论文标题
通过多模式多任务神经生存网络进行广告创意停止预测
Ad Creative Discontinuation Prediction with Multi-Modal Multi-Task Neural Survival Networks
论文作者
论文摘要
在适当的时间停止广告创意是可能对销售产生重大影响的最重要的广告操作之一。对无效广告的这种操作支持的探索率较低，而不是有效的广告。在预先分析了1,000,000个现实世界的广告创意之后，我们发现停药有两种类型：短期（即切出）和长期（即磨损）。在本文中，我们提出了一个实用的预测框架，用于以基于危险功能的损失功能启发出生存分析的基于危险功能的损失功能。我们的框架通过多模式深神经网络预测了停产，该网络将广告创意（例如文本，分类，图像，数值特征）作为输入。为了提高两种不同类型的中断类型的预测性能以及为销售做出贡献的广告创意，我们介绍了两种新技术：（1）具有多任务学习和（2）损失功能的两次估算技术和（2）（2）（2）损失功能的点击率加权技术。我们使用大型广告创意数据集评估了我们的框架，其中包括100亿个标准印象。就一致性指数而言（简短：0.896，长：0.939和总体：0.792），我们的框架的性能明显优于常规方法（0.531）。此外，我们确认我们的框架（i）表现出与短期案例的手动操作相同程度的中断效果，并且（ii）准确地预测了AD停用命令，这对于长期案例长期运行的广告创作很重要。
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.
