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Reseach Article

Recommender System based on Multidatasets

by B. Jayanthi, K. Duraisamy
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 65 - Number 20
Year of Publication: 2013
Authors: B. Jayanthi, K. Duraisamy
10.5120/11037-6045

B. Jayanthi, K. Duraisamy . Recommender System based on Multidatasets. International Journal of Computer Applications. 65, 20 ( March 2013), 1-5. DOI=10.5120/11037-6045

@article{ 10.5120/11037-6045,
author = { B. Jayanthi, K. Duraisamy },
title = { Recommender System based on Multidatasets },
journal = { International Journal of Computer Applications },
issue_date = { March 2013 },
volume = { 65 },
number = { 20 },
month = { March },
year = { 2013 },
issn = { 0975-8887 },
pages = { 1-5 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume65/number20/11037-6045/ },
doi = { 10.5120/11037-6045 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:19:19.274256+05:30
%A B. Jayanthi
%A K. Duraisamy
%T Recommender System based on Multidatasets
%J International Journal of Computer Applications
%@ 0975-8887
%V 65
%N 20
%P 1-5
%D 2013
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Recommender systems are one of the tools designed to help users deal with the information explosion by giving information recommendation according to their information needs. The cold-start problem refers to the situations where insufficient initial information of data sources for recommendations to make suggestions to users. When a new user extends into the system initially nothing is known about their preference and this need to be discovered. The process of how to get about it in the quickest and most accurate way is a challenge. This paper were designed in two phases, an off-line phase, where non-redundant multi-level and cross-level association rules and rare-item association rules are built and an on-line phase, where the rules are applied to real situations providing recommendations to customers to solve cold-start problem.

References
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Index Terms

Computer Science
Information Sciences

Keywords

Recommender System Cold-Start Problem Rare-Item Association Rule Non-Redundant