Efficient algorithms for online ad markets with time constraints

适用于有时间限制的在线广告市场的高效算法

• 批准号: 501092-2016
• 负责人: Mazumdar, Ravi
• 金额: $ 1.93万
• 依托单位: University of Waterloo
• 依托单位国家: 加拿大
• 项目类别: Collaborative Research and Development Grants
• 财政年份: 2017
• 资助国家: 加拿大
• 起止时间: 2017-01-01 至 2018-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=618856
• 关键词: Efficient; algorithms; online; ad; markets

Algorithmic trading is an essential part of today's economy. This proposal in on the design and implementation of algorithmic trading in the context of ad auctions in the wireless mobile context. This work is being done in collaboration with our industrial partner Addictive Mobility, Toronto that provides cutting edge solutions for providing ad space to advertisers in the context of mobile wireless systems by trading on ad auction exchanges.The proposed research is to develop advanced algorithms that help improve the yield and maximize the profitability for algorithmic trading in the ad auction context. The key issues are bidding strategies that must operate at very fast time scales of around 100 milliseconds. The research will consist of three main components: 1) Development of models and techniques for ad space bidding given strict time constraints.2) Development of algorithmic trading strategies, and 3) Implementation of strategies. We will work closely with our industrial partner in the 2nd and 3rd thrusts.The research will involve many components: 1) Building models of random interactions using ideas from random exchange theory, 2) Understanding the role of budget and time constraints both in terms of execution time as well as campaign time horizon, 3) Understanding the role of information in the context of actions, 4) Develop techniques and algorithms for price discovery, and 5) Development of real-time bidding algorithms that can operate on the information history within the constraints of the auction durations.The techniques will involve Markov models, mean field theory, stochastic decision theory, learning, and the development of algorithms.

电子贸易是当今经济的重要组成部分。这个建议是关于在无线移动的上下文中的广告拍卖的上下文中的算法交易的设计和实现。这项工作是与我们的工业合作伙伴Addictive Mobility，多伦多合作完成的，该公司提供了最先进的解决方案，通过在广告拍卖交易所进行交易，在移动的无线系统的背景下为广告商提供广告空间。拟议的研究是开发先进的算法，帮助提高收益率，并最大限度地提高广告拍卖背景下算法交易的盈利能力。关键问题是投标策略必须在大约100毫秒的非常快的时间尺度上运行。该研究将包括三个主要部分：1）在严格的时间限制下开发广告空间竞标的模型和技术。2）开发算法交易策略，以及3）策略的实施。我们将在第二和第三阶段与我们的工业合作伙伴密切合作。研究将涉及许多组成部分：1）使用随机交换理论的思想建立随机交互模型，2）理解预算和时间约束在执行时间和活动时间范围方面的作用，3）理解信息在行动背景下的作用，4）开发价格发现的技术和算法; 5）开发实时报价算法，该算法可以在拍卖持续时间的约束下对信息历史进行操作。这些技术将涉及马尔可夫模型，平均场理论，随机决策理论，学习和算法的开发。