User Session Identification Based on Strong Regularities in Inter-activity Time

TL;DR

This paper uncovers strong regularities in user activity timing across various online domains, proposing a data-driven method to identify user sessions with an inactivity threshold of about one hour, improving upon arbitrary thresholds.

Contribution

It introduces a methodology leveraging regularity in user activity patterns to determine a more accurate session inactivity threshold of approximately one hour.

Findings

User activity exhibits strong regularity across domains.

A 1-hour inactivity threshold effectively identifies user sessions.

Regularity insights can inform system design and user behavior analysis.

Abstract

Session identification is a common strategy used to develop metrics for web analytics and behavioral analyses of user-facing systems. Past work has argued that session identification strategies based on an inactivity threshold is inherently arbitrary or advocated that thresholds be set at about 30 minutes. In this work, we demonstrate a strong regularity in the temporal rhythms of user initiated events across several different domains of online activity (incl. video gaming, search, page views and volunteer contributions). We describe a methodology for identifying clusters of user activity and argue that regularity with which these activity clusters appear implies a good rule-of-thumb inactivity threshold of about 1 hour. We conclude with implications that these temporal rhythms may have for system design based on our observations and theories of goal-directed human activity.