Email is still among the most popular online activities. People spend a signi cant amount of time sending, reading and responding to email in order to communicate with others, manage tasks and archive personal information. Most previous research on email is based on either relatively small data samples from user surveys and interviews, or on consumer email accounts such as those from Yahoo! Mail or Gmail. Much less has been published on how people interact with enterprise email even though it contains less automatically generated commercial email and involves more organizational behavior than is evident in personal accounts. In this paper, we extend previous work on predicting email reply behavior by looking at enterprise se ings and considering more than dyadic communications. We characterize the in uence of various factors such as email content and metadata, historical interaction features and temporal features on email reply behavior. We also develop models to predict whether a recipient will reply to an email and how long it will take to do so. Experiments with the publicly-available Avocado email collection show that our methods outperform all baselines with large gains. We also analyze the importance of different features on reply behavior predictions. Our ndings provide new insights about how people interact with enterprise email and have implications for the design of the next generation of email clients.
Yang et al. REF focused on predicting the reply action and studied the impact of factors such as email metadata, historical interaction features and temporal features.
Characterizing and Predicting Enterprise Email Reply Behavior
SIGIR '17
Computer Science