双销售渠道中显示广告印象的动态分配

IF 6.7 2区 管理学 Q1 MANAGEMENT Omega-international Journal of Management Science Pub Date : 2024-10-24 DOI:10.1016/j.omega.2024.103213
{"title":"双销售渠道中显示广告印象的动态分配","authors":"","doi":"10.1016/j.omega.2024.103213","DOIUrl":null,"url":null,"abstract":"<div><div>We study a multi-period ad allocation problem faced by an online publisher who sells ad impressions on websites through two sales channels. In the guaranteed sales channel, advertisers submit heterogeneous offers for contracts under which the publisher guarantees delivery of a certain number of ad impressions over a certain period; in the real-time bidding (RTB) sales channel, the publisher runs an RTB auction to sell ad impressions. In each period, the publisher decides whether to accept or reject contract proposals; how to allocate ad impressions across existing contracts; and how many impressions to sell via RTB. The publisher faces uncertain demand from advertisers and an uncertain supply of impressions, which are generated by viewers visiting the publisher’s websites. We formulate the problem as a finite-horizon stochastic dynamic program, which poses significant methodological challenges. We first present structural properties of optimal policies under certain cases. To avoid the curse of dimensionality in dynamic programming, we develop an approach involving Lagrangian relaxations. We decompose the problem into a series of solvable subproblems and derive optimal policies. We further develop Lagrangian policies with performance guarantees. We show that when Lagrange multipliers depend on more signal history, the linear term’s weight of the number of contract types in the performance upper bound decreases. Furthermore, if the Lagrange multipliers depend on the full signal history, the corresponding Lagrangian policies will be asymptotically optimal to the number of contract types. We also explore a more suitable case for large-scale real-time ad allocation and create Lagrangian policies that yield comparable performance guarantees. Finally, we extend our main results to four new scenarios.</div></div>","PeriodicalId":19529,"journal":{"name":"Omega-international Journal of Management Science","volume":null,"pages":null},"PeriodicalIF":6.7000,"publicationDate":"2024-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Dynamic allocation of display advertising impressions in dual sales channels\",\"authors\":\"\",\"doi\":\"10.1016/j.omega.2024.103213\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>We study a multi-period ad allocation problem faced by an online publisher who sells ad impressions on websites through two sales channels. In the guaranteed sales channel, advertisers submit heterogeneous offers for contracts under which the publisher guarantees delivery of a certain number of ad impressions over a certain period; in the real-time bidding (RTB) sales channel, the publisher runs an RTB auction to sell ad impressions. In each period, the publisher decides whether to accept or reject contract proposals; how to allocate ad impressions across existing contracts; and how many impressions to sell via RTB. The publisher faces uncertain demand from advertisers and an uncertain supply of impressions, which are generated by viewers visiting the publisher’s websites. We formulate the problem as a finite-horizon stochastic dynamic program, which poses significant methodological challenges. We first present structural properties of optimal policies under certain cases. To avoid the curse of dimensionality in dynamic programming, we develop an approach involving Lagrangian relaxations. We decompose the problem into a series of solvable subproblems and derive optimal policies. We further develop Lagrangian policies with performance guarantees. We show that when Lagrange multipliers depend on more signal history, the linear term’s weight of the number of contract types in the performance upper bound decreases. Furthermore, if the Lagrange multipliers depend on the full signal history, the corresponding Lagrangian policies will be asymptotically optimal to the number of contract types. We also explore a more suitable case for large-scale real-time ad allocation and create Lagrangian policies that yield comparable performance guarantees. Finally, we extend our main results to four new scenarios.</div></div>\",\"PeriodicalId\":19529,\"journal\":{\"name\":\"Omega-international Journal of Management Science\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":6.7000,\"publicationDate\":\"2024-10-24\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Omega-international Journal of Management Science\",\"FirstCategoryId\":\"91\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0305048324001774\",\"RegionNum\":2,\"RegionCategory\":\"管理学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"MANAGEMENT\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Omega-international Journal of Management Science","FirstCategoryId":"91","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0305048324001774","RegionNum":2,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"MANAGEMENT","Score":null,"Total":0}
引用次数: 0

摘要

我们研究了一个在线出版商面临的多期广告分配问题,该出版商通过两种销售渠道在网站上销售广告印象。在保证销售渠道中,广告商提交不同的合同报价,根据这些合同,出版商保证在一定时期内交付一定数量的广告印象;在实时竞价(RTB)销售渠道中,出版商通过 RTB 拍卖出售广告印象。在每个时期,出版商都要决定是否接受或拒绝合同提案;如何在现有合同中分配广告印象;以及通过 RTB 出售多少广告印象。出版商面临着不确定的广告商需求和不确定的广告印象供应,广告印象是由访问出版商网站的观众产生的。我们将该问题表述为有限视距随机动态程序,这在方法论上提出了重大挑战。我们首先介绍了某些情况下最优策略的结构特性。为了避免动态程序中的维度诅咒,我们开发了一种涉及拉格朗日松弛的方法。我们将问题分解为一系列可解的子问题,并推导出最优策略。我们进一步开发了具有性能保证的拉格朗日策略。我们表明,当拉格朗日乘数取决于更多的信号历史时,性能上限中合同类型数量的线性项权重就会降低。此外,如果拉格朗日乘数取决于全部信号历史,那么相应的拉格朗日策略将在合约类型数量上渐近最优。我们还探索了一种更适合大规模实时广告分配的情况,并创建了能产生类似性能保证的拉格朗日策略。最后,我们将主要结果扩展到四种新情况。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
Dynamic allocation of display advertising impressions in dual sales channels
We study a multi-period ad allocation problem faced by an online publisher who sells ad impressions on websites through two sales channels. In the guaranteed sales channel, advertisers submit heterogeneous offers for contracts under which the publisher guarantees delivery of a certain number of ad impressions over a certain period; in the real-time bidding (RTB) sales channel, the publisher runs an RTB auction to sell ad impressions. In each period, the publisher decides whether to accept or reject contract proposals; how to allocate ad impressions across existing contracts; and how many impressions to sell via RTB. The publisher faces uncertain demand from advertisers and an uncertain supply of impressions, which are generated by viewers visiting the publisher’s websites. We formulate the problem as a finite-horizon stochastic dynamic program, which poses significant methodological challenges. We first present structural properties of optimal policies under certain cases. To avoid the curse of dimensionality in dynamic programming, we develop an approach involving Lagrangian relaxations. We decompose the problem into a series of solvable subproblems and derive optimal policies. We further develop Lagrangian policies with performance guarantees. We show that when Lagrange multipliers depend on more signal history, the linear term’s weight of the number of contract types in the performance upper bound decreases. Furthermore, if the Lagrange multipliers depend on the full signal history, the corresponding Lagrangian policies will be asymptotically optimal to the number of contract types. We also explore a more suitable case for large-scale real-time ad allocation and create Lagrangian policies that yield comparable performance guarantees. Finally, we extend our main results to four new scenarios.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
Omega-international Journal of Management Science
Omega-international Journal of Management Science 管理科学-运筹学与管理科学
CiteScore
13.80
自引率
11.60%
发文量
130
审稿时长
56 days
期刊介绍: Omega reports on developments in management, including the latest research results and applications. Original contributions and review articles describe the state of the art in specific fields or functions of management, while there are shorter critical assessments of particular management techniques. Other features of the journal are the "Memoranda" section for short communications and "Feedback", a correspondence column. Omega is both stimulating reading and an important source for practising managers, specialists in management services, operational research workers and management scientists, management consultants, academics, students and research personnel throughout the world. The material published is of high quality and relevance, written in a manner which makes it accessible to all of this wide-ranging readership. Preference will be given to papers with implications to the practice of management. Submissions of purely theoretical papers are discouraged. The review of material for publication in the journal reflects this aim.
期刊最新文献
Managing supply disruptions for risk-averse buyers: Diversified sourcing vs. disruption prevention Dynamic allocation of display advertising impressions in dual sales channels Integrating machine learning models to learn potentially non-monotonic preferences for multi-criteria sorting from large-scale assignment examples Routing and charging scheduling for the electric carsharing system with mobile charging vehicles Accurate preference-based method to obtain the deterministically optimal and satisfactory fairness-efficiency trade-off
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1