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1
6
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2688
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2700
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435
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440
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236
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32
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79132
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30749
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30749
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3581
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30707
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1673
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5026
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2082
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72641
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82854
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69134
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66934
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1356
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135
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135
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95
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1704
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1888
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356
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2605
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1721
13 2278:3.0:995950197 2762:4.0:995950455 1036:4.0:995950482 1240:4.0:995950482 3147:3.0:995950482 1610:4.0:995950498 3551:4.0:995950530 1674:3.0:995950565 3101:4.0:995950603 4661:3.0:995950633 733:3.0:995950673 225:4.0:995950702 1608:4.0:995950737 3755:3.0:995950737 912:4.0:995950802 969:4.0:995950802 920:4.0:995950834...
4041
13 527:4.0:995949588 2858:4.0:995949605 1617:3.0:995949624 1233:4.0:995949644 296:2.0:995949665 110:5.0:995949681 589:5.0:995949695 457:4.0:995949753 1625:3.0:995949753 1704:5.0:995949753 539:4.0:995949770 1266:2.0:995949811 2571:2.0:995949811 3499:3.0:995949853 1500:3.0:995949882 2268:5.0:995949882 3105:4.0:995949882 ...
3252
13 318:5.0:995949588 527:4.0:995949588 2858:4.0:995949605 1617:3.0:995949624 1233:4.0:995949644 296:2.0:995949665 110:5.0:995949681 589:5.0:995949695 457:4.0:995949753 1625:3.0:995949753 1704:5.0:995949753 539:4.0:995949770 1266:2.0:995949811 2571:2.0:995949811 3499:3.0:995949853 1500:3.0:995949882 2268:5.0:995949882 3...
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13 1617:3.0:995949624 1233:4.0:995949644 296:2.0:995949665 110:5.0:995949681 589:5.0:995949695 457:4.0:995949753 1625:3.0:995949753 1704:5.0:995949753 539:4.0:995949770 1266:2.0:995949811 2571:2.0:995949811 3499:3.0:995949853 1500:3.0:995949882 2268:5.0:995949882 3105:4.0:995949882 161:4.0:995949913 2359:4.0:995949913 ...
2278
14 7706:5.0:1336852131 4816:5.0:1336852142 8994:5.0:1336852158 8757:5.0:1336852170 4510:5.0:1336852196
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15 367:1.5:1289857804 500:4.0:1289857820 5349:3.0:1289857828 377:3.5:1289857838 5989:4.0:1289857844 56367:4.5:1289857869 68157:3.5:1289857887 597:4.0:1289857891
6711
15 5970:4.5:1289839127 56171:3.0:1289839271 3079:4.0:1289839359 1595:0.5:1289839471 5668:4.0:1289839498 80969:4.5:1289839939 79132:4.0:1289840006 8131:3.0:1289840122 65261:4.5:1289840252 1079:4.5:1289840321 4973:4.5:1289855573 63992:3.0:1289855744 4246:4.0:1289855761 59725:4.0:1289855781 72395:2.5:1289855842 1270:3.5:1...
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16 53322:3.5:1517021154 103335:2.0:1517021157 162606:5.0:1517021180 157699:5.0:1517021181 97923:4.5:1517021191 56757:2.0:1517021201 112171:4.0:1517021211 102903:4.5:1517021216 91535:5.0:1517021217 136020:3.0:1517021226 168248:5.0:1517021240 103810:4.5:1517021275 49530:3.0:1517021288 108188:4.0:1517021289 165347:4.0:151...
83134
16 168248:5.0:1517021240 103810:4.5:1517021275 49530:3.0:1517021288 108188:4.0:1517021289 165347:4.0:1517021295 152079:3.5:1517021304 58295:3.0:1517021306 165103:2.0:1517021346 110730:3.5:1517021392 79293:4.0:1517021401 114439:3.0:1517021432 166946:3.5:1517021482 140110:3.5:1517021502 152083:3.5:1517021547 115149:3.5:1...
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16 106916:3.0:1572740303 36517:3.0:1572740333 86833:1.5:1572740346 60756:0.5:1572740355 3979:0.5:1572740361 5110:2.0:1572740376 6709:1.5:1572740396 96588:2.0:1572740412 56145:2.5:1572740459 56949:3.0:1572740509 88405:3.5:1572740621 83134:2.5:1572740650 36401:3.0:1572740710 69784:1.0:1572740845 67923:3.0:1572740874 7137...
176101
17 1556:2.0:1027304819 1753:4.0:1027304819 2290:3.0:1027304819 260:5.0:1027304830 1961:5.0:1027304846 4226:5.0:1027304875 5010:4.0:1027304875 1393:3.0:1027304903 3275:5.0:1027304903 4034:5.0:1027304903 4995:5.0:1027304903
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17 5418:5.0:1027305284 5445:5.0:1027305284 5378:5.0:1027305315 5459:4.0:1027305333 50:5.0:1027305453 1198:5.0:1027305470 318:4.0:1027305563 1262:4.0:1027305563 527:5.0:1027305629 1197:4.0:1027305629 2571:5.0:1027305650 3996:5.0:1027305650 296:5.0:1027305662 1196:5.0:1027305669 750:4.0:1027305696 1210:5.0:1027305709 129...
6
17 4226:5.0:1027304875 5010:4.0:1027304875 1393:3.0:1027304903 3275:5.0:1027304903 4034:5.0:1027304903 4995:5.0:1027304903 4022:5.0:1027304931 4896:4.0:1027304945 5074:5.0:1027304958 1297:5.0:1027304970 2916:3.0:1027304970 4975:4.0:1027304981 2403:4.0:1027304996 4890:4.0:1027305008 1580:4.0:1027305020
1676
17 4678:3.0:1027305047 780:4.0:1027305068 4310:3.0:1027305068 2374:3.0:1027305087 5025:4.0:1027305087 5438:3.0:1027305098 2402:3.0:1027305136 3826:2.0:1027305147 2404:3.0:1027305226 2816:2.0:1027305236 5388:5.0:1027305266 5266:4.0:1027305284 5349:4.0:1027305284 5418:5.0:1027305284 5445:5.0:1027305284 5378:5.0:102730531...
50
17 25:4.0:1027304779 2012:3.0:1027304779 157:3.0:1027304797 2628:5.0:1027304797 480:4.0:1027304819 1556:2.0:1027304819 1753:4.0:1027304819 2290:3.0:1027304819 260:5.0:1027304830 1961:5.0:1027304846 4226:5.0:1027304875 5010:4.0:1027304875 1393:3.0:1027304903 3275:5.0:1027304903 4034:5.0:1027304903 4995:5.0:1027304903 40...
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18 147:3.0:1251922424 592:4.0:1251922455 33794:3.5:1251922470 595:4.0:1251922527 51834:3.0:1251922539 1405:1.0:1251922543 6793:3.0:1251922559 2174:4.0:1251922563 215:3.0:1251922571 55805:3.5:1251922580 2797:5.0:1251922673 2694:2.0:1251922693 7147:5.0:1251922704 1732:5.0:1251922716 3967:4.5:1251922752 3949:5.0:125192284...
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1387
19 1:3.0:974704488 594:5.0:974704488 3114:5.0:974704488 1022:5.0:974704603 1282:5.0:974704603 2080:4.0:974704603 2085:5.0:974704603 2355:4.0:974704603 2761:3.0:974704603 2987:5.0:974704603 595:4.0:974704689 1032:5.0:974704689 1405:5.0:974704689 2087:5.0:974704689 2096:5.0:974704689 2137:4.0:974704689 2700:4.0:974704689...
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19 594:5.0:974704488 3114:5.0:974704488 1022:5.0:974704603 1282:5.0:974704603 2080:4.0:974704603 2085:5.0:974704603 2355:4.0:974704603 2761:3.0:974704603 2987:5.0:974704603 595:4.0:974704689 1032:5.0:974704689 1405:5.0:974704689 2087:5.0:974704689 2096:5.0:974704689 2137:4.0:974704689 2700:4.0:974704689 3000:3.0:974704...
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20 318:5.0:1553180573 2959:2.5:1553180575 50:4.5:1553180577 858:4.5:1553180579 58559:3.5:1553180581 260:5.0:1553180585 1196:5.0:1553180587 200042:4.0:1553180593 79132:2.5:1553180600 68954:3.5:1553180601 72998:4.5:1553180603 68157:2.0:1553180609 99114:1.0:1553180613 70286:1.0:1553180617 194448:4.0:1553180622 188773:5.0:...
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20 4973:3.5:1553180757 193065:2.5:1553180762 187593:3.0:1553180764 1265:5.0:1553180785 1240:4.0:1553180790 1270:5.0:1553180792 1527:4.0:1553180796 3408:4.0:1553180801 4034:5.0:1553180804 6711:3.0:1553180806 1197:3.0:1553180838 457:4.0:1553180850 150:3.5:1553180857 3977:2.0:1553181175 3751:4.0:1553181176 5218:4.0:155318...
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20 1221:5.0:1553181207 541:4.0:1553181208 1193:5.0:1553181212 593:5.0:1553181214 2791:5.0:1553181215 912:5.0:1553181217 64614:4.0:1553181228 33679:2.0:1553181231 41566:2.0:1553181236 49530:4.0:1553181238 34405:4.0:1553181241 196417:4.0:1553181245 1203:4.0:1553181301 1213:3.0:1553181303 904:5.0:1553181305 1219:3.0:15531...
364
20 6377:4.0:1553180752 4973:3.5:1553180757 193065:2.5:1553180762 187593:3.0:1553180764 1265:5.0:1553180785 1240:4.0:1553180790 1270:5.0:1553180792 1527:4.0:1553180796 3408:4.0:1553180801 4034:5.0:1553180804 6711:3.0:1553180806 1197:3.0:1553180838 457:4.0:1553180850 150:3.5:1553180857 3977:2.0:1553181175 3751:4.0:155318...
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MovieLens 32M Sequential Recommender Dataset

This dataset is a processed version of the MovieLens 32M dataset, specifically formatted for sequential recommendation tasks. It contains user-item interaction sequences, enriched with rating and timestamp information, split into training, validation, and test sets.

Dataset Structure

The dataset is provided as a DatasetDict with three splits: train, validation, and test. Each split contains:

  • input_sequence: A string representing a user's interaction history. Each interaction is formatted as movieId:rating:timestamp. Sequences vary in length and starting points to provide diverse training examples.
  • target_item: A string representing the movieId of the next item the user interacted with, which the model is expected to predict.

Generation Parameters

This dataset was generated with the following parameters:

  • NUM_USERS: 50000 (Number of unique users included in the dataset)
  • MAX_SEQUENCES_PER_USER: 5 (Maximum number of training sequences sampled from each user's history)

These parameters are also embedded in the dataset's metadata for reproducibility.

Citation

Please cite the original MovieLens dataset if you use this data in your research:

F. Maxwell Harper and Joseph A. Konstan. 2015. The MovieLens Datasets: History and Context. ACM Transactions on Interactive Intelligent Systems (TiiS) 5, 4: 19:1–19:19. https://doi.org/10.1145/2827872

Acknowledgement

The Python scripts used to generate and process this dataset were developed with the assistance of Google's Gemini.

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