A Machine Learning Approach for Evaluating Creative Artifacts
This work addresses the need for objective and automatic quantification of creativity across domains, though it is incremental as it applies existing regression methods to a new dataset.
The paper tackled the problem of automatically evaluating creative artifacts by proposing a regression-based learning framework that incorporates criteria like novelty and influence, and reported promising results including improved movie rating prediction and identification of creative movies using IMDb and Rotten Tomatoes data.
Much work has been done in understanding human creativity and defining measures to evaluate creativity. This is necessary mainly for the reason of having an objective and automatic way of quantifying creative artifacts. In this work, we propose a regression-based learning framework which takes into account quantitatively the essential criteria for creativity like novelty, influence, value and unexpectedness. As it is often the case with most creative domains, there is no clear ground truth available for creativity. Our proposed learning framework is applicable to all creative domains; yet we evaluate it on a dataset of movies created from IMDb and Rotten Tomatoes due to availability of audience and critic scores, which can be used as proxy ground truth labels for creativity. We report promising results and observations from our experiments in the following ways : 1) Correlation of creative criteria with critic scores, 2) Improvement in movie rating prediction with inclusion of various creative criteria, and 3) Identification of creative movies.