论文标题
CAMTA：多点触摸归因的因果注意模型
CAMTA: Causal Attention Model for Multi-touch Attribution
论文作者
论文摘要
广告渠道已经从传统的印刷媒体，广告牌和广播广告演变为在线数字广告（AD），在线数字广告（AD）中，通过社交网络，展示广告，搜索等广告系列的广告系列接触到广告广告系列的一系列广告活动，而广告客户重新审视广告系列的设计，以便在新的广告通道中同时提供有关广告范围的重要信息，以估计广告的贡献（估计广告）的贡献（概述），该频道（概述）的贡献（概述）的贡献（概述）的贡献（概述）的贡献（概述）撰写了撰稿人的贡献。基于客户行动的顺序。这种贡献度量的过程通常称为多点触摸归因（MTA）。在这项工作中，我们提出了CAMTA，这是一种新型的深层复发性神经网络体系结构，在观察数据的背景下，是用户个性化MTA的随便归因机制。 CAMTA最大程度地减少了跨时间和接触点的通道分配中的选择偏差。此外，它以原则性的方式利用用户的前转换操作来预测通道前归因。为了定量基准基准提出的MTA模型，我们采用了现实世界中的Criteo数据集，并证明了CAMTA相对于预测准确性的出色性能，与几种基线相比。此外，我们为预测的频道归因提供了预算分配和用户行为建模的结果。
Advertising channels have evolved from conventional print media, billboards and radio advertising to online digital advertising (ad), where the users are exposed to a sequence of ad campaigns via social networks, display ads, search etc. While advertisers revisit the design of ad campaigns to concurrently serve the requirements emerging out of new ad channels, it is also critical for advertisers to estimate the contribution from touch-points (view, clicks, converts) on different channels, based on the sequence of customer actions. This process of contribution measurement is often referred to as multi-touch attribution (MTA). In this work, we propose CAMTA, a novel deep recurrent neural network architecture which is a casual attribution mechanism for user-personalised MTA in the context of observational data. CAMTA minimizes the selection bias in channel assignment across time-steps and touchpoints. Furthermore, it utilizes the users' pre-conversion actions in a principled way in order to predict pre-channel attribution. To quantitatively benchmark the proposed MTA model, we employ the real world Criteo dataset and demonstrate the superior performance of CAMTA with respect to prediction accuracy as compared to several baselines. In addition, we provide results for budget allocation and user-behaviour modelling on the predicted channel attribution.
