Two-stage Auction Design in Online Advertising
Track: Economics, online markets and human computation
Keywords: Mechanism design, Online advertising, Neural networks
Abstract: Modern online advertising systems usually involve a large amount of advertisers in each auction, causing scalability issues. To mitigate the problem, two-stage auctions are designed and deployed in practice, enabling efficient allocations of ad slots among numerous candidate advertisers within a short response time. Such a design uses a fast but coarse model to select a small subset of advertisers in the first stage, and a slow yet refined model to finally decide the winners. However, existing two-stage auction mechanisms primarily focus on optimizing welfare, ignoring other crucial objectives of the platform, such as revenue.
In this paper, we propose ad-wise selection metrics (namely Max-Wel and Max-Rev) that are based on an ad's contribution to the platform's objective (welfare or revenue). Then we provide theoretical guarantees for the proposed metrics. Our method is applicable to both welfare and revenue optimizations and can be easily implemented using neural networks. We conduct extensive experiments on both synthetic and industrial data to demonstrate the advantages of our proposed selection metrics over existing baselines.
Submission Number: 2370
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