Long Tail Attributes of Knowledge Worker Intranet Interactions

Authors:
Peter Géczy;Noriaki Izumi;Shotaro Akaho;Kôiti Hasida
Affiliations:
National Institute of Advanced Industrial Science and Technology (AIST), Tsukuba and Tokyo, Japan;National Institute of Advanced Industrial Science and Technology (AIST), Tsukuba and Tokyo, Japan;National Institute of Advanced Industrial Science and Technology (AIST), Tsukuba and Tokyo, Japan;National Institute of Advanced Industrial Science and Technology (AIST), Tsukuba and Tokyo, Japan
Venue:
MLDM '07 Proceedings of the 5th international conference on Machine Learning and Data Mining in Pattern Recognition
Year:
2007

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Abstract

Elucidation of human browsing behavior in electronic spaces has been attracting substantial attention in academic and commercial spheres. We present a novel formal approach to human behavior analysis in web based environments. The framework has been applied to analyzing knowledge workers' browsing behavior on a large corporate Intranet. Analysis indicates that users form elemental and complex browsing patterns and achieve their browsing objectives via few subgoals. Knowledge workers know their targets and exhibit diminutive exploratory behavior. Significant long tail attributes have been observed in all analyzed features. A novel distribution that accurately models it has been introduced.