Chaining text-to-image and large language model: A novel approach for
  generating personalized e-commerce banners
Shanu Vashishtha Abhinav Prakash Lalitesh Morishetti Kaushiki Nag Yokila Arora Sushant Kumar Kannan Achan
Text-to-image models such as stable diffusion have opened a plethora of
opportunities for generating art. Recent literature has surveyed the use of
text-to-image models for enhancing the work of many creative artists. Many
e-commerce platforms employ a manual process to generate the banners, which is
time-consuming and has limitations of scalability. In this work, we demonstrate
the use of text-to-image models for generating personalized web banners with
dynamic content for online shoppers based on their interactions. The novelty in
this approach lies in converting users' interaction data to meaningful prompts
without human intervention. To this end, we utilize a large language model
(LLM) to systematically extract a tuple of attributes from item
meta-information. The attributes are then passed to a text-to-image model via
prompt engineering to generate images for the banner. Our results show that the
proposed approach can create high-quality personalized banners for users.