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Collaborative classification of popular music on the internet and its social implications

Collaborative classification of popular music on the internet and its social implications Purpose – This paper aims to discuss how collaborative classification works in online music information retrieval systems and its impacts on the construction, fixation and orientation of the social uses of popular music on the internet. Design/methodology/approach – Using a comparative method, the paper examines the logic behind music classification in Recommender Systems by studying the case of Last.fm, one of the most popular web sites of this type on the web. Data collected about users' ritual classifications are compared with the classification used by the music industry, represented by the AllMusic web site. Findings – The paper identifies the differences between the criteria used for the collaborative classification of popular music, which is defined by users, and the traditional standards of commercial classification, used by the cultural industries, and discusses why commercial and non‐commercial classification methods vary. Practical implications – Collaborative ritual classification reveals a shift in the demand for cultural information that may affect the way in which this demand is organized, as well as the classification criteria for works on the digital music market. Social implications – Collective creation of a music classification in recommender systems represents a new model of cultural mediation that might change the way of building new uses, tastes and patterns of musical consumption in online environments. Originality/value – The paper highlights the way in which the classification process might influence the behavior of the users of music information retrieval systems, and vice versa. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png OCLC Systems & Services Emerald Publishing

Collaborative classification of popular music on the internet and its social implications

OCLC Systems & Services , Volume 27 (3): 38 – Aug 22, 2011

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Publisher
Emerald Publishing
Copyright
Copyright © 2011 Emerald Group Publishing Limited. All rights reserved.
ISSN
1065-075X
DOI
10.1108/10650751111164579
Publisher site
See Article on Publisher Site

Abstract

Purpose – This paper aims to discuss how collaborative classification works in online music information retrieval systems and its impacts on the construction, fixation and orientation of the social uses of popular music on the internet. Design/methodology/approach – Using a comparative method, the paper examines the logic behind music classification in Recommender Systems by studying the case of Last.fm, one of the most popular web sites of this type on the web. Data collected about users' ritual classifications are compared with the classification used by the music industry, represented by the AllMusic web site. Findings – The paper identifies the differences between the criteria used for the collaborative classification of popular music, which is defined by users, and the traditional standards of commercial classification, used by the cultural industries, and discusses why commercial and non‐commercial classification methods vary. Practical implications – Collaborative ritual classification reveals a shift in the demand for cultural information that may affect the way in which this demand is organized, as well as the classification criteria for works on the digital music market. Social implications – Collective creation of a music classification in recommender systems represents a new model of cultural mediation that might change the way of building new uses, tastes and patterns of musical consumption in online environments. Originality/value – The paper highlights the way in which the classification process might influence the behavior of the users of music information retrieval systems, and vice versa.

Journal

OCLC Systems & ServicesEmerald Publishing

Published: Aug 22, 2011

Keywords: Collaborative classification; Commercial classification; Popular music; Music information retrieval systems; Recommender systems; Last.fm; Social action; Social networks

References