Datasets:

reczoo
/

Criteo_x4

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0	null	0	null	0	2,885	null	0	49	85	null	0	null	24	68fd1e64	3df44d94	ab8d144a	58ed2ff8	4cf72387	7e0ccccf	7bd17596	1f89b562	a73ee510	3b08e48b	90e43499	915df586	ac6687f6	07d13a8f	4dd467c3	fa164cc9	776ce399	a0f3a3dc	null	null	c4ae6950	null	93bad2c0	a7e54c7b	null	null
1	3	0	null	null	191	8	5	5	15	1	2	null	null	05db9164	78ccd99e	d0c01507	62b4602e	0942e0a7	6f6d9be8	dde8fbe1	51d76abe	a73ee510	82ab7978	4c14b9bc	eebf94aa	85fcb68e	051219e6	9917ad07	5292ab7e	e5ba7672	e7e991cb	f44bef3c	a458ea53	996f4cd8	null	32c7478e	fdd7a5a0	9d93af03	7833e4df
0	1	82	2	3	440	24	201	31	370	1	11	0	3	68fd1e64	403ea497	2cbec47f	3e2bfbda	25c83c98	fe6b92e5	2903ead3	062b5529	a73ee510	baa1d4df	a0a5e9d7	21a23bfe	ee79db7b	07d13a8f	e3209fc2	587267a3	27c07bd6	a78bd508	21ddcdc9	5840adea	c2a93b37	null	423fab69	1793a828	e8b83407	2fede552
0	0	2	null	null	2,977	98	2	6	50	0	1	null	null	05db9164	52d631d9	d032c263	c18be181	25c83c98	fbad5c96	ce4f7f55	0b153874	a73ee510	ebb8102f	38f692a7	dfbb09fb	6e5da64f	64c94865	35e4d583	84898b2a	07c540c4	9943b99f	null	null	0014c32a	null	32c7478e	3b183c5c	null	null
0	0	1	null	null	2,969	126	3	27	77	0	1	null	null	ae82ea21	d833535f	77f2f2e5	d16679b9	25c83c98	fbad5c96	3668fc4e	0b153874	a73ee510	b78b25b1	23f20700	9f32b866	59be1107	b28479f6	a66dcf27	31ca40b6	07c540c4	7b49e3d2	null	null	dfcfc3fa	null	3a171ecb	aee52b6f	null	null
0	null	4	9	17	13,032	663	20	37	1,623	null	5	null	36	be589b51	0a519c5c	b00d1501	d16679b9	f281d2a7	7e0ccccf	b72ec13d	0b153874	a73ee510	b1e27fa4	3547565f	e0d76380	12880350	b28479f6	7f6af6b0	1203a270	e5ba7672	eea3ab97	null	null	73d06dde	null	dbb486d7	aee52b6f	null	null
0	null	1	null	null	1,628	127	7	0	68	null	1	0	null	68fd1e64	38a947a1	null	null	25c83c98	7e0ccccf	11401ad8	0b153874	a73ee510	1ce1e29d	4a1677bb	null	ab2497dc	64c94865	40a510d5	null	8efede7f	5e8bcf8b	null	null	null	null	32c7478e	null	null	null
0	0	1	452	37	4,616	352	194	35	37	0	1	1	37	68fd1e64	c5c1d6ae	5ad42896	8820c221	25c83c98	7e0ccccf	63985851	0b153874	a73ee510	58f020bc	5874c9c9	7b29edcb	740c210d	b28479f6	29a18ba0	3948d462	3486227d	836a67dd	21ddcdc9	5840adea	b56923bf	null	32c7478e	218c9ff7	7a402766	b1966909
0	0	121	2	3	32,425	null	0	8	1	0	0	0	3	68fd1e64	d833535f	b00d1501	d16679b9	25c83c98	7e0ccccf	3aaa5b43	0b153874	a73ee510	3b08e48b	5298a972	e0d76380	96fa211f	1adce6ef	2ee9f086	1203a270	3486227d	7b49e3d2	null	null	73d06dde	null	3a171ecb	aee52b6f	null	null
0	null	3	null	2	9,465	54	3	4	43	null	2	null	2	5a9ed9b0	e112a9de	4e1c9eda	22504558	307e775a	fe6b92e5	ce4f7f55	c8ddd494	a73ee510	2ec4dbbb	38f692a7	23bc90a1	6e5da64f	1adce6ef	11da3cff	776f5665	07c540c4	a7cf409e	null	null	5a5953a2	null	32c7478e	8f079aa5	null	null
0	null	0	11	32	9,515	356	44	41	890	null	4	null	32	05db9164	09e68b86	6f7b7ab4	2afc9b85	25c83c98	13718bbd	d385ea68	0b153874	a73ee510	3b08e48b	7940fc2a	642e0279	00e20e7b	07d13a8f	36721ddc	f16e21bb	e5ba7672	5aed7436	45d6674c	a458ea53	b3dd1dd5	null	32c7478e	3fdb382b	e8b83407	c5686cdc
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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 #Validation #Test

Criteo_x4 45,840,617 36,672,493 4,584,062 4,584,062
- Criteo_x4_001
  
  In this setting, we follow the winner's solution of the Criteo challenge to discretize each integer value x to ⌊log2(x)⌋, if x > 2; and x = 1 otherwise. For all categorical fields, we replace infrequent features with a default <OOV> token by setting the threshold min_category_count=10. Note that we do not follow the exact preprocessing steps in AutoInt, because this preprocessing performs much better. We fix embedding_dim=16 as with AutoInt.
- Criteo_x4_002
  
  In this setting, we follow the winner's solution of the Criteo challenge to discretize each integer value x to ⌊log2(x)⌋, if x > 2; and x = 1 otherwise. For all categorical fields, we replace infrequent features with a default <OOV> token by setting the threshold min_category_count=2. We fix embedding_dim=40 in this setting.
Source: https://www.kaggle.com/c/criteo-display-ad-challenge/data
Download: https://huggingface.co/datasets/reczoo/Criteo_x4/tree/main
RecZoo Datasets: https://github.com/reczoo/Datasets
Used by papers:
- Weiping Song, Chence Shi, Zhiping Xiao, Zhijian Duan, Yewen Xu, Ming Zhang, Jian Tang. AutoInt: Automatic Feature Interaction Learning via Self-Attentive Neural Networks. In CIKM 2019.
- Jieming Zhu, Jinyang Liu, Shuai Yang, Qi Zhang, Xiuqiang He. BARS-CTR: Open Benchmarking for Click-Through Rate Prediction. In CIKM 2021.

Dataset Split	Total	#Train	#Validation	#Test
Criteo_x4	45,840,617	36,672,493	4,584,062	4,584,062

Check the md5sum for data integrity:

$ md5sum train.csv valid.csv test.csv
4a53bb7cbc0e4ee25f9d6a73ed824b1a  train.csv
fba5428b22895016e790e2dec623cb56  valid.csv
cfc37da0d75c4d2d8778e76997df2976  test.csv

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Size of downloaded dataset files:

4.05 GB

Size of the auto-converted Parquet files:

5.91 GB

Number of rows:

45,840,617