Open AccessIf You Like Radiohead, You Might Like This Article
Author(s) -
Celma Òscar,
Lamere Paul
Publication year - 2011
Publication title -
ai magazine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.597
H-Index - 79
eISSN - 2371-9621
pISSN - 0738-4602
DOI - 10.1609/aimag.v32i3.2363
Subject(s) - active listening , task (project management) , taste , computer science , digital audio , pop music automation , popular music , recommender system , multimedia , programming , visual arts , music industry , music history , art , world wide web , musical , speech recognition , music education , psychology , communication , engineering , audio signal , speech coding , systems engineering , neuroscience
With the recent dramatic transformations in the world of digital music, a music listener is now just a couple of clicks away from being able to listen to nearly any song that has ever been recorded. With so much music readily available, tools that help a user find new, interesting music that matches his or her taste become increasingly important. In this article we explore one such tool: music recommendation. We describe common music recommendation use cases such as finding new artists, finding others with similar listening tastes, and generating interesting music playlists. We describe the various approaches currently being explored by practitioners to satisfy these use cases. Finally, we show how results of three different music recommendation technologies compare when applied to the task of finding similar artists to a seed artist.