A Big Data Approach to Computational Creativity

TL;DR

This paper presents a data-driven approach to computational creativity that integrates diverse data sources and big data analytics to generate high-quality creative artifacts, demonstrated through a culinary system.

Contribution

It introduces a novel big data methodology that enhances the selective step in computational creativity, enabling the production of more valid and creative outputs.

Findings

System can autonomously generate creative recipes and menus

Data integration improves quality and novelty of outputs

Approach addresses data volume, velocity, variety, and veracity issues

Abstract

Computational creativity is an emerging branch of artificial intelligence that places computers in the center of the creative process. Broadly, creativity involves a generative step to produce many ideas and a selective step to determine the ones that are the best. Many previous attempts at computational creativity, however, have not been able to achieve a valid selective step. This work shows how bringing data sources from the creative domain and from hedonic psychophysics together with big data analytics techniques can overcome this shortcoming to yield a system that can produce novel and high-quality creative artifacts. Our data-driven approach is demonstrated through a computational creativity system for culinary recipes and menus we developed and deployed, which can operate either autonomously or semi-autonomously with human interaction. We also comment on the volume, velocity, variety, and veracity of data in computational creativity.