Identifying User Sessions in Interactions with Intelligent Digital Assistants.

Search sessions have traditionally been considered as the focal unit of analysis for seeking behavioral insights from user interactions. While most session identification techniques have focused on the traditional web search setting; in this work, we instead consider user interactions with digital assistants (e.g. Cortana, Siri) and aim at identifying session boundary cut-offs. To our knowledge, this is one of the first studies investigating user interactions with a desktop based digital assistant. Historically, most user session identification strategies based on inactivity thresholds are either inherently arbitrary, or set at about 30 minutes. We postulate that such 30 minute thresholds may not be optimal for segregating user interactions with intelligent assistants into sessions. Instead, we model user-activity times as a Gaussian mixture model and look for evidence of a valley to identify optimal inter-activity thresholds for identifying sessions. Our results suggest a smaller threshold($\\sim$2 minutes) for session boundary cut-off in digital assistants than the traditionally used 30 minute threshold for web search engines.