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Large-scale Analysis of Group-specific Music Genre Taste From Collaborative Tags
Johannes Kepler University, Austria.
Jönköping University, School of Engineering, JTH, Computer Science and Informatics.ORCID iD: 0000-0003-4344-9986
2017 (English)Conference paper, Published paper (Refereed)
Abstract [en]

In this paper, we describe the LFM-1b User Genre Profile dataset. It provides detailed information on musical genre preferences for more than 120,000 listeners and links to the LFM-1b dataset. We created the dataset by exploiting social tags, indexing them using two genre term sets, and aggregating the resulting annotated listening events on the user level. We foresee several applications of the dataset in music retrieval and recommendation tasks, among others to build and evaluate decent user models, to alleviate cold-start situations in music recommender systems, and to increase their performance using the additional abstraction layer of genre. We further present results of statistical analyses of the dataset, regarding genre preferences and their consistencies. We do so for the entire user population and for user groups defined by demographic similarities. Moreover, we report interesting insights about correlations between musical preferences on the genre level.

Place, publisher, year, edition, pages
2017.
National Category
Media Engineering
Identifiers
URN: urn:nbn:se:hj:diva-37614OAI: oai:DiVA.org:hj-37614DiVA: diva2:1149812
Conference
The 19th IEEE International Symposium on Multimedia (ISM2017), Taichung, December 11-13, 2017.
Available from: 2017-10-17 Created: 2017-10-17 Last updated: 2017-10-19Bibliographically approved

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fulltext(225 kB)9 downloads
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CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf