
RecipePairs dataset, originally from the 2022 EMNLP paper: ["SHARE: a System for Hierarchical Assistive Recipe Editing"](https://aclanthology.org/2022.emnlp-main.761/) 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.",
}
```
task_categories:text-generation multilinguality:monolingual size_categories:1M<n<10M source_datasets:original language:en license:gpl-3.0 region:us
{"annotations_creators": "no-annotation", "language_creators": "found", "language": "en", "license": "gpl-3.0", "multilinguality": "monolingual", "size_categories": ["1M<n<10M"], "source_datasets": "original", "task_categories": ["text-generation"], "pretty_name": "RecipePairs", "dataset_info": [{"config_name": "1.5.0", "splits": [{"name": "pairs", "num_examples": 6908697}]}]}
TAGS
#task_categories-text-generation #multilinguality-monolingual #size_categories-1M<n<10M #source_datasets-original #language-English #license-gpl-3.0 #region-us 


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/URL dataset.

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

TAGS
#task_categories-text-generation #multilinguality-monolingual #size_categories-1M<n<10M #source_datasets-original #language-English #license-gpl-3.0 #region-us 

passage: TAGS
#task_categories-text-generation #multilinguality-monolingual #size_categories-1M<n<10M #source_datasets-original #language-English #license-gpl-3.0 #region-us 
