ACQ: A Unified Framework for Automated Programmatic Creativity in Online Advertising

TL;DR

This paper introduces ACQ, a two-stage framework that automates ad creative management in online advertising, optimizing creative quotas to maximize platform revenue through predictive modeling and efficient allocation strategies.

Contribution

The paper presents a novel unified framework combining cost prediction and quota allocation, including a multi-task learning model and a large-scale MCKP solver, for automated creative management in DSPs.

Findings

Increased ad platform revenue by 9.34% through ACQ.

Effective mitigation of target imbalance with a multi-task learning model.

Scalable solution handling tens of millions of ads.

Abstract

In online advertising, the demand-side platform (a.k.a. DSP) enables advertisers to create different ad creatives for real-time bidding. Intuitively, advertisers tend to create more ad creatives for a single photo to increase the probability of participating in bidding, further enhancing their ad cost. From the perspective of DSP, the following are two overlooked issues. On the one hand, the number of ad creatives cannot grow indefinitely. On the other hand, the marginal effects of ad cost diminish as the number of ad creatives increases. To this end, this paper proposes a two-stage framework named Automated Creatives Quota (ACQ) to achieve the automatic creation and deactivation of ad creatives. ACQ dynamically allocates the creative quota across multiple advertisers to maximize the revenue of the ad platform. ACQ comprises two components: a prediction module to estimate the cost of a photo under different numbers of ad creatives, and an allocation module to decide the quota for photos considering their estimated costs in the prediction module. Specifically, in the prediction module, we develop a multi-task learning model based on an unbalanced binary tree to effectively mitigate the target variable imbalance problem. In the allocation module, we formulate the quota allocation problem as a multiple-choice knapsack problem (MCKP) and develop an efficient solver to solve such large-scale problems involving tens of millions of ads. We performed extensive offline and online experiments to validate the superiority of our proposed framework, which increased cost by 9.34%.