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A Novel Approach for Document Retrieval System with User Preferences

by Sandeep Kaur, Nidhi Bhatla
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 102 - Number 9
Year of Publication: 2014
Authors: Sandeep Kaur, Nidhi Bhatla
10.5120/17845-8789

Sandeep Kaur, Nidhi Bhatla . A Novel Approach for Document Retrieval System with User Preferences. International Journal of Computer Applications. 102, 9 ( September 2014), 27-30. DOI=10.5120/17845-8789

@article{ 10.5120/17845-8789,
author = { Sandeep Kaur, Nidhi Bhatla },
title = { A Novel Approach for Document Retrieval System with User Preferences },
journal = { International Journal of Computer Applications },
issue_date = { September 2014 },
volume = { 102 },
number = { 9 },
month = { September },
year = { 2014 },
issn = { 0975-8887 },
pages = { 27-30 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume102/number9/17845-8789/ },
doi = { 10.5120/17845-8789 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:32:41.512459+05:30
%A Sandeep Kaur
%A Nidhi Bhatla
%T A Novel Approach for Document Retrieval System with User Preferences
%J International Journal of Computer Applications
%@ 0975-8887
%V 102
%N 9
%P 27-30
%D 2014
%I Foundation of Computer Science (FCS), NY, USA
Abstract

This paper proposes a method for Document Retrieval Systems. The document retrieval system finds information to given criteria by matching text record (documents) against user queries. The results generated from information retrieval system must have user preferences. Each user has its own perspectives and cultural context of each word or when the user is searching for highly specific, focussed topic. The probabilistic ranking based on graphic Bayesian statistics is associated with a Kuhn munkres algorithm for it to be really successful to group similar documents. The probabilistic ranking based Kuhn munkres algorithm uses the graphical model such as Bayesian statistics with Bayesian's theorem to find the probability of documents for more relevant results.

References
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Index Terms

Computer Science
Information Sciences

Keywords

IR Bayesian Statistics Bayesian Probability Graphical models Relevancy

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