AdBooster: Personalized Ad Creative Generation using Stable Diffusion
  Outpainting
Veronika Shilova Ludovic Dos Santos Flavian Vasile Gaëtan Racic Ugo Tanielian
In digital advertising, the selection of the optimal item (recommendation)
and its best creative presentation (creative optimization) have traditionally
been considered separate disciplines. However, both contribute significantly to
user satisfaction, underpinning our assumption that it relies on both an item's
relevance and its presentation, particularly in the case of visual creatives.
In response, we introduce the task of {\itshape Generative Creative
Optimization (GCO)}, which proposes the use of generative models for creative
generation that incorporate user interests, and {\itshape AdBooster}, a model
for personalized ad creatives based on the Stable Diffusion outpainting
architecture. This model uniquely incorporates user interests both during
fine-tuning and at generation time. To further improve AdBooster's performance,
we also introduce an automated data augmentation pipeline. Through our
experiments on simulated data, we validate AdBooster's effectiveness in
generating more relevant creatives than default product images, showing its
potential of enhancing user engagement.