  Online retail is a visual experience- Shoppers often use images as first
order information to decide if an item matches their personal style. Image
characteristics such as color, simplicity, scene composition, texture, style,
aesthetics and overall quality play a crucial role in making a purchase
decision, clicking on or liking a product listing. In this paper we use a set
of image features that indicate quality to predict product listing popularity
on a major e-commerce website, Etsy. We first define listing popularity through
search clicks, favoriting and purchase activity. Next, we infer listing quality
from the pixel-level information of listed images as quality features. We then
compare our findings to text-only models for popularity prediction. Our initial
results indicate that a combined image and text modeling of product listings
outperforms text-only models in popularity prediction.
