datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
criteo-attribution-dataset
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
2017 AdKDD & TargetAd Workshop, in conjunction with The 23rd ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2017)
When using this dataset, please cite the paper… See the full description on the dataset page: https://huggingface.co/datasets/criteo/criteo-attribution-dataset.criteo-uplift
Introduction
This dataset is released along with the paper:
A Large Scale Benchmark for Uplift Modeling
Eustache Diemert, Artem Betlei, Christophe Renaudin; (Criteo AI Lab), Massih-Reza Amini (LIG, Grenoble INP)
This work was published in: AdKDD 2018 Workshop, in conjunction with KDD 2018.
When using this dataset, please cite the paper with following bibtex:
@inproceedings{Diemert2018,
author = {{Diemert Eustache, Betlei Artem} and Renaudin, Christophe and Massih-Reza, Amini}… See the full description on the dataset page: https://huggingface.co/datasets/criteo/criteo-uplift.Criteo_x1
Criteo_x1
Dataset description:
The Criteo dataset is a widely-used benchmark dataset for CTR prediction, which contains about one week of click-through data for display advertising. It has 13 numerical feature fields and 26 categorical feature fields. Following the AFN work, we randomly split the data into 7:2:1* as the training set, validation set, and test set, respectively.
The dataset statistics are summarized as follows:
Dataset Split
Total
#Train
#Validation
#Test… See the full description on the dataset page: https://huggingface.co/datasets/reczoo/Criteo_x1.Criteo_x4
Criteo_x4
Dataset description:
The Criteo dataset is a widely-used benchmark dataset for CTR prediction, which contains about one week of click-through data for display advertising. It has 13 numerical feature fields and 26 categorical feature fields. Following the setting with the AutoInt work, we randomly split the data into 8:1:1 as the training set, validation set, and test set, respectively.
The dataset statistics are summarized as follows:
Dataset Split
Total
#Train… See the full description on the dataset page: https://huggingface.co/datasets/reczoo/Criteo_x4.Criteo_x2
Criteo_x2
Dataset description:
This dataset employs the Criteo 1TB Click Logs for display advertising, which contains one month of click-through data with billions of data samples. Following the same setting with the AutoGroup work, we select "data 6-12" as the training set while using "day-13" for testing. To reduce label imbalance, we perform negative sub-sampling to keep the positive ratio roughly at 50%. It has 13 numerical feature fields and 26 categorical feature fields. In… See the full description on the dataset page: https://huggingface.co/datasets/reczoo/Criteo_x2.FairJob
FairJob: A Real-World Dataset for Fairness in Online Systems
Summary
This dataset is released by Criteo to foster research and innovation on Fairness in Advertising and AI systems in general.
See also Criteo pledge for Fairness in Advertising.
The dataset is intended to learn click predictions models and evaluate by how much their predictions are biased between different gender groups.
The associated paper is available at Vladimirova et al. 2024.
License… See the full description on the dataset page: https://huggingface.co/datasets/criteo/FairJob.criteo-uplift
Introduction
This dataset is released along with the paper:
A Large Scale Benchmark for Uplift Modeling
Eustache Diemert, Artem Betlei, Christophe Renaudin; (Criteo AI Lab), Massih-Reza Amini (LIG, Grenoble INP)
This work was published in: AdKDD 2018 Workshop, in conjunction with KDD 2018.
When using this dataset, please cite the paper with following bibtex:
@inproceedings{Diemert2018,
author = {{Diemert Eustache, Betlei Artem} and Renaudin, Christophe and Massih-Reza, Amini}… See the full description on the dataset page: https://huggingface.co/datasets/fizzedith/criteo-uplift.criteo-attribution-dataset
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
2017 AdKDD & TargetAd Workshop, in conjunction with The 23rd ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2017)
When using this dataset, please cite the… See the full description on the dataset page: https://huggingface.co/datasets/soukatroy/criteo-attribution-dataset.criteo-uplift
Introduction
This dataset is released along with the paper:
A Large Scale Benchmark for Uplift Modeling
Eustache Diemert, Artem Betlei, Christophe Renaudin; (Criteo AI Lab), Massih-Reza Amini (LIG, Grenoble INP)
This work was published in: AdKDD 2018 Workshop, in conjunction with KDD 2018.
When using this dataset, please cite the paper with following bibtex:
@inproceedings{Diemert2018,
author = {{Diemert Eustache, Betlei Artem} and Renaudin, Christophe and Massih-Reza, Amini}… See the full description on the dataset page: https://huggingface.co/datasets/spanchom/criteo-uplift.