Two-stage Auction Design in Online Advertising

摘要

Modern online advertising systems often involve a substantial number of advertisers in each auction, which results in scalability issues. To address this challenge, two-stage auctions have been designed and implemented in practice. These auctions enable efficient allocation of ad slots among numerous candidate advertisers in a short response time. This approach employs a fast yet coarse model in the first stage to select a small subset of advertisers, followed by a slow, more refined model to determine the final winners. However, existing two-stage auction mechanisms primarily focus on optimizing welfare, overlooking other critical objectives of the platform, such as revenue. In this paper, we propose ad-wise selection metrics, named Max-Wel and Max-Rev, which optimize the platform's welfare and revenue, respectively. These metrics are based on each ad's contribution to the corresponding objective function. We also 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. Through extensive experiments conducted on both synthetic and industrial data, we demonstrate the advantages of our proposed selection metrics compared to existing baselines.