  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.
