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.

翻译：广告创意的视觉外观对其效果具有重要影响。广告平台可通过组合广告主提供的创意元素生成不同视觉外观的广告创意。然而，随着广告创意元素数量的增加，从海量可能组合中选取合适组合变得极具挑战性。业界主流方法是对各创意元素进行独立选择，这往往忽视了建模过程中创意元素间交互的重要性。为此，本文提出一种面向多创意元素的跨元素组合选择框架CECS。在编码过程中，采用跨元素交互机制，基于当前候选创意动态调整单个创意元素的表征。在解码过程中，将创意组合问题转化为多创意元素的级联选择问题，通过带级联设计的指针机制建模候选元素间的关联关系。在真实数据集上的综合实验表明，CECS在离线指标上达到最优性能。此外，该算法已部署至工业应用系统，实现了6.02%的CTR提升和10.37%的GMV增长，显著提升了业务收益。