AdBooster: Personalized Ad Creative Generation using Stable Diffusion Outpainting

TL;DR

AdBooster leverages Stable Diffusion outpainting to generate personalized ad creatives that incorporate user interests, aiming to improve relevance and user engagement in digital advertising.

Contribution

The paper introduces Generative Creative Optimization (GCO) and presents AdBooster, a novel personalized ad creative generation model using Stable Diffusion outpainting with user interest integration.

Findings

AdBooster generates more relevant creatives than default images.

Automated data augmentation improves creative quality.

Potential to enhance user engagement in digital ads.

Abstract

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.