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base
int64
target
int64
name_overlap
string
name_iou
float64
categories
string
96,313
86,230
grilled garlic ch
0.5312
['dairy_free']
86,230
96,313
grilled garlic ch
0.5312
['low_calorie', 'low_sugar', 'vegetarian']
107,592
96,313
garlic cheese grits
0.7037
['low_calorie', 'low_sugar']
442,284
96,313
garlic cheese grits
0.5135
['low_calorie', 'low_carb', 'low_sugar', 'vegetarian']
96,313
379,443
grilled garlic ch
0.5312
['dairy_free']
379,443
96,313
grilled garlic ch
0.5312
['low_calorie', 'low_carb', 'low_sugar', 'vegetarian']
216,364
96,313
ed garlic cheese grits
0.5116
['low_calorie', 'low_carb', 'low_sugar', 'vegetarian']
96,313
316,225
grilled garlic ch
0.5312
['dairy_free']
316,225
96,313
grilled garlic ch
0.5312
['low_calorie', 'low_carb', 'low_sugar', 'vegetarian']
67,385
96,313
garlic cheese grits
0.7037
['low_calorie', 'low_carb', 'low_sugar', 'vegetarian']
155,316
96,313
garlic cheese grits
0.7037
['low_calorie', 'low_carb', 'low_sugar']
96,313
375,457
garlic cheese grits
0.5758
['low_fat']
375,457
96,313
garlic cheese grits
0.5758
['low_carb', 'low_sugar', 'vegetarian']
353,738
96,313
garlic cheese grits
0.5135
['low_calorie', 'low_carb', 'low_sugar']
202,932
96,313
garlic cheese grits
0.7037
['low_calorie', 'low_carb', 'low_sugar']
472,759
96,313
ed garlic cheese grits
0.7333
['low_calorie', 'low_sugar', 'vegetarian']
232,037
28,165
shrimp and andouille jambalaya
0.6522
['low_carb', 'low_sugar']
232,047
185,154
with dried cherries
0.5135
['vegan', 'vegetarian']
326,752
232,050
almond brittle
0.5833
['dairy_free', 'vegan']
341,532
232,050
sweet almond brittle
0.8
['dairy_free', 'vegan']
44,541
232,050
almond brittle
0.5833
['dairy_free', 'vegan']
232,083
182,639
asparagus omelette
0.75
['low_carb']
232,083
376,507
omelette wrap
0.5417
['dairy_free', 'low_calorie', 'low_carb', 'low_sugar']
376,507
232,083
omelette wrap
0.5417
['vegetarian']
232,083
414,590
asparagus omelette
0.5294
['low_carb']
232,083
474,068
asparagus omelette
0.5625
['low_calorie', 'low_carb', 'low_fat', 'low_sodium']
232,083
370,616
asparagus omelette
0.75
['dairy_free']
232,083
6,813
asparagus omelet
0.6667
['low_calorie', 'low_carb']
182,662
79,222
crab chowder
0.5
['low_calorie', 'low_fat', 'low_sodium']
323,601
79,222
crab chowder
0.6316
['low_calorie', 'low_fat', 'low_sodium']
393,078
79,222
crab chowder
0.6316
['low_calorie', 'low_fat', 'low_sodium']
79,222
46,379
crab chowder
0.6316
['low_carb']
46,379
79,222
crab chowder
0.6316
['low_fat']
287,512
79,222
crab chowder
0.6316
['low_calorie', 'low_fat', 'low_sodium']
218,807
393,638
simple sloppy joes
0.5
['low_carb']
367,891
393,638
simple sloppy joes
0.6429
['gluten_free', 'low_carb']
378,423
393,638
simple sloppy joes
0.6429
['low_carb']
367,041
393,638
simple sloppy joes
0.5294
['gluten_free', 'low_carb']
429,666
393,638
simple sloppy joes
0.6429
['low_carb']
417,270
393,638
simple sloppy joes
0.6429
['dairy_free', 'low_carb']
232,086
34,803
chocolate chip muffins
0.5116
['low_fat', 'low_sodium']
232,086
515,481
chocolate chip muffins
0.5238
['dairy_free']
232,086
323,164
chocolate chip muffins
0.5
['dairy_free', 'vegan']
232,086
218,089
chocolate chip muffins
0.6111
['low_calorie', 'low_carb', 'low_sodium']
232,086
468,387
n chocolate chip muffins
0.6857
['dairy_free']
468,387
232,086
n chocolate chip muffins
0.6857
['gluten_free']
232,086
419,713
chocolate chip muffins
0.5238
['dairy_free', 'vegan']
232,086
166,157
n chocolate chip muffins
0.6857
['dairy_free', 'vegan']
232,086
167,897
chocolate chip muffins
0.5238
['dairy_free']
232,086
225,340
chocolate chip muffins
0.6111
['low_calorie', 'low_carb', 'low_sodium']
261,757
232,086
chocolate chip muffins
0.5
['gluten_free']
232,086
316,472
chocolate chip muffins
0.55
['dairy_free']
374,729
232,086
chocolate chip muffins
0.55
['gluten_free']
232,086
416,820
chocolate chip muffins
0.6111
['dairy_free']
232,086
259,320
chocolate chip muffins
0.5
['low_fat']
232,086
172,945
chocolate chip muffins
0.5116
['dairy_free']
232,086
107,296
chocolate chip muffins
0.5946
['low_fat']
232,086
179,045
chocolate chip muffins
0.5
['low_calorie']
232,086
334,902
n chocolate chip muffins
0.6857
['dairy_free']
232,086
435,314
chocolate chip muffins
0.5238
['dairy_free', 'vegan']
232,086
302,314
chocolate chip muffins
0.6111
['dairy_free']
232,086
50,535
chocolate chip muffins
0.5
['low_fat']
232,086
260,347
chocolate chip muffins
0.6111
['dairy_free']
232,086
343,462
chocolate chip muffins
0.6111
['dairy_free']
232,086
418,073
n chocolate chip muffins
0.6857
['dairy_free']
232,086
470,977
chocolate chip muffins
0.5366
['dairy_free']
232,086
37,585
chocolate chip muffins
0.6111
['dairy_free']
232,086
382,365
chocolate chip muffins
0.6111
['dairy_free']
232,086
336,219
chocolate chip muffins
0.5116
['dairy_free', 'low_fat', 'vegan']
232,086
3,564
n chocolate chip muffins
0.6857
['dairy_free']
411,261
232,086
golden chocolate c
0.5625
['gluten_free']
232,086
308,079
chocolate chip muffins
0.6286
['dairy_free']
232,086
388,388
chocolate chip muffins
0.5789
['dairy_free']
232,086
462,079
n chocolate chip muffins
0.6
['dairy_free']
462,079
232,086
n chocolate chip muffins
0.6
['gluten_free']
232,086
57,079
chocolate chip muffins
0.7586
['dairy_free']
232,086
80,619
chocolate chip muffins
0.6286
['low_calorie']
232,086
84,426
chocolate chip muffins
0.5946
['dairy_free', 'low_fat']
232,086
395,494
chocolate chip muffins
0.6111
['dairy_free', 'vegan']
232,086
516,457
n chocolate chip muffins
0.6857
['dairy_free']
232,086
377,421
chocolate chip muffins
0.5238
['dairy_free', 'low_fat']
232,086
226,077
n chocolate chip muffins
0.6857
['dairy_free']
232,086
460,688
chocolate chip muffins
0.5
['dairy_free', 'vegan']
232,086
220,379
chocolate chip muffins
0.6111
['dairy_free']
232,086
290,118
chocolate chip muffins
0.6111
['dairy_free']
232,086
298,488
chocolate chip muffins
0.5238
['dairy_free', 'vegan']
232,086
19,424
chocolate chip muffins
0.6111
['dairy_free']
232,086
350,003
chocolate chip muffins
0.6111
['dairy_free', 'vegan']
350,003
232,086
chocolate chip muffins
0.6111
['gluten_free']
232,086
330,569
n chocolate chip muffins
0.5333
['dairy_free']
232,086
260,351
chocolate chip muffins
0.6111
['dairy_free', 'low_sodium']
232,086
441,002
n chocolate chip muffins
0.6316
['dairy_free', 'vegan']
232,086
255,845
chocolate chip muffin
0.7
['low_calorie', 'low_carb', 'low_sodium']
139,831
232,088
chocolate chip cookies
0.5238
['dairy_free']
232,088
443,705
chocolate chip cookies
0.6471
['low_sodium']
443,705
232,088
chocolate chip cookies
0.6471
['dairy_free']
355,169
232,088
chocolate chip cookies
0.5366
['dairy_free']
20,763
232,088
chocolate chip cookies
0.5366
['dairy_free']
142,101
232,088
chocolate chip cookies
0.6286
['dairy_free']
232,088
390,542
chocolate chip cookies
0.5238
['low_calorie']
End of preview.
YAML Metadata Error: "annotations_creators" must be an array
YAML Metadata Error: "language_creators" must be an array

RecipePairs dataset, originally from the 2022 EMNLP paper: "SHARE: a System for Hierarchical Assistive Recipe Editing" by Shuyang Li, Yufei Li, Jianmo Ni, and Julian McAuley.

This version (1.5.0) has been updated with 6.9M pairs of base -> target recipes, alongside their name overlap, IOU (longest common subsequence / union), and target dietary categories. These cover the 459K recipes from the original GeniusKitcen/Food.com dataset.

If you would like to use this data or found it useful in your work/research, please cite the following papers:

@inproceedings{li-etal-2022-share,
    title = "{SHARE}: a System for Hierarchical Assistive Recipe Editing",
    author = "Li, Shuyang  and
      Li, Yufei  and
      Ni, Jianmo  and
      McAuley, Julian",
    booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing",
    month = dec,
    year = "2022",
    address = "Abu Dhabi, United Arab Emirates",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2022.emnlp-main.761",
    pages = "11077--11090",
    abstract = "The large population of home cooks with dietary restrictions is under-served by existing cooking resources and recipe generation models. To help them, we propose the task of controllable recipe editing: adapt a base recipe to satisfy a user-specified dietary constraint. This task is challenging, and cannot be adequately solved with human-written ingredient substitution rules or existing end-to-end recipe generation models. We tackle this problem with SHARE: a System for Hierarchical Assistive Recipe Editing, which performs simultaneous ingredient substitution before generating natural-language steps using the edited ingredients. By decoupling ingredient and step editing, our step generator can explicitly integrate the available ingredients. Experiments on the novel RecipePairs dataset{---}83K pairs of similar recipes where each recipe satisfies one of seven dietary constraints{---}demonstrate that SHARE produces convincing, coherent recipes that are appropriate for a target dietary constraint. We further show through human evaluations and real-world cooking trials that recipes edited by SHARE can be easily followed by home cooks to create appealing dishes.",
}

@inproceedings{majumder-etal-2019-generating,
    title = "Generating Personalized Recipes from Historical User Preferences",
    author = "Majumder, Bodhisattwa Prasad  and
      Li, Shuyang  and
      Ni, Jianmo  and
      McAuley, Julian",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/D19-1613",
    doi = "10.18653/v1/D19-1613",
    pages = "5976--5982",
    abstract = "Existing approaches to recipe generation are unable to create recipes for users with culinary preferences but incomplete knowledge of ingredients in specific dishes. We propose a new task of personalized recipe generation to help these users: expanding a name and incomplete ingredient details into complete natural-text instructions aligned with the user{'}s historical preferences. We attend on technique- and recipe-level representations of a user{'}s previously consumed recipes, fusing these {`}user-aware{'} representations in an attention fusion layer to control recipe text generation. Experiments on a new dataset of 180K recipes and 700K interactions show our model{'}s ability to generate plausible and personalized recipes compared to non-personalized baselines.",
}
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