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

TL;DR

RecipeMind is a novel computational model that predicts ingredient compatibility to assist users in creating recipes by suggesting suitable ingredients based on food pairing principles.

Contribution

The paper introduces RecipeMind, a food affinity prediction model trained on a large dataset, enabling effective ingredient suggestion for recipe ideation.

Findings

Accurately predicts ingredient compatibility scores

Enhances recipe creation by suggesting complementary ingredients

Demonstrates potential in culinary assistance applications

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.