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.

利用文本与图像模型生成个性化网页横幅，基于用户的互动行为生成动态内容的横幅，通过大型语言模型系统地提取项目元信息的属性，并通过提示工程将属性传递给文本与图像模型生成横幅的图像，结果表明该方法可以为用户创建高质量的个性化横幅。