
+9;0.05417405693062993;2.4569364892295944;0.021538116890158193
+10;0.0357337866530729;2.048885560260378;0.027229760448449734
+11;0.02422468916735485;1.7184218500846973;0.033688116639026405
+12;0.01686884080816877;1.512096206268133;0.04256957781762428
+13;0.011723383322452808;1.2980893644479816;0.05258438334231591
+14;0.00835714763265579;1.1345208344752733;0.0644702806246388
+15;0.00623896460408367;0.9944869496366027;0.07569927598273567
+16;0.004649353306123967;0.8766500878134798;0.08954449470814815
+17;0.0034337681959194883;0.7780990377271169;0.1076140716491054
+18;0.00268961192653149;0.712301325826477;0.1257706455148872
+19;0.00211818302857212;0.6379885420653203;0.14303902927982048
+20;0.0016415593795923728;0.5791123398383868;0.1675372494934399