"Creativity is the heart and soul of advertising services". Effective creatives can create a win-win scenario: advertisers can reach target users and achieve marketing objectives more effectively, users can more quickly find products of interest, and platforms can generate more advertising revenue. With the advent of AI-Generated Content, advertisers now can produce vast amounts of creative content at a minimal cost. The current challenge lies in how advertising systems can select the most pertinent creative in real-time for each user personally. Existing methods typically perform serial ranking of ads or creatives, limiting the creative module in terms of both effectiveness and efficiency. In this paper, we propose for the first time a novel architecture for online parallel estimation of ads and creatives ranking, as well as the corresponding offline joint optimization model. The online architecture enables sophisticated personalized creative modeling while reducing overall latency. The offline joint model for CTR estimation allows mutual awareness and collaborative optimization between ads and creatives. Additionally, we optimize the offline evaluation metrics for the implicit feedback sorting task involved in ad creative ranking. We conduct extensive experiments to compare ours with two state-of-the-art approaches. The results demonstrate the effectiveness of our approach in both offline evaluations and real-world advertising platforms online in terms of response time, CTR, and CPM.

翻译：“创意是广告服务的核心与灵魂”。有效的创意能够实现三方共赢：广告主可以更精准地触达目标用户并达成营销目标，用户能够更快找到感兴趣的产品，平台也能获得更多广告收入。随着人工智能生成内容（AI-Generated Content）技术的兴起，广告主现在能够以极低成本生成海量创意内容。当前面临的挑战在于：广告系统如何为每位用户实时选取最恰当的个性化创意。现有方法通常对广告或创意进行串行排序，导致创意模块在效果和效率上均受到制约。本文首次提出一种用于在线并行评估广告与创意排序的新型架构，以及相应的离线联合优化模型。在线架构在降低整体延迟的同时，实现了复杂的个性化创意建模；用于点击率（CTR）预估的离线联合模型则使广告与创意能够相互感知并协同优化。此外，我们还针对广告创意排序涉及的隐式反馈排序任务优化了离线评估指标。我们开展了大量实验，将本方法与两种当前最优方法进行对比。结果表明，本方法在离线评估和真实广告平台的在线响应时间、点击率（CTR）及千次展示成本（CPM）方面均具有显著有效性。