# Learning to Create Better Ads: Generation and Ranking Approaches for Ad Creative Refinement

arXiv: https://arxiv.org/abs/2008.07467 · HTML: https://mlsanity.com/paper/2008.07467/

## Key facts

- Cited by 29 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Aug 2020.

## Built on by (most cited first, 29 of 29)

- [A New Creative Generation Pipeline for Click-Through Rate with Stable Diffusion Model](https://mlsanity.com/paper/2401.10934/) (2024)
- [Striking Gold in Advertising: Standardization and Exploration of Ad Text Generation](https://mlsanity.com/paper/2309.12030/) (2023)
- [Generate E-commerce Product Background by Integrating Category Commonality and Personalized Style](https://mlsanity.com/paper/2312.13309/) (2023)
- [Towards Reliable Advertising Image Generation Using Human Feedback](https://mlsanity.com/paper/2408.00418/) (2024)
- [CTR-Driven Advertising Image Generation with Multimodal Large Language Models](https://mlsanity.com/paper/2502.06823/) (2025)
- [Natural Language Generation for Advertising: A Survey](https://mlsanity.com/paper/2306.12719/) (2023)
- [Staging E-Commerce Products for Online Advertising using Retrieval Assisted Image Generation](https://mlsanity.com/paper/2307.15326/) (2023)
- [AdParaphrase: Paraphrase Dataset for Analyzing Linguistic Features toward Generating Attractive Ad Texts](https://mlsanity.com/paper/2502.04674/) (2025)
- [TSI: an Ad Text Strength Indicator using Text-to-CTR and Semantic-Ad-Similarity](https://mlsanity.com/paper/2108.08226/) (2021)
- [AdTEC: A Unified Benchmark for Evaluating Text Quality in Search Engine Advertising](https://mlsanity.com/paper/2408.05906/) (2024)
- [RePainter: Empowering E-commerce Object Removal via Spatial-matting Reinforcement Learning](https://mlsanity.com/paper/2510.07721/) (2025)
- [AdParaphrase v2.0: Generating Attractive Ad Texts Using a Preference-Annotated Paraphrase Dataset](https://mlsanity.com/paper/2505.20826/) (2025)
- [Toward Improving Coherence and Diversity of Slogan Generation](https://mlsanity.com/paper/2102.05924/) (2021)
- [Generative Modeling with Multi-Instance Reward Learning for E-commerce Creative Optimization](https://mlsanity.com/paper/2508.09730/) (2025)
- [Experimentation Accelerator: Interpretable Insights and Creative Recommendations for A/B Testing with Content-Aware ranking](https://mlsanity.com/paper/2602.13852/) (2026)
- [SINGA-Easy: An Easy-to-Use Framework for MultiModal Analysis](https://mlsanity.com/paper/2108.02572/) (2021)
- [VisualTextRank: Unsupervised Graph-based Content Extraction for Automating Ad Text to Image Search](https://mlsanity.com/paper/2108.02725/) (2021)
- [Text is NOT Enough: Integrating Visual Impressions into Open-domain Dialogue Generation](https://mlsanity.com/paper/2109.05778/) (2021)
- [Discovering Personalized Semantics for Soft Attributes in Recommender Systems using Concept Activation Vectors](https://mlsanity.com/paper/2202.02830/) (2022)
- [Reinforced MOOCs Concept Recommendation in Heterogeneous Information Networks](https://mlsanity.com/paper/2203.11011/) (2022)
- [Explanation-Guided Fairness Testing through Genetic Algorithm](https://mlsanity.com/paper/2205.08335/) (2022)
- [Using Graph Neural Networks for Program Termination](https://mlsanity.com/paper/2207.14648/) (2022)
- [Tribe or Not? Critical Inspection of Group Differences Using TribalGram](https://mlsanity.com/paper/2303.09664/) (2023)
- [Patch-enhanced Mask Encoder Prompt Image Generation](https://mlsanity.com/paper/2405.19085/) (2024)
- [NextAds: Towards Next-generation Personalized Video Advertising](https://mlsanity.com/paper/2603.02137/) (2026)
- [AgenticGen: Reward-Guided Agentic Video Generation for Advertising](https://mlsanity.com/paper/2609.09187/) (2026)
- [PinDCO: Whole-Page Aware Dynamic Creative Optimization at Scale](https://mlsanity.com/paper/2609.11943/) (2026)
- [I Am AdMan: A Pipeline for Automatic Generation of Personalized Advertising Imagery](https://mlsanity.com/paper/2609.12694/) (2026)
- [Evolving Creatives with Clicks: CTR-Driven Advertising image Editing via Attribute Disentanglement and Preference Alignment](https://mlsanity.com/paper/2610.13066/) (2026)

## Abstract (arXiv)

In the online advertising industry, the process of designing an ad creative (i.e., ad text and image) requires manual labor. Typically, each advertiser launches multiple creatives via online A/B tests to infer effective creatives for the target audience, that are then refined further in an iterative fashion. Due to the manual nature of this process, it is time-consuming to learn, refine, and deploy the modified creatives. Since major ad platforms typically run A/B tests for multiple advertisers in parallel, we explore the possibility of collaboratively learning ad creative refinement via A/B tests of multiple advertisers. In particular, given an input ad creative, we study approaches to refine the given ad text and image by: (i) generating new ad text, (ii) recommending keyphrases for new ad text, and (iii) recommending image tags (objects in image) to select new ad image. Based on A/B tests conducted by multiple advertisers, we form pairwise examples of inferior and superior ad creatives, and use such pairs to train models for the above tasks. For generating new ad text, we demonstrate the efficacy of an encoder-decoder architecture with copy mechanism, which allows some words from the (inferior) input text to be copied to the output while incorporating new words associated with higher click-through-rate. For the keyphrase and image tag recommendation task, we demonstrate the efficacy of a deep relevance matching model, as well as the relative robustness of ranking approaches compared to ad text generation in cold-start scenarios with unseen advertisers. We also share broadly applicable insights from our experiments using data from the Yahoo Gemini ad platform.