label
int64 0
1
| user_id
int64 0
90.4k
| item_id
int64 1
90.4k
| tag_id
int64 2
69.1k
|
|---|---|---|---|
0 | 51,798 | 2,473 | 37,583 |
0 | 66,335 | 61,344 | 29,842 |
0 | 89,085 | 60,033 | 47,050 |
1 | 61,293 | 8,073 | 3,903 |
0 | 81,335 | 56,575 | 50,067 |
0 | 65,166 | 48,181 | 12,510 |
0 | 75,300 | 26,027 | 38,510 |
1 | 10,219 | 2,122 | 383 |
1 | 80,855 | 80,856 | 24,728 |
1 | 67,033 | 721 | 19,495 |
0 | 80,574 | 4,352 | 1,885 |
0 | 90,194 | 13,389 | 13,229 |
0 | 81,469 | 34,789 | 24,788 |
1 | 76,049 | 56,307 | 51,431 |
0 | 63,553 | 1,662 | 46,915 |
0 | 86,940 | 13,820 | 44,681 |
1 | 39,479 | 39,496 | 972 |
0 | 68,854 | 57,527 | 41,338 |
0 | 85,648 | 488 | 24,424 |
1 | 86,148 | 10,155 | 82 |
0 | 65,166 | 25,552 | 27,746 |
1 | 68,854 | 63,464 | 9,130 |
0 | 67,886 | 800 | 46,637 |
0 | 74,824 | 17,296 | 8,180 |
0 | 86,498 | 41,080 | 28,484 |
0 | 65,166 | 60,615 | 28,617 |
1 | 71,708 | 25,715 | 764 |
0 | 85,231 | 16,221 | 16,180 |
0 | 80,954 | 62,140 | 43,799 |
0 | 80,874 | 1,656 | 34,769 |
0 | 84,982 | 603 | 26,084 |
0 | 80,034 | 11,717 | 54,781 |
1 | 58,995 | 13,820 | 12,684 |
0 | 66,217 | 57,129 | 24,385 |
1 | 82,033 | 6,833 | 10,044 |
1 | 80,179 | 12,914 | 17,734 |
0 | 68,658 | 8,171 | 28,680 |
1 | 81,557 | 26,261 | 10,504 |
0 | 88,928 | 67,064 | 27,341 |
0 | 71,683 | 29,781 | 6,713 |
1 | 66,893 | 501 | 14,782 |
0 | 85,606 | 721 | 24,507 |
0 | 78,897 | 40,815 | 30,825 |
0 | 87,624 | 8,171 | 35,649 |
0 | 67,270 | 15,255 | 39,558 |
0 | 59,125 | 603 | 22,563 |
0 | 86,301 | 21,868 | 2,516 |
0 | 86,299 | 46,892 | 49,832 |
1 | 85,783 | 19,482 | 11,347 |
1 | 90,194 | 10,099 | 477 |
1 | 57,686 | 33,143 | 1,814 |
0 | 61,861 | 54,364 | 35,746 |
0 | 78,005 | 13,687 | 25,123 |
1 | 68,220 | 65,248 | 6,802 |
1 | 81,792 | 28,147 | 10,444 |
0 | 66,590 | 12,833 | 23,703 |
0 | 68,854 | 60,433 | 3,848 |
0 | 87,689 | 1,630 | 40,066 |
1 | 38,499 | 8,965 | 13,046 |
0 | 74,856 | 7,523 | 19,303 |
0 | 75,092 | 19,389 | 33,433 |
1 | 60,494 | 53,608 | 33,158 |
0 | 82,036 | 73,796 | 58,562 |
1 | 55,712 | 392 | 409 |
0 | 74,724 | 47,209 | 29,720 |
1 | 52,064 | 11,105 | 11,878 |
1 | 84,833 | 5,150 | 5,157 |
1 | 84,910 | 1,846 | 35,538 |
0 | 49,624 | 10,355 | 45,183 |
1 | 86,072 | 70,460 | 3,831 |
0 | 87,509 | 1,508 | 10,010 |
0 | 76,824 | 15,783 | 19,734 |
0 | 72,038 | 11,495 | 45,541 |
0 | 85,911 | 85,939 | 20,046 |
1 | 78,559 | 68,148 | 14,525 |
0 | 77,375 | 1,678 | 47,379 |
0 | 54,423 | 653 | 21,760 |
0 | 90,077 | 23,748 | 6,355 |
0 | 68,854 | 69,666 | 36,331 |
1 | 64,128 | 20,609 | 13,481 |
1 | 87,906 | 16,842 | 2,012 |
0 | 72,038 | 10,099 | 33,513 |
0 | 75,774 | 5,573 | 15,708 |
1 | 86,940 | 36,699 | 1,898 |
1 | 78,559 | 13,577 | 3,302 |
0 | 87,742 | 30,648 | 60,288 |
1 | 62,154 | 60,463 | 914 |
1 | 76,877 | 33,569 | 3,885 |
0 | 68,854 | 22,624 | 9,140 |
1 | 66,779 | 10,099 | 477 |
1 | 59,709 | 59,917 | 8,534 |
0 | 64,920 | 7,504 | 55,530 |
0 | 43,874 | 1,052 | 36,230 |
1 | 67,393 | 50,423 | 1,923 |
0 | 49,624 | 12,512 | 13,530 |
1 | 77,567 | 63,583 | 12,223 |
0 | 80,103 | 49,406 | 29,856 |
0 | 62,154 | 3,160 | 28,672 |
0 | 46,657 | 7,537 | 3,166 |
1 | 63,329 | 1,508 | 4,553 |
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MovielensLatest_x1
The MovieLens dataset consists of users' tagging records on movies. The task is formulated as personalized tag recommendation with each tagging record (user_id, item_id, tag_id) as an data instance. The target value denotes whether the user has assigned a particular tag to the movie. We provide the reusable, processed dataset released by the BARS benchmark, which are randomly split into 7:2:1 as the training set, validation set, and test set, respectively.
Dataset Details
Repository: https://github.com/reczoo/BARS/blob/main/datasets/MovieLens/README.md#movielenslatest_x1
Used by papers:
- Kelong Mao, Jieming Zhu, Liangcai Su, Guohao Cai, Yuru Li, Zhenhua Dong. FinalMLP: An Enhanced Two-Stream MLP Model for CTR Prediction. In AAAI 2023.
- Jieming Zhu, Qinglin Jia, Guohao Cai, Quanyu Dai, Jingjie Li, Zhenhua Dong, Ruiming Tang, Rui Zhang. FINAL: Factorized Interaction Layer for CTR Prediction. In SIGIR 2023.
- Weiyu Cheng, Yanyan Shen, Linpeng Huang. Adaptive Factorization Network: Learning Adaptive-Order Feature Interactions. In AAAI 2020.
Check the md5sum for data integrity:
```bash $ md5sum train.csv valid.csv test.csv efc8bceeaa0e895d566470fc99f3f271 train.csv e1930223a5026e910ed5a48687de8af1 valid.csv 54e8c6baff2e059fe067fb9b69e692d0 test.csv ```
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