Cross-Element Combinatorial Selection for Multi-Element Creative in
  Display Advertising
Wei Zhang Ping Zhang Jian Dong Yongkang Wang Pengye Zhang Bo Zhang Xingxing Wang Dong Wang
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.