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

TL;DR
This paper introduces Uni-AdGen, a unified autoregressive model for generating personalized advertising images and texts by understanding user preferences from historical behaviors, improving realism and relevance.
Contribution
The paper presents the first large-scale dataset for personalized ad generation and a novel model that jointly generates images and texts with enhanced personalization capabilities.
Findings
Outperforms baseline methods in ad quality and personalization.
Effectively captures user preferences from noisy multimodal data.
Achieves higher realism in generated advertisements.
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…
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