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A Model-based Music Recommender System using Collaborative Filtering Technique

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2019
Adi Riyan Prasetya, Desi Ramayanti

Adi Riyan Prasetya and Desi Ramayanti. A Model-based Music Recommender System using Collaborative Filtering Technique. International Journal of Computer Applications 181(36):1-4, January 2019. BibTeX

	author = {Adi Riyan Prasetya and Desi Ramayanti},
	title = {A Model-based Music Recommender System using Collaborative Filtering Technique},
	journal = {International Journal of Computer Applications},
	issue_date = {January 2019},
	volume = {181},
	number = {36},
	month = {Jan},
	year = {2019},
	issn = {0975-8887},
	pages = {1-4},
	numpages = {4},
	url = {},
	doi = {10.5120/ijca2019918320},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}


Collaborative filtering (CF) techniques have been proven to be one of the successful approaches to building music recommender systems (MRS). The CF’s system work by creates suggestions for users based on their neighbors preferences. On the other hand the online music sites data are highly dynamic. As a result, the listeners face the problem of missing music suggestions. Recommender systems help the users to find their music interests and information they are looking for. In this paper we provide other researchers in that field with new valuable knowledge and insights regarding music recommendations using collaborative filtering technique that has the ability to recommend music to a new user as well as the other existing users. We then present three main categories of collaborative filtering techniques: memory-based, model-based, and the last hybrid method that combining collaborative and content-based.


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Music Personalization, Music Recommendation System.