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Algorithm Research & Explore
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App. Res. of Comp. Printed Article
Vol. 39, 2022 No. 2
461-467

Research on policy optimization algorithm based on probability distance with penalized point in real-time bidding of display advertising

Li Wenquan1
Qi Qi1
Li Ni2
Liu Yongna3
1. College of Computer Science & Technology, Hainan University, Haikou 570228, China
2. College of Mathematics & Statistics, Hainan Normal University, Haikou 571158, China
3. College of Applied Science & Technology, Hainan University, Danzhou Hainan 571737, China
DOI: 10.19734/j.issn.1001-3695.2021.07.0264

Abstract

Real-time bidding(RTB) is a widely used advertising mode in online display advertising. In response to the problem that the best bidding strategy is difficult to obtain due to the high dynamics of the RTB auction environment, this paper proposed a method of optimizing bid strategy based on reinforcement learning(RL), which used the policy optimization with penalized point probability distance(POP3D) algorithm to learn the best bidding strategy. In the POP3D-based bidding framework, the advertising bidding process was modeled as an episodic Markov decision process. Each epiosode contained a fixed number of time steps, and the bid for each ad display was determined by its estimated click-through rate and bidding factors were jointly determined. At each time step, the bidding agent would adjust the bidding factors according to the auction situation at the previous time step, so that the bidding strategy could adapt to the highly dynamic auction environment. The goal of the bidding agent was to learn the best bidding factor adjustment strategy. The experimental results on the iPinYou data set demonstrate that compared with the DRLB algorithm, the proposed bidding algorithm increases the number of clicks by 0.2% when the budget ratio is 1/16 and 1/32, as well as increased the win rate by 1.8%, 1.0% and 1.7% when the budget ratio is were 1/8, 1/16 and 1/32. In addition, the proposed method also has advantages in terms of stability. It shows the superiority of this method.

Foundation Support

国家自然科学基金资助项目(11861030)
国家重点研发计划资助项目(2018YFB1404400)
海南省自然科学基金资助项目(2019RC176)
教育部第49批留学回国人员科研启动基金资助项目(2015-311)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2021.07.0264
Publish at: Application Research of Computers Printed Article, Vol. 39, 2022 No. 2
Section: Algorithm Research & Explore
Pages: 461-467
Serial Number: 1001-3695(2022)02-023-0461-07

Publish History

[2021-09-18] Accepted Paper
[2022-02-05] Printed Article

Cite This Article

李文权, 齐琦, 李霓, 等. 基于带惩罚的点概率距离策略优化算法在展示广告实时竞标中的研究 [J]. 计算机应用研究, 2022, 39 (2): 461-467. (Li Wenquan, Qi Qi, Li Ni, et al. Research on policy optimization algorithm based on probability distance with penalized point in real-time bidding of display advertising [J]. Application Research of Computers, 2022, 39 (2): 461-467. )

About the Journal

  • Application Research of Computers Monthly Journal
  • Journal ID ISSN 1001-3695
    CN  51-1196/TP

Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

Aiming at the urgently needed cutting-edge technology in this discipline, Application Research of Computers reflects the mainstream technology, hot technology and the latest development trend of computer application research at home and abroad in a timely manner. The main contents of the journal include high-level academic papers in this discipline, the latest scientific research results and major application results. The contents of the columns involve new theories of computer discipline, basic computer theory, algorithm theory research, algorithm design and analysis, blockchain technology, system software and software engineering technology, pattern recognition and artificial intelligence, architecture, advanced computing, parallel processing, database technology, computer network and communication technology, information security technology, computer image graphics and its latest hot application technology.

Application Research of Computers has many high-level readers and authors, and its readers are mainly senior and middle-level researchers and engineers engaged in the field of computer science, as well as teachers and students majoring in computer science and related majors in colleges and universities. Over the years, the total citation frequency and Web download rate of Application Research of Computers have been ranked among the top of similar academic journals in this discipline, and the academic papers published are highly popular among the readers for their novelty, academics, foresight, orientation and practicality.

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