NOISE REDUCTION IN WEB DATA: A LEARNING APPROACH BASED ON DYNAMIC USER INTERESTS

Abstract

One of the significant issues facing web users
is the amount of noise in web data which
hinders the process of finding useful
information in relation to their dynamic
interests. Current research works consider
noise as any data that does not form part of
the main web page and propose noise web
data reduction tools which mainly focus on
eliminating noise in relation to the content
and layout of web data. This paper argues that
not all data that form part of the main web
page is of a user interest and not all noise data
is actually noise to a given user. Therefore,
learning of noise web data allocated to the
user requests ensures not only reduction of
noisiness level in a web user profile, but also
a decrease in the loss of useful information
hence improves the quality of a web user
profile. Noise Web Data Learning (NWDL)
tool/algorithm capable of learning noise web
data in web user profile is proposed. The
proposed work considers elimination of noise
data in relation to dynamic user interest. In
order to validate the performance of the
proposed work, an experimental design setup
is presented. The results obtained are
compared with the current algorithms applied
in noise web data reduction process. The
experimental results show that the proposed
work considers the dynamic change of user
interest prior to elimination of noise data. The
proposed work contributes towards improving
the quality of a web user profile by reducing
the amount of useful information eliminated
as noise.