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

Abstract

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.

arXiv commentAccepted and to be appeared in CIKM-2022

References in corpus (8)

• Adam: A Method for Stochastic Optimization
• Deep Sets
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• KitcheNette: Predicting and Recommending Food Ingredient Pairings using Siamese Neural Networks
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• Recipe1M+: A Dataset for Learning Cross-Modal Embeddings for Cooking Recipes and Food Images
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Cited by in corpus (1)

• KitchenScale: Learning to predict ingredient quantities from recipe contexts