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ACQ: A Deployed Two-Stage Framework for Automated Creative Quota Allocation in Large-Scale Online Advertising

arXiv 2412.06167 ↗First posted 9 Dec 2024cs.AIcited by 0

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Abstract (arXiv)

In digital advertising, demand-side platforms (DSPs) allow advertisers to create multiple ad creatives from a single photo for real-time bidding. While increasing the number of creatives can improve bidding opportunities, it cannot scale indefinitely, and the incremental advertising revenue typically exhibits diminishing returns as more creatives are generated. This raises a practical problem for DSPs: how to automatically determine an appropriate creative quota for each photo at scale. To address this problem, we propose Automated Creatives Quota (ACQ), a deployed two-stage framework for creative quota allocation in large-scale online advertising. In the first stage, ACQ predicts quota-conditioned expected revenue using a multi-task model built on an unbalanced binary tree, which is designed to handle the highly skewed revenue distribution across quota levels. In the second stage, ACQ formulates quota allocation under global capacity constraints as a multiple-choice knapsack problem (MCKP) and solves it with an efficient dual-based algorithm. Extensive offline experiments and online experiments on Kuaishou's advertising delivery platform demonstrate the effectiveness of ACQ, achieving a 6.20% increase in platform advertising revenue.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 17 of its 31 references that are in the map. First posted on arXiv 9 Dec 2024.

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References · 17

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5 more
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