  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.
