Collaborative Research: CIF: Medium: An Information-Theoretic Foundation for Adaptive Bidding in First-Price Auctions

合作研究：CIF：媒介：一价拍卖中自适应出价的信息理论基础

• 批准号: 2106508
• 负责人: Zhengyuan Zhou
• 金额: $ 45万
• 依托单位: New York University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-10-01 至 2025-09-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2106508&HistoricalAwards=false
• 关键词: Collaborative; Research; CIF; Medium; Information

With the advent and increasing consolidation of e-commerce, digital advertising has very recently replaced traditional advertising as the main marketing force in the economy. In the past two years, a particularly important development in the digital advertising industry is the shift from second-price auctions to first-price auctions for online display ads. This shift immediately motivated the intellectually challenging question of how to bid in first-price auctions, because unlike in second-price auctions, bidding one's private value truthfully is no longer optimal. Furthermore, this shift has two unique modern characteristics: 1) the auctions are occurring repeatedly at a very high frequency and the bidding decisions must be made on that (milliseconds) timescale; second, there is exchange-dependent feedback information that one can and should leverage to inform one's sequential bidding decisions. These two characteristics expose drawbacks in the existing game-theoretical approaches and call for novel and principled developments in sequential bidding. The methodological and algorithmic innovation established in this project could also potentially help various organizations with advertising needs to navigate in the new and challenging landscape of display ads bidding.The broad goal of this project is to develop a methodological framework that intelligently and adaptively leverages past information to construct bidding strategies that are both computationally and statistically efficient. This requires developing information-theoretic tools to understand the fundamental learning limits for bidding in first-price auctions, where the reward function is neither convex nor continuous but has a special structure of its own that needs to be exploited. Further, it requires developing computationally efficient bidding and private value estimation algorithms for repeated first-price auctions that could meet the demanding nature of real-time bidding and large-scale historical bidding dataset, as well as learning-theoretical tools that enable the analysis and rigorous characterization of the algorithms' performance.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

随着电子商务的出现和综合化的增长，数字广告最近已取代了传统广告，成为经济中的主要营销力量。在过去的两年中，数字广告行业的一个特别重要的发展是从第二价格拍卖转向在线展示广告的第一价格拍卖。这一转变立即激发了如何在第一价格拍卖中竞标的智力挑战性问题，因为与第二价格拍卖不同，以真实的方式竞标人的私人价值不再是最佳选择。此外，这种转变具有两个独特的现代特征：1）拍卖在很高的频率上反复发生，并且必须对此（毫秒）时间表做出招标决定；其次，有与交换有关的反馈信息，人们可以并且应该利用以告知一个人的顺序招标决策。这两个特征在现有的游戏理论方法中揭示了缺点，并要求在顺序招标中进行新颖和有原则的发展。该项目中建立的方法论和算法创新也可能有可能帮助拥有广告的各种组织在展示广告的新且具有挑战性的景观中进行导航。该项目的广泛目标是智能地利用了过去的信息框架来制定过去的信息，以构建过去和统计上有效的竞标策略。这需要开发信息理论工具，以了解第一价格拍卖中竞标的基本学习限制，因为奖励功能既不是凸面也不是连续的，而是具有特殊的结构，需要利用它。此外，它需要开发重复的第一价格拍卖的计算有效的竞标和私人价值估计算法，这些算法可以符合实时竞标和大规模历史竞标数据集的苛刻性质，以及学习理论的学习理论工具，以实现算法的绩效的分析和严格的分析和严格的特征。智力优点和更广泛的影响审查标准。

项目成果

MEOW: A Space-Efficient Nonparametric Bid Shading Algorithm

MEOW：一种节省空间的非参数投标着色算法

• 发表时间: 2021
• 期刊: KDD '21: Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining
• 作者: Wei Zhang, Brendan Kitts