id: 05581743
dt: a
an: 05581743
au: Géczy, Peter; Izumi, Noriaki; Akaho, Shotaro; Hasida, Kôiti
ti: Long tail attributes of knowledge worker intranet interactions.
so: Perner, Petra (ed.), Machine learning and data mining in pattern
    recognition. 5th international conference, MLDM 2007, Leipzig, Germany,
    July 18-20, 2007. Proceedings. Berlin: Springer (ISBN
    978-3-540-73498-7/pbk). Lecture Notes in Computer Science 4571. Lecture
    Notes in Artificial Intelligence, 419-433 (2007).
py: 2007
pu: Berlin: Springer
la: EN
cc: 
ut: 
ci: 
li: doi:10.1007/978-3-540-73499-4_32
ab: Summary: 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.
rv: