Research Article
Music Recommendation System based on Unsupervised Discretization
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
|
| Volume 145 - Issue 7 |
| Published: Jul 2016 |
| Authors: M. Sunitha, T. Adilakshmi |
10.5120/ijca2016910635
|
M. Sunitha, T. Adilakshmi . Music Recommendation System based on Unsupervised Discretization. International Journal of Computer Applications. 145, 7 (Jul 2016), 22-25. DOI=10.5120/ijca2016910635
@article{ 10.5120/ijca2016910635,
author = { M. Sunitha,T. Adilakshmi },
title = { Music Recommendation System based on Unsupervised Discretization },
journal = { International Journal of Computer Applications },
year = { 2016 },
volume = { 145 },
number = { 7 },
pages = { 22-25 },
doi = { 10.5120/ijca2016910635 },
publisher = { Foundation of Computer Science (FCS), NY, USA }
}
%0 Journal Article
%D 2016
%A M. Sunitha
%A T. Adilakshmi
%T Music Recommendation System based on Unsupervised Discretization%T
%J International Journal of Computer Applications
%V 145
%N 7
%P 22-25
%R 10.5120/ijca2016910635
%I Foundation of Computer Science (FCS), NY, USA
Abstract
Because of the revolution in the field of Internet and E-commerce, users are overwhelmed by choices either it may be a book or movie or Music etc. Recommendations systems are serving as one of the important tool to handle information overloading by providing recommendations to users. In this paper we proposed a method to handle music recommendation problem. Unsupervised discretization is used to cluster the items which are similar in their frequency distribution. The proposed method is evaluated by using a benchmark dataset Last.fm. the results depict the fact that the proposed method performs better than the traditional popular recommendation approach.
References
- Last. FM – A popular music web portal http://www.last.fm
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- G.Karypis, “Evaluation of item-based top-N recommendation algorithms,” CIKM 2001, pp. 247–254, 2001.
- Pandora – A free internet radio. http://www.pandora.com
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- Textbook: Pang-ning Tan, Vipin Kumar, Michael Steinbach, Introduction to Data Mining, Pearson
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- Abbassi Z, Amer-Yahia S, Lakshmanan LVS, Vassilvitskii S, Yu C (2009) Getting recommender systems to think outside the box. In: Proceedings of the third ACM conference on recommender systems RecSys 09, ACM Press, pp 285–288
- iTunes
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