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Music Recommendation System based on Unsupervised Discretization

by M. Sunitha, T. Adilakshmi
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 145 - Number 7
Year of Publication: 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 },
issue_date = { Jul 2016 },
volume = { 145 },
number = { 7 },
month = { Jul },
year = { 2016 },
issn = { 0975-8887 },
pages = { 22-25 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume145/number7/25291-2016910635/ },
doi = { 10.5120/ijca2016910635 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:48:10.425240+05:30
%A M. Sunitha
%A T. Adilakshmi
%T Music Recommendation System based on Unsupervised Discretization
%J International Journal of Computer Applications
%@ 0975-8887
%V 145
%N 7
%P 22-25
%D 2016
%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
  1. Last. FM – A popular music web portal http://www.last.fm
  2. Kaji Katsuhiko, Keiji Hirata, and Nagao Katashi. A music recommendation system based on annotations about listener’s preferences and situations. Axmedis, 0:231-234, 2005.
  3. G.Karypis, “Evaluation of item-based top-N recommendation algorithms,” CIKM 2001, pp. 247–254, 2001.
  4. Pandora – A free internet radio. http://www.pandora.com
  5. C. Anderson. The long Tail. Wired Magazine, 12(10): 170-177, 2004.
  6. Textbook: Pang-ning Tan, Vipin Kumar, Michael Steinbach, Introduction to Data Mining, Pearson
  7. Adomavicius G, Tuzhilin A (2005) Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions. IEEE Trans Knowl Data Eng 17(6):734–749
  8. 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
  9. iTunes
Index Terms

Computer Science
Information Sciences

Keywords

Internet E-commerce information overloading Recommendations systems Unsupervised discretization

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