0


基于Thompson抽样的SSP报头竞价策略优化

Optimization of a SSP’s Header Bidding Strategy using Thompson Sampling
课程网址: http://videolectures.net/kdd2018_grislain_header_bidding_strategy...  
主讲教师: Nicolas Grislain
开课单位: AlephD
开课时间: 2018-11-23
课程语种: 英语
中文简介:
在过去的十年里,数字媒体(网络或应用程序出版商)普遍使用实时广告拍卖来销售他们的广告空间。创建了多个拍卖平台,也称为供应方平台(SSP)。由于这种多样性,出版商开始在SSP之间制造竞争。在这种设置中,有两个连续的拍卖:每个SSP中的第二个价格拍卖和SSP之间的第二次第一个价格拍卖,称为标头竞价拍卖。在本文中,我们考虑一个SSP与其他SSP竞争广告空间。SSP充当想要购买广告位的广告商和想要出售其广告位的网络出版商之间的中介,并且需要定义投标策略,以便能够在尽可能少的支出的同时向广告商提供尽可能多的广告。这个单一共享平台的收入优化可以写成一个上下文盗贼问题,其中上下文包含有关广告机会的可用信息,例如互联网用户的财产或广告位置。在这种情况下,使用经典的多武器强盗策略(如UCB和EXP3的原始版本)效率低下,收敛速度低,因为武器之间的相关性很强。在本文中,我们设计并实验了汤普森采样算法的一个版本,该算法很容易将这种相关性考虑在内。我们将这种贝叶斯算法与粒子滤波器相结合,粒子滤波器允许通过顺序估计最高出价的分布来处理非平稳性,以赢得拍卖。我们在两个真实的拍卖数据集上应用了这种方法,并表明它显著优于更经典的方法。本文中定义的策略正在开发中,将部署在全球数千家出版商身上。
课程简介: Over the last decade, digital media (web or app publishers) generalized the use of real time ad auctions to sell their ad spaces. Multiple auction platforms, also called Supply-Side Platforms (SSP), were created. Because of this multiplicity, publishers started to create competition between SSPs. In this setting, there are two successive auctions: a second price auction in each SSP and a secondary, first price auction, called header bidding auction, between SSPs. In this paper, we consider an SSP competing with other SSPs for ad spaces. The SSP acts as an intermediary between an advertiser wanting to buy ad spaces and a web publisher wanting to sell its ad spaces, and needs to define a bidding strategy to be able to deliver to the advertisers as many ads as possible while spending as little as possible. The revenue optimization of this SSP can be written as a contextual bandit problem, where the context consists of the information available about the ad opportunity, such as properties of the internet user or of the ad placement. Using classical multi-armed bandit strategies (such as the original versions of UCB and EXP3) is inefficient in this setting and yields a low convergence speed, as the arms are very correlated. In this paper we design and experiment a version of the Thompson Sampling algorithm that easily takes this correlation into account. We combine this bayesian algorithm with a particle filter, which permits to handle non-stationarity by sequentially estimating the distribution of the highest bid to beat in order to win an auction. We apply this methodology on two real auction datasets, and show that it significantly outperforms more classical approaches. The strategy defined in this paper is being developed to be deployed on thousands of publishers worldwide.
关 键 词: 普遍使用实时广告; 供应方平台; 上下文盗贼问题; 贝叶斯算法; 与粒子滤波器相结合
课程来源: 视频讲座网
数据采集: 2023-03-23:cyh
最后编审: 2023-03-23:cyh
阅读次数: 30