CFP last date
20 December 2024
Call for Paper
January Edition
IJCA solicits high quality original research papers for the upcoming January edition of the journal. The last date of research paper submission is 20 December 2024

Submit your paper
Know more
The week's pick
Responsive image
Improved Shuffled Frog Leaping Algorithm with Self-Adaptive Shuffling for Fuzzy Logic PD+G Controller Optimization in Robotic Manipulators

Duc Hoang Nguyen
Random Articles
Reseach Article

Music Recommendation System based on Unsupervised Discretization

by M. Sunitha, T. Adilakshmi
journal cover thumbnail

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

Powered by PhDFocusTM