User Session Identification Based on Strong Regularities in Inter-activity Time
This addresses the need for less arbitrary session identification metrics in web analytics and behavioral analysis, though it is incremental as it refines existing threshold-based approaches.
The paper tackled the problem of arbitrary inactivity thresholds in user session identification by demonstrating strong regularities in inter-activity times across online domains, resulting in a proposed rule-of-thumb threshold of about 1 hour.
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.