Using Linked Open Data for Novel Artist Recommendations

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by Stephan Baumann, Rafael Schirru
Abstract:
As part of the Linking Open Data initiative many (community) platforms have made their data freely available in recent years. In these systems valuable information from the music domain can be found. We use this data for contentbased music recommendations thus not requiring expensive metadata annotations from domain experts. In this demo paper we present a system that uses data from the Freebase platform to describe music artists. The artists are represented using metadata profiles that characterize different aspects of their works such as genres and instrumentations but also collaborations with other artists. The system uses a Lucene index for content-based artist recommendations aiming at Web-scalability.
Reference:
Using Linked Open Data for Novel Artist Recommendations (Stephan Baumann, Rafael Schirru), In 13th Internal Society for Music Information Retrival Conference, 2012.
Bibtex Entry:
@inproceedings{abstract,
abstract = {As part of the Linking Open Data initiative many (community) platforms have made their data freely available in recent years. In these systems valuable information from the music domain can be found. We use this data for contentbased music recommendations thus not requiring expensive metadata annotations from domain experts. In this demo paper we present a system that uses data from the Freebase platform to describe music artists. The artists are represented using metadata profiles that characterize different aspects of their works such as genres and instrumentations but also collaborations with other artists. The system uses a Lucene index for content-based artist recommendations aiming at Web-scalability.},
address = {Porto (Portugal)},
author = {Baumann, Stephan and Schirru, Rafael},
booktitle = {13th Internal Society for Music Information Retrival Conference},
keywords = {SML-LIB-BIBLIO,lang:ENG},
mendeley-tags = {SML-LIB-BIBLIO,lang:ENG},
title = {{Using Linked Open Data for Novel Artist Recommendations}},
year = {2012}
}
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