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
Source PublicationINFORMATION SCIENCES
ISSN0020-0255
2018-12-01
Volume469Pages:119-140
Corresponding AuthorWang, Fei-Yue(feiyue.wang@ia.ac.cn)
AbstractReal 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. (C) 2018 Elsevier Inc. All rights reserved.
KeywordComputational advertising Real time bidding Demand side platform Pareto optimal Mechanism design Computational experiment
DOI10.1016/j.ins.2018.08.012
WOS KeywordSPONSORED SEARCH AUCTIONS ; FRAMEWORK ; KEYWORDS ; DESIGN
Indexed BySCI
Language英语
Funding ProjectNational Natural Science Foundation of China[71702182] ; National Natural Science Foundation of China[71472174] ; National Natural Science Foundation of China[61533019] ; National Natural Science Foundation of China[71232006]
Funding OrganizationNational Natural Science Foundation of China
WOS Research AreaComputer Science
WOS SubjectComputer Science, Information Systems
WOS IDWOS:000448229800008
PublisherELSEVIER SCIENCE INC
Citation statistics
Cited Times:1[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/21607
Collection复杂系统管理与控制国家重点实验室_先进控制与自动化
Corresponding AuthorWang, Fei-Yue
Affiliation1.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
Recommended Citation
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,2018,469:119-140.
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,469,119-140.
MLA Qin, Rui,et al."A Pareto optimal mechanism for demand-side platforms in real time bidding advertising markets".INFORMATION SCIENCES 469(2018):119-140.
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