# Ad Creative Discontinuation Prediction with Multi-Modal Multi-Task Neural Survival Networks

arXiv: https://arxiv.org/abs/2204.11588 · HTML: https://mlsanity.com/paper/2204.11588/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 37 references that are in the map.
- First posted on arXiv 2 Apr 2022.

## Builds on (most cited first, 10 of 10)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Entity Embeddings of Categorical Variables](https://mlsanity.com/paper/1604.06737/) (2016)
- [A Scalable Discrete-Time Survival Model for Neural Networks](https://mlsanity.com/paper/1805.00917/) (2018)
- [Bid Optimization by Multivariable Control in Display Advertising](https://mlsanity.com/paper/1905.10928/) (2019)
- [Deep CTR Prediction in Display Advertising](https://mlsanity.com/paper/1609.06018/) (2016)
- [Bidding Machine: Learning to Bid for Directly Optimizing Profits in Display Advertising](https://mlsanity.com/paper/1803.02194/) (2018)
- [Survival analysis as a classification problem](https://mlsanity.com/paper/1909.11171/) (2019)
- [Conversion Prediction Using Multi-task Conditional Attention Networks to Support the Creation of Effective Ad Creative](https://mlsanity.com/paper/1905.07289/) (2019)
- [Fatigue-Aware Ad Creative Selection](https://mlsanity.com/paper/1908.08936/) (2019)

## Built on by (most cited first, 1 of 1)

- [Improving Prediction Performance and Model Interpretability through Attention Mechanisms from Basic and Applied Research Perspectives](https://mlsanity.com/paper/2303.14116/) (2023)

## Abstract (arXiv)

Discontinuing ad creatives at an appropriate time is one of the most important ad operations that can have a significant impact on sales. Such operational support for ineffective ads has been less explored than that for effective ads. After pre-analyzing 1,000,000 real-world ad creatives, we found that there are two types of discontinuation: short-term (i.e., cut-out) and long-term (i.e., wear-out). In this paper, we propose a practical prediction framework for the discontinuation of ad creatives with a hazard function-based loss function inspired by survival analysis. Our framework predicts the discontinuations with a multi-modal deep neural network that takes as input the ad creative (e.g., text, categorical, image, numerical features). To improve the prediction performance for the two different types of discontinuations and for the ad creatives that contribute to sales, we introduce two new techniques: (1) a two-term estimation technique with multi-task learning and (2) a click-through rate-weighting technique for the loss function. We evaluated our framework using the large-scale ad creative dataset, including 10 billion scale impressions. In terms of the concordance index (short: 0.896, long: 0.939, and overall: 0.792), our framework achieved significantly better performance than the conventional method (0.531). Additionally, we confirmed that our framework (i) demonstrated the same degree of discontinuation effect as manual operations for short-term cases, and (ii) accurately predicted the ad discontinuation order, which is important for long-running ad creatives for long-term cases.