{"title":"用于实时竞价的定点标签归属","authors":"Martin Bompaire, Antoine Désir, Benjamin Heymann","doi":"10.1287/msom.2021.0611","DOIUrl":null,"url":null,"abstract":"Problem definition: Most of the display advertising inventory is sold through real-time auctions. The participants of these auctions are typically bidders (Google, Criteo, RTB House, and Trade Desk for instance) that participate on behalf of advertisers. In order to estimate the value of each display opportunity, they usually train advanced machine learning algorithms using historical data. In the labeled training set, the inputs are vectors of features representing each display opportunity, and the labels are the generated rewards. In practice, the rewards are given by the advertiser and are tied to whether a particular user converts. Consequently, the rewards are aggregated at the user level and never observed at the display level. A fundamental task that has, to the best of our knowledge, been overlooked is to account for this mismatch and split, or attribute, the rewards at the right granularity level before training a learning algorithm. We call this the label attribution problem. Methodology/results: In this paper, we develop an approach to the label attribution problem, which is both theoretically justified and practical. In particular, we develop a fixed point algorithm that allows for large-scale implementation and showcase our solution using a large-scale publicly available data set from Criteo, a large demand-side platform. We dub our approach the fixed point label attribution algorithm. Managerial implications: There is often a hidden leap of faith when transforming the advertiser’s signal into display labeling. Demand Side Platforms providers should be careful when building their machine learning pipeline and carefully solve the label attribution step.","PeriodicalId":501267,"journal":{"name":"Manufacturing & Service Operations Management","volume":"12 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Fixed Point Label Attribution for Real-Time Bidding\",\"authors\":\"Martin Bompaire, Antoine Désir, Benjamin Heymann\",\"doi\":\"10.1287/msom.2021.0611\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Problem definition: Most of the display advertising inventory is sold through real-time auctions. The participants of these auctions are typically bidders (Google, Criteo, RTB House, and Trade Desk for instance) that participate on behalf of advertisers. In order to estimate the value of each display opportunity, they usually train advanced machine learning algorithms using historical data. In the labeled training set, the inputs are vectors of features representing each display opportunity, and the labels are the generated rewards. In practice, the rewards are given by the advertiser and are tied to whether a particular user converts. Consequently, the rewards are aggregated at the user level and never observed at the display level. A fundamental task that has, to the best of our knowledge, been overlooked is to account for this mismatch and split, or attribute, the rewards at the right granularity level before training a learning algorithm. We call this the label attribution problem. Methodology/results: In this paper, we develop an approach to the label attribution problem, which is both theoretically justified and practical. 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引用次数: 0
摘要
问题定义:大部分显示广告库存都是通过实时拍卖出售的。这些拍卖的参与者通常是代表广告商参与拍卖的竞标者(如 Google、Criteo、RTB House 和 Trade Desk)。为了估算每个展示机会的价值,他们通常使用历史数据训练高级机器学习算法。在有标签的训练集中,输入是代表每个展示机会的特征向量,而标签则是生成的奖励。在实践中,奖励由广告商提供,并与特定用户是否转化挂钩。因此,奖励是在用户层面汇总的,从未在展示层面观察到。据我们所知,一个被忽视的基本任务就是在训练学习算法之前,考虑到这种不匹配,并在正确的粒度水平上分割或归属奖励。我们称之为标签归属问题。方法/结果:在本文中,我们针对标签归属问题开发了一种既有理论依据又切实可行的方法。特别是,我们开发了一种可大规模实施的定点算法,并使用来自大型需求方平台 Criteo 的大规模公开数据集展示了我们的解决方案。我们将这种方法命名为定点标签归因算法。管理意义:在将广告商的信号转化为展示标签时,往往存在着隐性的信仰飞跃。需求方平台提供商在构建机器学习管道时应小心谨慎,仔细解决标签归因步骤。
Fixed Point Label Attribution for Real-Time Bidding
Problem definition: Most of the display advertising inventory is sold through real-time auctions. The participants of these auctions are typically bidders (Google, Criteo, RTB House, and Trade Desk for instance) that participate on behalf of advertisers. In order to estimate the value of each display opportunity, they usually train advanced machine learning algorithms using historical data. In the labeled training set, the inputs are vectors of features representing each display opportunity, and the labels are the generated rewards. In practice, the rewards are given by the advertiser and are tied to whether a particular user converts. Consequently, the rewards are aggregated at the user level and never observed at the display level. A fundamental task that has, to the best of our knowledge, been overlooked is to account for this mismatch and split, or attribute, the rewards at the right granularity level before training a learning algorithm. We call this the label attribution problem. Methodology/results: In this paper, we develop an approach to the label attribution problem, which is both theoretically justified and practical. In particular, we develop a fixed point algorithm that allows for large-scale implementation and showcase our solution using a large-scale publicly available data set from Criteo, a large demand-side platform. We dub our approach the fixed point label attribution algorithm. Managerial implications: There is often a hidden leap of faith when transforming the advertiser’s signal into display labeling. Demand Side Platforms providers should be careful when building their machine learning pipeline and carefully solve the label attribution step.