Design Your Ad: Personalized Advertising Image and Text Generation with Unified Autoregressive Models
For e-commerce platforms, this work addresses the need for personalized ad generation that captures individual user preferences beyond average CTR, though it is an incremental improvement over existing multi-model pipelines.
The paper tackles personalized advertising image and text generation in e-commerce by proposing Uni-AdGen, a unified autoregressive model that jointly generates both modalities from user click histories. It outperforms baselines in general and personalized advertisement generation, supported by a new dataset PAd1M and a PBS metric.
Generating realistic and user-preferred advertisements is a key challenge in e-commerce. Existing approaches utilize multiple independent models driven by click-through-rate (CTR) to controllably create attractive image or text advertisements. However, their pipelines lack cross-modal perception and rely on CTR that only reflects average preferences. Therefore, we explore jointly generating personalized image-text advertisements from historical click behaviors. We first design a Unified Advertisement Generative model (Uni-AdGen) that employs a single autoregressive framework to produce both advertising images and texts. By incorporating a foreground perception module and instruction tuning, Uni-AdGen enhances the realism of the generated content. To further personalize advertisements, we equip Uni-AdGen with a coarse-to-fine preference understanding module that effectively captures user interests from noisy multimodal historical behaviors to drive personalized generation. Additionally, we construct the first large-scale Personalized Advertising image-text dataset (PAd1M) and introduce a Product Background Similarity (PBS) metric to facilitate training and evaluation. Extensive experiments show that our method outperforms baselines in general and personalized advertisement generation. Our project is available at https://github.com/JD-GenX/Uni-AdGen.