Dynamic Creative Optimization in Online Display Advertising
Recently I’ve been researching creative-related content. Based on current investigation, I divide this field into three main parts: creative generation, creative selection, and creative delivery, with specific responsibilities as follows:
- Creative generation: Using materials (titles, images, videos, landing pages, etc.) to generate candidate creatives (the ads users see)
- Creative selection: Selecting top-k creatives from a campaign’s candidates (a campaign typically has multiple candidates) for delivery
- Creative delivery: Delivering the selected creatives online
Strictly speaking, these three parts are not clearly demarcated. For example, the first two can be unified as creative generation (generating final delivery creatives from raw materials), and the latter two can be unified as creative delivery (selecting from candidates and delivering online).
This article mainly focuses on a paper related to creative delivery, particularly the third part (without E&E-based selection). The paper title is Dynamic Creative Optimization in Online Display Advertising. This paper models the online delivery problem as a bipartite graph matching problem, providing both exact and approximate online solutions. More importantly, this modeling approach is not limited to the creative domain and can be applied to many delivery scenarios.