The effectiveness of ad creatives is greatly influenced by their visual
appearance. Advertising platforms can generate ad creatives with different
appearances by combining creative elements provided by advertisers. However,
with the increasing number of ad creative elements, it becomes challenging to
select a suitable combination from the countless possibilities. The industry's
mainstream approach is to select individual creative elements independently,
which often overlooks the importance of interaction between creative elements
during the modeling process. In response, this paper proposes a Cross-Element
Combinatorial Selection framework for multiple creative elements, termed CECS.
In the encoder process, a cross-element interaction is adopted to dynamically
adjust the expression of a single creative element based on the current
candidate creatives. In the decoder process, the creative combination problem
is transformed into a cascade selection problem of multiple creative elements.
A pointer mechanism with a cascade design is used to model the associations
among candidates. Comprehensive experiments on real-world datasets show that
CECS achieved the SOTA score on offline metrics. Moreover, the CECS algorithm
has been deployed in our industrial application, resulting in a significant
6.02% CTR and 10.37% GMV lift, which is beneficial to the business.

本文提出一种名为 CECS 的方法，通过交叉元素组合选择框架来解决广告创意元素选择过程中的挑战，它将创意组合问题转化为多个创意元素的级联选择问题，并使用指针机制进行建模和优化，实验表明 CECS 在实践中取得了最佳的表现效果。

显示广告中多元创意的跨元素组合选择

Cross-Element Combinatorial Selection for Multi-Element Creative in  Display Advertising

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.

本文提出了一种基于深度神经网络，并以生存分析为灵感的 hazard function-based loss function 的实用预测框架，用于预测广告创意的中止，并成功地将其与传统方法进行了比较和验证。