Machine Learning for Creativity: Using Similarity Networks to Design Better Crowdfunding Projects

Abstract

A fundamental tension exists in creativity between novelty and similarity. This research exploits this tension to help creators craft successful projects in crowdfunding. To do so, the authors apply the concept of combinatorial creativity, analyzing each new project in connection to prior similar projects. By using machine learning techniques (Word2vec and Word Mover’s Distance), they measure the degrees of similarity between crowdfunding projects on Kickstarter. They analyze how this similarity pattern relates to a project’s funding performance and find that (1) the prior level of success of similar projects strongly predicts a new project’s funding performance, (2) the funding performance increases with a balance between being novel and imitative, (3) the optimal funding goal is close to the funds raised by prior similar projects, and (4) the funding performance increases with a balance between atypical and conventional imitation. The authors use these findings to generate actionable recommendations for project creators and crowdfunding platforms.