A Pareto optimal mechanism for demand-side platforms in real time bidding advertising markets
Qin, Rui1,2,4; Yuan, Yong1,2; Wang, Fei-Yue1,2,3
2018-12
发表期刊Information Sciences
卷号469期号:NA页码:119-140
摘要Real time bidding (RTB) advertising has been widely recognized as one of the most promising big-data-driven business models, and a fast-growing research field of computational advertising in recent years. In RTB markets, each ad impression is sold through a two-stage resale auction session, in which demand side platforms (DSPs) play an important role as intermediators. Specifically, DSPs buy ad impressions from the Ad Exchange (AdX) platform and resell them to their registered advertisers, who are interested in the target audience behind the ad impressions. The mechanism design of this two-stage resale auction is a hot research topic and also a critical component in maintaining the effectiveness and efficiency of the RTB ecosystems. In this paper, we strive to identify and design a new mechanism for this auction model in stochastic market environments, with the aim of maximizing the total expected revenue of the winning advertiser and the DSP, and improving the expected revenues for both the winning advertiser and the DSP from each ad impression. Our proposed new mechanism is Pareto optimal for the advertisers and DSPs. We study the equivalent forms of our proposed mechanism in cases when the stochastic market environments can be characterized by uniformly or normally distributed random variables, respectively. We also validate our auction mechanism using the computational experiment approach. The experimental results indicate that our mechanism can make both advertisers and DSPs better off. Our work is expected to provide useful managerial insights for DSPs in RTB market practice.; Real time bidding (RTB) advertising has been widely recognized as one of the most promising big-data-driven business models, and a fast-growing research field of computational advertising in recent years. In RTB markets, each ad impression is sold through a two-stage resale auction session, in which demand side platforms (DSPs) play an important role as intermediators. Specifically, DSPs buy ad impressions from the Ad Exchange (AdX) platform and resell them to their registered advertisers, who are interested in the target audience behind the ad impressions. The mechanism design of this two-stage resale auction is a hot research topic and also a critical component in maintaining the effectiveness and efficiency of the RTB ecosystems. In this paper, we strive to identify and design a new mechanism for this auction model in stochastic market environments, with the aim of maximizing the total expected revenue of the winning advertiser and the DSP, and improving the expected revenues for both the winning advertiser and the DSP from each ad impression. Our proposed new mechanism is Pareto optimal for the advertisers and DSPs. We study the equivalent forms of our proposed mechanism in cases when the stochastic market environments can be characterized by uniformly or normally distributed random variables, respectively. We also validate our auction mechanism using the computational experiment approach. The experimental results indicate that our mechanism can make both advertisers and DSPs better off. Our work is expected to provide useful managerial insights for DSPs in RTB market practice.
关键词Computational Advertising Real Time Bidding Demand Side Platform Pareto Optimal Mechanism Design Computational Experiment
DOI10.1016/j.ins.2018.08.012
收录类别SCI
引用统计
文献类型期刊论文
条目标识符http://ir.ia.ac.cn/handle/173211/21607
专题复杂系统管理与控制国家重点实验室_先进控制与自动化
通讯作者Wang, Fei-Yue
作者单位1.The State Key Laboratory for Management and Control of Complex Systems, Institute of Automation, Chinese Academy of Sciences
2.Qingdao Academy of Intelligent Industries
3.Research Center of Military Computational Experiments and Parallel System, National University of Defense Technology
4.Beijing Engineering Research Center of Intelligent Systems and Technology, Institute of Automation, Chinese Academy of Sciences
推荐引用方式
GB/T 7714
Qin, Rui,Yuan, Yong,Wang, Fei-Yue. A Pareto optimal mechanism for demand-side platforms in real time bidding advertising markets[J]. Information Sciences,2018,469(NA):119-140.
APA Qin, Rui,Yuan, Yong,&Wang, Fei-Yue.(2018).A Pareto optimal mechanism for demand-side platforms in real time bidding advertising markets.Information Sciences,469(NA),119-140.
MLA Qin, Rui,et al."A Pareto optimal mechanism for demand-side platforms in real time bidding advertising markets".Information Sciences 469.NA(2018):119-140.
条目包含的文件
文件名称/大小 文献类型 版本类型 开放类型 使用许可
INS-qin.pdf(1804KB)期刊论文作者接受稿开放获取CC BY-NC-SA浏览 请求全文
个性服务
推荐该条目
保存到收藏夹
查看访问统计
导出为Endnote文件
谷歌学术
谷歌学术中相似的文章
[Qin, Rui]的文章
[Yuan, Yong]的文章
[Wang, Fei-Yue]的文章
百度学术
百度学术中相似的文章
[Qin, Rui]的文章
[Yuan, Yong]的文章
[Wang, Fei-Yue]的文章
必应学术
必应学术中相似的文章
[Qin, Rui]的文章
[Yuan, Yong]的文章
[Wang, Fei-Yue]的文章
相关权益政策
暂无数据
收藏/分享
文件名: INS-qin.pdf
格式: Adobe PDF
所有评论 (0)
暂无评论
 

除非特别说明,本系统中所有内容都受版权保护,并保留所有权利。