Multi-Objective Actor-Critics for Real-Time Bidding in Display Advertising

Haolin Zhou, Chaoqi Yang, Xiaofeng Gao, Qiong Chen, Gongshen Liu, Guihai Chen

ECML-PKDD 2022 pp. 20-37

doi:10.1007/978-3-031-26412-2_2 /ecmlpkdd/2022/zhou2022ecmlpkdd-multiobjective/

Abstract

Online Real-Time Bidding (RTB) is a complex auction game among which advertisers struggle to bid for ad impressions when a user request occurs. Considering display cost, Return on Investment (ROI), and other influential Key Performance Indicators (KPIs), large ad platforms try to balance the trade-off among various goals in dynamics. To address the challenge, we propose a Multi-ObjecTive Actor-Critics algorithm based on reinforcement learning (RL), named MoTiAC, for the problem of bidding optimization with various goals. In MoTiAC, objective-specific agents update the global network asynchronously with different goals and perspectives, leading to a robust bidding policy. Unlike previous RL models, the proposed MoTiAC can simultaneously fulfill multi-objective tasks in complicated bidding environments. In addition, we mathematically prove that our model will converge to Pareto optimality. Finally, experiments on a large-scale real-world commercial dataset from Tencent verify the effectiveness of MoTiAC versus a set of recent approaches

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Cite

Text

Zhou et al. "Multi-Objective Actor-Critics for Real-Time Bidding in Display Advertising." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2022. doi:10.1007/978-3-031-26412-2_2

Markdown

[Zhou et al. "Multi-Objective Actor-Critics for Real-Time Bidding in Display Advertising." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2022.](https://mlanthology.org/ecmlpkdd/2022/zhou2022ecmlpkdd-multiobjective/) doi:10.1007/978-3-031-26412-2_2

BibTeX

@inproceedings{zhou2022ecmlpkdd-multiobjective,
  title     = {{Multi-Objective Actor-Critics for Real-Time Bidding in Display Advertising}},
  author    = {Zhou, Haolin and Yang, Chaoqi and Gao, Xiaofeng and Chen, Qiong and Liu, Gongshen and Chen, Guihai},
  booktitle = {European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases},
  year      = {2022},
  pages     = {20-37},
  doi       = {10.1007/978-3-031-26412-2_2},
  url       = {https://mlanthology.org/ecmlpkdd/2022/zhou2022ecmlpkdd-multiobjective/}
}