  As any savvy online shopper knows, second-hand peer-to-peer marketplaces are
filled with images of mixed quality. How does image quality impact marketplace
outcomes, and can quality be automatically predicted? In this work, we
conducted a large-scale study on the quality of user-generated images in
peer-to-peer marketplaces. By gathering a dataset of common second-hand
products (~75,000 images) and annotating a subset with human-labeled quality
judgments, we were able to model and predict image quality with decent accuracy
(~87%). We then conducted two studies focused on understanding the relationship
between these image quality scores and two marketplace outcomes: sales and
perceived trustworthiness. We show that image quality is associated with higher
likelihood that an item will be sold, though other factors such as view count
were better predictors of sales. Nonetheless, we show that high quality
user-generated images selected by our models outperform stock imagery in
eliciting perceptions of trust from users. Our findings can inform the design
of future marketplaces and guide potential sellers to take better product
images.
