实时竞价中的固定点标签归因
Fixed point label attribution for real-time bidding
AI总结：
本文针对实时竞价中奖励仅在用户层面聚合而无法在展示层面观察的不匹配问题，提出了标签归因任务，并开发了理论上合理且可大规模实现的固定点标签归因（FiPLA）算法，利用 Criteo 大规模公开数据集验证了其有效性。
摘要：
问题定义：大部分展示广告库存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主参与竞价的出价方（例如 Google、Criteo、RTB House、Trade Desk）。为了评估每次展示机会的价值，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，输入是表示每次展示机会的特征向量，标签是生成的奖励。在实践中，奖励由广告主提供，并与特定用户是否转化相关联。因此，奖励是在用户层面聚合的，从不在展示层面观察到。据我们所知，一个被忽视的基本任务是处理这种不匹配，并在训练学习算法之前，以正确的粒度级别拆分或归因奖励。我们将其称为标签归因问题。方法与结果：在本文中，我们开发了一种解决标签归因问题的方法，该方法在理论上是有依据的且实用。具体而言，我们开发了一种允许大规模实现的固定点算法，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。我们将我们的方法称为固定点标签归因（FiPLA）算法。管理启示：在将广告主的信号转化为展示标签时，通常存在一个隐含的信念飞跃。DSP 提供商在构建其机器学习流程时应谨慎，并仔细解决标签归因步骤。
英文摘要：
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, Trade Desk for instance) who 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 or not 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 dataset from Criteo, a large Demand Side Platform. We dub our approach the Fixed Point Label Attribution (FiPLA) Algorithm. Managerial implications: There is often a hidden leap of faith when transforming the advertiser's signal into display labelling. DSP providers should be careful when building their machine learning pipeline and carefully solve the label attribution step.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
