论文标题
rnn-Survival模型来决定电子邮件发送时间
An RNN-Survival Model to Decide Email Send Times
论文作者
论文摘要
电子邮件通信无处不在。公司控制发送电子邮件的时间，从而瞬间接收到收件人的瞬间（假定发送电子邮件是从发送时间收到电子邮件的）。但是，他们无法控制收件人打开电子邮件所需的持续时间，该电子邮件被标记为开放时间。重要的是，在打开的电子邮件中，大多数出现在他们的发送时间的短窗口内。我们认为，当发送时间方便接收者时，电子邮件可能会更快地打开，而对于其他发送时间，电子邮件可能会被忽略。因此，要计算适当的发送时间，准确预测时间到开放很重要。我们在生存模型框架中提出了一个复发性神经网络（RNN），以预测每个接受者的时间到开放。使用它，我们计算适当的发送时间。我们尝试在五个月内发送给一百万客户的电子邮件数据集。一个人从发件人那里收到的电子邮件顺序是与发件人过去的电子邮件进行交互的结果，因此包含有用的信号，可以为我们的模型提供信息。这种连续的依赖性提供了我们提出的RNN-Survival（RNN-S）方法，用于预测时间到开放时间的生存分析方法。我们表明，可以从预测的时间到开放的时间准确地计算发送电子邮件的最佳时间。这种方法允许公司调整发送电子邮件的时间，这是在其控制的情况下，以有利地影响开放率和参与度。
Email communications are ubiquitous. Firms control send times of emails and thereby the instants at which emails reach recipients (it is assumed email is received instantaneously from the send time). However, they do not control the duration it takes for recipients to open emails, labeled as time-to-open. Importantly, among emails that are opened, most occur within a short window from their send times. We posit that emails are likely to be opened sooner when send times are convenient for recipients, while for other send times, emails can get ignored. Thus, to compute appropriate send times it is important to predict times-to-open accurately. We propose a recurrent neural network (RNN) in a survival model framework to predict times-to-open, for each recipient. Using that we compute appropriate send times. We experiment on a data set of emails sent to a million customers over five months. The sequence of emails received by a person from a sender is a result of interactions with past emails from the sender, and hence contain useful signal that inform our model. This sequential dependence affords our proposed RNN-Survival (RNN-S) approach to outperform survival analysis approaches in predicting times-to-open. We show that best times to send emails can be computed accurately from predicted times-to-open. This approach allows a firm to tune send times of emails, which is in its control, to favorably influence open rates and engagement.
