Learning system of web navigation patterns through hypertext probabilistic grammars
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Cortez Vasquez, Augusto | 2015-01-05
One issue of real interest in the area of
web data mining is to capture users’ activities during
connection and extract behavior patterns that help define
their preferences in order to improve the design of
future pages adapting websites interfaces to individual
users. This research is intended to provide, first of all,
a presentation of the methodological foundations of the
use of probabilistic languages to identify relevant or
most visited websites. Secondly, the web sessions are
represented by graphs and probabilistic context-free
grammars so that the sessions that have the highest
probabilities are considered the most visited and most
preferred, therefore, the most important in relation to
a particular topic. It aims to develop a tool for processing
web sessions obtained from a log server represented
by probabilistic context-free grammars.
LEER