# Fatigue-Aware Ad Creative Selection

arXiv: https://arxiv.org/abs/1908.08936 · HTML: https://mlsanity.com/paper/1908.08936/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Aug 2019.

## Built on by (most cited first, 4 of 4)

- [A Real-World Implementation of Unbiased Lift-based Bidding System](https://mlsanity.com/paper/2202.13868/) (2022)
- [Creative4U: MLLMs-based Advertising Creative Image Selector with Comparative Reasoning](https://mlsanity.com/paper/2508.12628/) (2025)
- [Ad Creative Discontinuation Prediction with Multi-Modal Multi-Task Neural Survival Networks](https://mlsanity.com/paper/2204.11588/) (2022)
- [State-Separated SARSA: A Practical Sequential Decision-Making Algorithm with Recovering Rewards](https://mlsanity.com/paper/2403.11520/) (2024)

## Abstract (arXiv)

In online display advertising, selecting the most effective ad creative (ad image) for each impression is a crucial task for DSPs (Demand-Side Platforms) to fulfill their goals (click-through rate, number of conversions, revenue, and brand improvement). As widely recognized in the marketing literature, the effect of ad creative changes with the number of repetitive ad exposures. In this study, we propose an efficient and easy-to-implement ad creative selection algorithm that explicitly considers user's psychological status when selecting ad creatives. The proposed system was deployed in a real-world production environment and tested against the baseline algorithms. The results show superiority of the proposed algorithm.