  Online advertising is a huge, rapidly growing advertising market in today's
world. One common form of online advertising is using image ads. A decision is
made (often in real time) every time a user sees an ad, and the advertiser is
eager to determine the best ad to display. Consequently, many algorithms have
been developed that calculate the optimal ad to show to the current user at the
present time. Typically, these algorithms focus on variations of the ad,
optimizing among different properties such as background color, image size, or
set of images. However, there is a more fundamental layer. Our study looks at
new qualities of ads that can be determined before an ad is shown (rather than
online optimization) and defines which ads are most likely to be successful.
  We present a set of novel algorithms that utilize deep-learning image
processing, machine learning, and graph theory to investigate online
advertising and to construct prediction models which can foresee an image ad's
success. We evaluated our algorithms on a dataset with over 260,000 ad images,
as well as a smaller dataset specifically related to the automotive industry,
and we succeeded in constructing regression models for ad image click rate
prediction. The obtained results emphasize the great potential of using
deep-learning algorithms to effectively and efficiently analyze image ads and
to create better and more innovative online ads. Moreover, the algorithms
presented in this paper can help predict ad success and can be applied to
analyze other large-scale image corpora.
