Automated email activity management: an unsupervised learning approach

Abstract

Many structured activities are managed by email. For instance, a consumer purchasing an item from an e-commerce vendor may receive a message confirming the order, a warning of a delay, and then a shipment notification. Existing email clients do not understand this structure, forcing users to manage their activities by sifting through lists of messages. As a first step to developing email applications that provide high-level support for structured activities, we consider the problem of automatically learning an activity's structure. We formalize activities as finite-state automata, where states correspond to the status of the process, and transitions represent messages sent between participants. We propose several unsupervised machine learning algorithms in this context, and evaluate them on a collection of e-commerce email.