# Enabling and Understanding Personalization in AI-Generated Advertising Imagery

arXiv: https://arxiv.org/abs/2609.12697 · HTML: https://mlsanity.com/paper/2609.12697/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 87 references that are in the map.
- First posted on arXiv 11 Sep 2026.

## Builds on (most cited first, 9 of 9)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Deep Learning Recommendation Model for Personalization and Recommendation Systems](https://mlsanity.com/paper/1906.00091/) (2019)
- [A New Creative Generation Pipeline for Click-Through Rate with Stable Diffusion Model](https://mlsanity.com/paper/2401.10934/) (2024)
- [Chaining text-to-image and large language model: A novel approach for generating personalized e-commerce banners](https://mlsanity.com/paper/2403.05578/) (2024)
- [AdBooster: Personalized Ad Creative Generation using Stable Diffusion Outpainting](https://mlsanity.com/paper/2309.11507/) (2023)
- [Dynamic Product Image Generation and Recommendation at Scale for Personalized E-commerce](https://mlsanity.com/paper/2408.12392/) (2024)
- [SoMin.ai: Personality-Driven Content Generation Platform](https://mlsanity.com/paper/2011.14615/) (2020)
- [Two-stage dynamic creative optimization under sparse ambiguous samples for e-commerce advertising](https://mlsanity.com/paper/2312.01295/) (2023)

## Abstract (arXiv)

Personalized marketing traditionally matches static products to customers, while dynamic creative optimization focuses mainly on AI-driven text personalization or basic product image modifications. We address this gap by developing and implementing an AI-based framework that generates personalized advertising imagery directly from customer data. We evaluate this framework in a two-stage within-subject study with N=100 participants across four products and three levels of personalization, varied by the amount and specificity of customer data used. Participants rated each image on attitude toward the advertisement, attitude toward the product, and purchase intention. Results show that participants perceive differences across personalization levels and evaluate AI-generated advertising imagery most positively at a moderate level of personalization. High personalization increases perceived personalization, which is positively associated with all three outcome measures, but also increases perceived creepiness, which is negatively associated with the outcomes and dominates the total effect.