Simultaneous Optimization of Bid Shading and Internal Auction for Demand-Side Platforms

Authors

  • Yadong Xu Institute for Interdisciplinary Information Sciences, Tsinghua University
  • Bonan Ni Institute for Interdisciplinary Information Sciences, Tsinghua University
  • Weiran Shen Gaoling School of Artificial Intelligence, Renmin University of China
  • Xun Wang Institute for Interdisciplinary Information Sciences, Tsinghua University
  • Zichen Wang ByteDance
  • Yinsong Xue ByteDance
  • Pingzhong Tang Institute for Interdisciplinary Information Sciences, Tsinghua University

DOI:

https://doi.org/10.1609/aaai.v38i9.28855

Keywords:

GTEP: Auctions and Market-Based Systems, GTEP: Mechanism Design, MAS: Mechanism Design

Abstract

Online advertising has been one of the most important sources for industry's growth, where the demand-side platforms (DSP) play an important role via bidding to the ad exchanges on behalf of their advertiser clients. Since more and more ad exchanges have shifted from second to first price auctions, it is challenging for DSPs to adjust bidding strategy in the volatile environment. Recent studies on bid shading in first-price auctions may have limited performance due to relatively strong hypotheses about winning probability distribution. Moreover, these studies do not consider the incentive of advertiser clients, which can be crucial for a reliable advertising platform. In this work, we consider both the optimization of bid shading technique and the design of internal auction which is ex-post incentive compatible (IC) for the management of a DSP. Firstly, we prove that the joint design of bid shading and ex-post IC auction can be reduced to choosing one monotone bid function for each advertiser without loss of optimality. Then we propose a parameterized neural network to implement the monotone bid functions. With well-designed surrogate loss, the objective can be optimized in an end-to-end manner. Finally, our experimental results demonstrate the effectiveness and superiority of our algorithm.
AAAI-24 / IAAI-24 / EAAI-24 Proceedings Cover

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Published

2024-03-24

How to Cite

Xu, Y., Ni, B., Shen, W., Wang, X., Wang, Z., Xue, Y., & Tang, P. (2024). Simultaneous Optimization of Bid Shading and Internal Auction for Demand-Side Platforms. Proceedings of the AAAI Conference on Artificial Intelligence, 38(9), 9935-9943. https://doi.org/10.1609/aaai.v38i9.28855

Issue

Vol. 38 No. 9: AAAI-24 Technical Tracks 9

Section

AAAI Technical Track on Game Theory and Economic Paradigms