A user defined taxonomy of factors that divide online information retrieval sessions

Although research is increasingly interested in session-based retrieval, comparably little work has focused on how best to divide web histories into sessions. Most automated attempts to divide web histories into sessions have focused on dividing web logs using simplistic rules, including user identifiers and specific time gaps. This research, however, is focused on understanding the full range of factors that affect the division of sessions, so that we can begin to go beyond current naive techniques like fixed time periods of inactivity. To investigate these factors, 10,000 log items were manually analysed by their owners into 847 naturally occurring web sessions. During interviews, participants reviewed their own web histories to identify these sessions, and described the causes of divisions between sessions. This paper contributes a taxonomy of six factors that can be used to better model the divisions between sessions, along with initial insights into how the divided sessions manifested in web logs. The factors in our taxonomy provide focus for future work, including our own, for finding practical ways to more intelligently divide and identify sessions for improved session-based retrieval.