AdBooster: Personalized Ad Creative Generation using Stable Diffusion Outpainting

• URL: http://arxiv.org/abs/2309.11507v1
• Date: Fri, 8 Sep 2023 12:57:05 GMT
• Title: AdBooster: Personalized Ad Creative Generation using Stable Diffusion Outpainting
• Authors: Veronika Shilova, Ludovic Dos Santos, Flavian Vasile, Ga\"etan Racic, Ugo Tanielian
• Abstract summary: In digital advertising, the selection of the optimal item (recommendation) and its best creative presentation (creative optimization) have traditionally been considered separate disciplines. We introduce the task of generative models for creative generation that incorporate user interests, and itshape AdBooster, a model for personalized ad creatives.
• Score: 7.515971669919419
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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