论文标题
漏斗结构的决策问题：一种多任务学习方法，并应用于电子邮件营销活动
Decision Making Problems with Funnel Structure: A Multi-Task Learning Approach with Application to Email Marketing Campaigns
论文作者
论文摘要
本文研究了漏斗结构的决策问题。漏斗结构是营销领域中著名的概念，发生在那些决策者以分层方式与环境互动的系统中，从深层观察到的观察比浅层较少。例如，在电子邮件营销活动应用程序中，这些图层对应于打开，单击和购买活动。点击购买以购买的转换很少发生，因为除非单击电子邮件中的链接，否则无法进行购买。 我们将这个具有挑战性的决策问题制定为具有漏斗结构的上下文匪徒，并开发了一种多任务学习算法，从而减轻了更深层的层次缺乏足够的观察结果。我们分析了算法的预测错误和遗憾。我们通过简单的模拟来验证关于预测错误的理论。基于一家主要电子邮件营销公司的现实数据的模拟环境和环境的实验表明，我们的算法比以前的方法可显着改善。
This paper studies the decision making problem with Funnel Structure. Funnel structure, a well-known concept in the marketing field, occurs in those systems where the decision maker interacts with the environment in a layered manner receiving far fewer observations from deep layers than shallow ones. For example, in the email marketing campaign application, the layers correspond to Open, Click and Purchase events. Conversions from Click to Purchase happen very infrequently because a purchase cannot be made unless the link in an email is clicked on. We formulate this challenging decision making problem as a contextual bandit with funnel structure and develop a multi-task learning algorithm that mitigates the lack of sufficient observations from deeper layers. We analyze both the prediction error and the regret of our algorithms. We verify our theory on prediction errors through a simple simulation. Experiments on both a simulated environment and an environment based on real-world data from a major email marketing company show that our algorithms offer significant improvement over previous methods.
