Poster

Design Your Ad: Personalized Advertising Image and Text Generation with Unified Autoregressive Models

Yexing Xu ⋅ Wei Feng ⋅ Shen Zhang ⋅ Haohan Wang ⋅ Yuxin Qin ⋅ Yaoyu Li ⋅ Ao Ma ⋅ Yuhao Luo ⋅ Lu Wang ⋅ Xudong Ren ⋅ Haoran Wang ⋅ Run Ling ⋅ Zheng Zhang ⋅ Jingjing Lv ⋅ Junjie Shen ⋅ Ching Law ⋅ Longguang Wang ⋅ Yulan Guo

Abstract

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. The dataset and codes will be released after acceptance.