Looking into the growth of information in the web it is a very tedious
process of getting the exact information the user is looking for. Many search
engines generate user profile related data listing. This paper involves one
such process where the rating is given to the link that the user is clicking
on. Rather than avoiding the uninterested links both interested links and the
uninterested links are listed. But sorted according to the weightings given to
each link by the number of visit made by the particular user and the amount of
time spent on the particular link.
Description
Efficient Personalized Web Mining: Utilizing The Most Utilized Data
%0 Journal Article
%1 grace2011
%A Grace, L. K. Joshila
%A Maheswari, V.
%A Nagamalai, Dhinaharan
%D 2011
%K data data_mining search_engine web_mining
%T Efficient Personalized Web Mining : Utilizing The Most Utilized Data
%U http://arxiv.org/ftp/arxiv/papers/1109/1109.1989.pdf
%X Looking into the growth of information in the web it is a very tedious
process of getting the exact information the user is looking for. Many search
engines generate user profile related data listing. This paper involves one
such process where the rating is given to the link that the user is clicking
on. Rather than avoiding the uninterested links both interested links and the
uninterested links are listed. But sorted according to the weightings given to
each link by the number of visit made by the particular user and the amount of
time spent on the particular link.
@article{grace2011,
abstract = { Looking into the growth of information in the web it is a very tedious
process of getting the exact information the user is looking for. Many search
engines generate user profile related data listing. This paper involves one
such process where the rating is given to the link that the user is clicking
on. Rather than avoiding the uninterested links both interested links and the
uninterested links are listed. But sorted according to the weightings given to
each link by the number of visit made by the particular user and the amount of
time spent on the particular link.
},
added-at = {2011-09-12T08:22:25.000+0200},
author = {Grace, L. K. Joshila and Maheswari, V. and Nagamalai, Dhinaharan},
biburl = {https://www.bibsonomy.org/bibtex/2353557ae86d0d66fba9fb64df66cdd3f/maxirichter},
description = {Efficient Personalized Web Mining: Utilizing The Most Utilized Data},
interhash = {d9fb56dce0131fb6496a25ff54580c02},
intrahash = {353557ae86d0d66fba9fb64df66cdd3f},
keywords = {data data_mining search_engine web_mining},
note = {cite arxiv:1109.1989Comment: conference paper},
timestamp = {2012-01-16T12:37:46.000+0100},
title = {Efficient Personalized Web Mining : Utilizing The Most Utilized Data},
url = {http://arxiv.org/ftp/arxiv/papers/1109/1109.1989.pdf},
year = 2011
}