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.
Description
[1411.2878] User Session Identification Based on Strong Regularities in Inter-activity Time
%0 Generic
%1 halfaker2014session
%A Halfaker, Aaron
%A Keyes, Oliver
%A Kluver, Daniel
%A Thebault-Spieker, Jacob
%A Nguyen, Tien
%A Shores, Kenneth
%A Uduwage, Anuradha
%A Warncke-Wang, Morten
%D 2014
%K identification navigation session
%T User Session Identification Based on Strong Regularities in
Inter-activity Time
%U http://arxiv.org/abs/1411.2878
%X 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.
@misc{halfaker2014session,
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.},
added-at = {2016-07-04T11:08:38.000+0200},
author = {Halfaker, Aaron and Keyes, Oliver and Kluver, Daniel and Thebault-Spieker, Jacob and Nguyen, Tien and Shores, Kenneth and Uduwage, Anuradha and Warncke-Wang, Morten},
biburl = {https://www.bibsonomy.org/bibtex/2d86e9bc9a2eeafc018ba15532ceb8028/thoni},
description = {[1411.2878] User Session Identification Based on Strong Regularities in Inter-activity Time},
interhash = {47fe5dba37b7bf4ab521458b9b02bb0e},
intrahash = {d86e9bc9a2eeafc018ba15532ceb8028},
keywords = {identification navigation session},
note = {cite arxiv:1411.2878Comment: 9 pages, 5 figures, 1 table},
timestamp = {2016-11-02T06:50:19.000+0100},
title = {User Session Identification Based on Strong Regularities in
Inter-activity Time},
url = {http://arxiv.org/abs/1411.2878},
year = 2014
}