Paper Info
Title
A Customizable Behavior Model for Temporal Prediction of Web User Sequences
Abstract
One of the important Internet challenges in coming years will be the introduction of intelligent services and the creation of a more personalized environment for users. A key prerequisite for such services is the modeling of user behavior and a natural starting place for this are Web logs. In this paper we propose a model for predicting sequences of user accesses which is distinguished by two elements: it is customizable and it reflects sequentiality. Customizable, in this context, means that the proposed model can be adapted to the characteristics of the server to more accurately capture its behavior. The concept of sequentiality in our model consists of three elements: (1) preservation of the sequence of the click stream in the antecedent, (2) preservation of the sequence of the click stream in the consequent and (3) a measure of the gap between the antecedent and the consequent in terms of the number of user clicks.
Year
DOI
Venue
2002
10.1007/978-3-540-39663-5_5
LECTURE NOTES IN ARTIFICIAL INTELLIGENCE
Keywords
Field
DocType
behavior modeling
World Wide Web,Clickstream,Computer science,Association rule learning,The Internet,Prediction system
Conference
Volume
ISSN
Citations 
2703
0302-9743
9
PageRank 
References 
Authors
0.79
17
2
Name
Order
Citations
PageRank
Enrique Frías-Martínez121417.71
Vijay Karamcheti264667.03