A Recipe for Creating Recipes: An Ingredient Embedding Approach

Abstract

An idea is a collection of existing concepts or words. What makes an idea original or appealing is how these elements are combined in the context in which they appear. Similarly, a food recipe is a combination of ingredients, and it is often evaluated based on how these ingredients fit together to form the whole. This research leverages representation learning methods, specifically word embeddings, to measure the fit among ingredients in the recipe and capture the possibly complex interactions between these ingredients. Using a large-scale online recipe dataset with over 57K recipes, the authors investigate how the fit between the ingredients relates to recipe popularity (trial) and favorability (ratings). Counter to prior research on creativity, which primarily suggests that creativity is mostly associated with positive outcomes, the study finds that recipes with unique ingredients have lower trials, but higher ratings given trial. In contrast, high fit among ingredients promotes both trial and ratings. The paper also extends the proposed approach to an ideation dataset for a new health app. Based on these findings, the authors develop a generative recipe tool that suggests recipe improvements by adding, removing, or substituting ingredients (http://recipecreativity.com/) and demonstrate its practical value using online panel experiments.

Keywords: creativity, idea generation, food recipes, computational creativity, word embeddings, representation learning, online applications