EXPRESS: A Recipe for Creativity: An Ingredient Embedding Approach

Abstract

An idea is more than a simple collection of words or ingredients that make up the idea. What makes an idea original or appealing is how these elements are combined in the context in which they appear. This research leverages representation learning methods, specifically word embeddings, to measure internal coherence among the components of a creative idea and capture the relationship between its coherence and idea evaluation or success. Using a large-scale online recipe dataset with over 57K recipes, as well as an ideation dataset of 1,057 ideas, the authors investigate how the fit among idea components relates to downstream measures such as popularity and evaluation. The results consistently show that internal coherence of an idea promotes its popularity, trial, and post-trial evaluation. Counter to prior research on creativity, which suggests that creativity is mostly associated with positive outcomes, the study finds that ideas with unique ingredients have lower popularity or trial, but higher ratings given trial in the context of food recipes. Based on these findings, the authors develop a generative recipe tool that suggests recipe improvements by adding, removing, or substituting ingredients ( http://recipecreativity.com/ ).