Alternative-ingredient Recommendation Based on Co-occurrence Relation on Recipe Database

Yamanishi, Ryosuke; Shino, Naoki; Nishihara, Yoko; Fukumoto, Junichi; Kaizaki, Aya

doi:10.1016/j.procs.2015.08.138

Cited by 8 publications

(4 citation statements)

References 5 publications

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“…We are motivated by work on single ingredient substitution [31,35], but our system accommodates multiple simultaneous substitutions when predicting a target ingredient set. Recent work in recipe generation has focused on improving coherence [2] and ingredient specificity [17].…”

Section: Related Workmentioning

confidence: 99%

SHARE: a System for Hierarchical Assistive Recipe Editing

Li²,

et al. 2021

Preprint

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Section: Related Workmentioning

confidence: 99%

SHARE: a System for Hierarchical Assistive Recipe Editing

Li²,

et al. 2021

Preprint

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“…8 We then use a rule-based method to substitute any ingredients that violate the dietary constraint with alternatives from a food substitution guide (Steen and Newman, 2010). While there is work on automatically substituting recipe ingredients with similar ones (Teng et al, 2012;Boscarino et al, 2014;Yamanishi et al, 2015), to our knowledge no work makes recipe substitutions in accordance with dietary constraints.…”

Section: Contextual Rewriter + Ingredient Promptmentioning

confidence: 99%

Substance over Style: Document-Level Targeted Content Transfer

Hegel

Rao

Çelikyılmaz

et al. 2020

Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

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Existing language models excel at writing from scratch, but many real-world scenarios require rewriting an existing document to fit a set of constraints. Although sentence-level rewriting has been fairly well-studied, little work has addressed the challenge of rewriting an entire document coherently. In this work, we introduce the task of document-level targeted content transfer and address it in the recipe domain, with a recipe as the document and a dietary restriction (such as vegan or dairy-free) as the targeted constraint. We propose a novel model for this task based on the generative pretrained language model (GPT-2) and train on a large number of roughly-aligned recipe pairs. 1 Both automatic and human evaluations show that our model out-performs existing methods by generating coherent and diverse rewrites that obey the constraint while remaining close to the original document. Finally, we analyze our model's rewrites to assess progress toward the goal of making language generation more attuned to constraints that are substantive rather than stylistic.

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“…Prior research in recipe editing has focused on substituting individual ingredients in the ingredients list (Yamanishi et al, 2015) or recommending new recipes based on similar ingredients (Teng et al, 2012). Individual ingredient substitution rules (e.g., tapioca flour and xanthan gum for wheat flour) often necessitate additional changes to the cooking procedure to function properly (Li et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

Assistive Recipe Editing through Critiquing

Antognini,

Li,

Faltings

et al. 2023

Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics

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Home cooks often have specific requirements regarding individual ingredients in a recipe (e.g., allergies). Substituting ingredients in a recipe can necessitate complex changes to instructions (e.g., replacing chicken with tofu in a stir fry requires pressing the tofu, marinating it for less time, and par-cooking)-which has thus far hampered efforts to automatically create satisfactory versions of recipes. We address these challenges with the RecipeCrit model that allows users to edit existing recipes by proposing individual ingredients to add or remove. Crucially, we develop an unsupervised critiquing module that allows our model to iteratively re-write recipe instructions to accommodate the complex changes needed for ingredient substitutions. Experiments on the Recipe1M dataset show that our model can more effectively edit recipes compared to strong languagemodeling baselines, creating recipes that satisfy user constraints and humans deem more correct, serendipitous, coherent, and relevant.

show abstract

Alternative-ingredient Recommendation Based on Co-occurrence Relation on Recipe Database

Cited by 8 publications

References 5 publications

SHARE: a System for Hierarchical Assistive Recipe Editing

SHARE: a System for Hierarchical Assistive Recipe Editing

Substance over Style: Document-Level Targeted Content Transfer

Assistive Recipe Editing through Critiquing

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