Languages: English
Multilinguality: monolingual
Size Categories: 1M<n<10M
Language Creators: found
Annotations Creators: no-annotation
Source Datasets: original
License: gpl-3.0
Dataset Preview
Go to dataset viewer
The dataset preview is not available for this split.
The server is busier than usual and the response is not ready yet. Please retry later.
Error code:   ResponseNotReady

This error is unexpected. Please open an issue for direct support.

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/ dataset.

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

    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 = "",
    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.",

    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 = "",
    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.",
Downloads last month