RecipeMind: Guiding Ingredient Choices from Food Pairing to Recipe Completion using Cascaded Set Transformer
This work addresses recipe creation for users in the cuisine domain, but it appears incremental as it builds on existing methods for ingredient pairing.
The authors tackled the problem of recipe ideation by developing RecipeMind, a model that predicts food affinity scores to guide ingredient selection, and demonstrated its potential through experiments and qualitative analysis.
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.