Predicting A Creator's Preferences In, and From, Interactive Generative Art

TL;DR

This study investigates whether user choices in interactive generative art can predict their broader preferences or traits, finding that preferences within the art form are predictable among themselves but do not extend to other life preferences.

Contribution

The paper introduces a dataset of preferences from 311 subjects and demonstrates that preferences within a generative art form are reliably predictable, unlike cross-domain preferences.

Findings

Preferences within the generative art form are predictable of each other.

Preferences in the art form do not predict preferences in other life domains.

Machine learning models can effectively predict intra-domain preferences.

Abstract

As a lay user creates an art piece using an interactive generative art tool, what, if anything, do the choices they make tell us about them and their preferences? These preferences could be in the specific generative art form (e.g., color palettes, density of the piece, thickness or curvatures of any lines in the piece); predicting them could lead to a smarter interactive tool. Or they could be preferences in other walks of life (e.g., music, fashion, food, interior design, paintings) or attributes of the person (e.g., personality type, gender, artistic inclinations); predicting them could lead to improved personalized recommendations for products or experiences. To study this research question, we collect preferences from 311 subjects, both in a specific generative art form and in other walks of life. We analyze the preferences and train machine learning models to predict a subset of preferences from the remaining. We find that preferences in the generative art form we studied cannot predict preferences in other walks of life better than chance (and vice versa). However, preferences within the generative art form are reliably predictive of each other.