A naive recommendation model for large databases

It is difficult for users to find items as the number of choices increase and they become overwhelmed with high volume of data. In order to avoid them from bewilderment, a recommender could be applied to find more related items in shorter time. In this paper, we proposed a naive recommender model wh...

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Main Authors:	Jafarkarimi, Hosein, Tze, Alex Hiang Sim, Saadatdoost, Robab
Format:	Article
Published:	International Journal of Information and Education Technology 2012
Subjects:	QA75 Electronic computers. Computer science
Online Access:	http://eprints.utm.my/id/eprint/30529/ http://www.ijiet.org/show-31-222-1.html
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Institution:	Universiti Teknologi Malaysia

id	my.utm.30529
record_format	eprints
spelling	my.utm.305292019-09-22T07:25:36Z http://eprints.utm.my/id/eprint/30529/ A naive recommendation model for large databases Jafarkarimi, Hosein Tze, Alex Hiang Sim Saadatdoost, Robab QA75 Electronic computers. Computer science It is difficult for users to find items as the number of choices increase and they become overwhelmed with high volume of data. In order to avoid them from bewilderment, a recommender could be applied to find more related items in shorter time. In this paper, we proposed a naive recommender model which uses Association Rules Mining technique to generate two item sets enabling to find all existing rules for a certain item and has the capability to search on demand which decrease the response time dramatically This model mines transactions’ database to discover the existing rules among items and stores them in a sparse matrix. It also searches the matrix by means of a naive algorithm to generate a search list.We have applied and evaluated our model in Universiti Teknologi Malaysia and the results reflect a high level of accuracy. International Journal of Information and Education Technology 2012-06 Article PeerReviewed Jafarkarimi, Hosein and Tze, Alex Hiang Sim and Saadatdoost, Robab (2012) A naive recommendation model for large databases. International Journal of Information and Education Technology, 2 (3). pp. 216-219. ISSN 2010-3689 http://www.ijiet.org/show-31-222-1.html
institution	Universiti Teknologi Malaysia
building	UTM Library
collection	Institutional Repository
continent	Asia
country	Malaysia
content_provider	Universiti Teknologi Malaysia
content_source	UTM Institutional Repository
url_provider	http://eprints.utm.my/
topic	QA75 Electronic computers. Computer science
spellingShingle	QA75 Electronic computers. Computer science Jafarkarimi, Hosein Tze, Alex Hiang Sim Saadatdoost, Robab A naive recommendation model for large databases
description	It is difficult for users to find items as the number of choices increase and they become overwhelmed with high volume of data. In order to avoid them from bewilderment, a recommender could be applied to find more related items in shorter time. In this paper, we proposed a naive recommender model which uses Association Rules Mining technique to generate two item sets enabling to find all existing rules for a certain item and has the capability to search on demand which decrease the response time dramatically This model mines transactions’ database to discover the existing rules among items and stores them in a sparse matrix. It also searches the matrix by means of a naive algorithm to generate a search list.We have applied and evaluated our model in Universiti Teknologi Malaysia and the results reflect a high level of accuracy.
format	Article
author	Jafarkarimi, Hosein Tze, Alex Hiang Sim Saadatdoost, Robab
author_facet	Jafarkarimi, Hosein Tze, Alex Hiang Sim Saadatdoost, Robab
author_sort	Jafarkarimi, Hosein
title	A naive recommendation model for large databases
title_short	A naive recommendation model for large databases
title_full	A naive recommendation model for large databases
title_fullStr	A naive recommendation model for large databases
title_full_unstemmed	A naive recommendation model for large databases
title_sort	naive recommendation model for large databases
publisher	International Journal of Information and Education Technology
publishDate	2012
url	http://eprints.utm.my/id/eprint/30529/ http://www.ijiet.org/show-31-222-1.html
_version_	1646010280578121728

A naive recommendation model for large databases

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