  Homepage is the first touch point in the customer's journey and is one of the
prominent channels of revenue for many e-commerce companies. A user's attention
is mostly captured by homepage banner images (also called Ads/Creatives). The
set of banners shown and their design, influence the customer's interest and
plays a key role in optimizing the click through rates of the banners.
Presently, massive and repetitive effort is put in, to manually create
aesthetically pleasing banner images. Due to the large amount of time and
effort involved in this process, only a small set of banners are made live at
any point. This reduces the number of banners created as well as the degree of
personalization that can be achieved. This paper thus presents a method to
generate creatives automatically on a large scale in a short duration. The
availability of diverse banners generated helps in improving personalization as
they can cater to the taste of larger audience. The focus of our paper is on
generating wide variety of homepage banners that can be made as an input for
user level personalization engine. Following are the main contributions of this
paper: 1) We introduce and explain the need for large scale banner generation
for e-commerce 2) We present on how we utilize existing deep learning based
detectors which can automatically annotate the required objects/tags from the
image. 3) We also propose a Genetic Algorithm based method to generate an
optimal banner layout for the given image content, input components and other
design constraints. 4) Further, to aid the process of picking the right set of
banners, we designed a ranking method and evaluated multiple models. All our
experiments have been performed on data from Myntra (http://www.myntra.com),
one of the top fashion e-commerce players in India.
