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In digital advertising, the selection of the optimal item (recommendation)and its best creative presentation (creative optimization) have traditionallybeen considered separate disciplines. However, both contribute significantly touser satisfaction, underpinning our assumption that it relies on both an item'srelevance and its presentation, particularly in the case of visual creatives.In response, we introduce the task of {\itshape Generative CreativeOptimization (GCO)}, which proposes the use of generative models for creativegeneration that incorporate user interests, and {\itshape AdBooster}, a modelfor personalized ad creatives based on the Stable Diffusion outpaintingarchitecture. This model uniquely incorporates user interests both duringfine-tuning and at generation time. To further improve AdBooster's performance,we also introduce an automated data augmentation pipeline. Through ourexperiments on simulated data, we validate AdBooster's effectiveness ingenerating more relevant creatives than default product images, showing itspotential of enhancing user engagement.

AdBooster: Personalized Ad Creative Generation using Stable Diffusion Outpainting