AdBooster: Personalized Ad Creative Generation using Stable Diffusion Outpainting
This addresses the problem of improving user engagement in digital advertising by combining recommendation and creative optimization, though it appears incremental as it builds on existing Stable Diffusion techniques.
The paper tackles the problem of generating personalized ad creatives by introducing Generative Creative Optimization (GCO) and AdBooster, a model based on Stable Diffusion outpainting that incorporates user interests, and shows it generates more relevant creatives than default product images in experiments on simulated data.
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.