mlsanityData to 14 Oct 2026

RecipeMind: Guiding Ingredient Choices from Food Pairing to Recipe Completion using Cascaded Set Transformer

arXiv 2210.10628 ↗First posted 14 Oct 2022cs.IR cs.AI cs.LGcited by 4

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Abstract (arXiv)

We propose a computational approach for recipe ideation, a downstream task that helps users select and gather ingredients for creating dishes. To perform this task, we developed RecipeMind, a food affinity score prediction model that quantifies the suitability of adding an ingredient to set of other ingredients. We constructed a large-scale dataset containing ingredient co-occurrence based scores to train and evaluate RecipeMind on food affinity score prediction. Deployed in recipe ideation, RecipeMind helps the user expand an initial set of ingredients by suggesting additional ingredients. Experiments and qualitative analysis show RecipeMind's potential in fulfilling its assistive role in cuisine domain.

Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Builds on 21 of its 57 references that are in the map. First posted on arXiv 14 Oct 2022.

Builds on

21 of 57 references.

References · 21

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9 more
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Built on by

Cited by year

Citing papers per year: 2023: 1; 2024: 2; 2025: 1.

4 papers.

  1. 2023KitchenScale: Learning to predict ingredient quantities from recipe contextscited by 2
  2. 2024CookingSense: A Culinary Knowledgebase with Multidisciplinary Assertionscited by 2
  3. 2025S2SRec2: Set-to-Set Recommendation for Basket Completion with Recipecited by 1
  4. 2024Culinary Class Wars: Evaluating LLMs using ASH in Cuisine Transfer Taskcited by 0