Summary of Acq: a Unified Framework For Automated Programmatic Creativity in Online Advertising, by Ruizhi Wang et al.
ACQ: A Unified Framework for Automated Programmatic Creativity in Online Advertising
by Ruizhi Wang, Kai Liu, Bingjie Li, Yu Rong, Qingpeng Cai, Fei Pan, Peng Jiang
First submitted to arxiv on: 9 Dec 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes a two-stage framework called Automated Creatives Quota (ACQ) for demand-side platforms (DSPs) in online advertising. The ACQ framework dynamically allocates creative quotas across multiple advertisers to maximize ad platform revenue. The framework consists of a prediction module that estimates the cost of a photo under different numbers of ad creatives, and an allocation module that decides the quota for photos considering their estimated costs. The prediction module uses a multi-task learning model based on an unbalanced binary tree to mitigate the target variable imbalance problem. The allocation module formulates the quota allocation problem as a multiple-choice knapsack problem (MCKP) and develops an efficient solver to solve large-scale problems involving tens of millions of ads. The authors conducted extensive offline and online experiments, showing that their proposed framework increased cost by 9.34%. The ACQ framework can help DSPs optimize ad creative production, reducing costs while maintaining revenue. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine you’re an advertiser trying to get your message out on the internet. You create lots of different ads with pictures and text to try to get people’s attention. But this approach has a problem – it can be expensive! To solve this issue, researchers created a new system called Automated Creatives Quota (ACQ). ACQ helps decide how many different ads to create for each picture, so that you’re not wasting money. The system uses special algorithms and models to figure out which ads will work best and when to stop creating more. In tests, the ACQ system was able to increase revenue by 9.34% while keeping costs under control. This new approach can help online advertisers be more efficient and effective in their advertising efforts. |
Keywords
» Artificial intelligence » Attention » Multi task
