Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • 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
Predicting Musical Sophistication from Music Listening Behaviors: A Preliminary Study
Jönköping University, School of Engineering, JTH, Computer Science and Informatics.ORCID iD: 0000-0003-4344-9986
Maastricht University.
2018 (English)Conference paper, Published paper (Refereed)
Abstract [en]

Psychological models are increasingly being used to explain online behavioral traces. Aside from the commonly used personality traits as a general user model, more domain dependent models are gaining attention. The use of domain dependent psychological models allows for more fine-grained identification of behaviors and provide a deeper understanding behind the occurrence of those behaviors. Understanding behaviors based on psychological models can provide an advantage over data-driven approaches. For example, relying on psychological models allow for ways to personalize when data is scarce. In this preliminary work we look at the relation between users' musical sophistication and their online music listening behaviors and to what extent we can successfully predict musical sophistication. An analysis of data from a study with 61 participants shows that listening behaviors can successfully be used to infer users' musical sophistication.

Place, publisher, year, edition, pages
2018.
National Category
Other Electrical Engineering, Electronic Engineering, Information Engineering Media and Communications
Identifiers
URN: urn:nbn:se:hj:diva-41359OAI: oai:DiVA.org:hj-41359DiVA, id: diva2:1245711
Conference
12th ACM Conference on Recommender Systems
Available from: 2018-09-05 Created: 2018-09-05 Last updated: 2019-04-04

Open Access in DiVA

fulltext(195 kB)9 downloads
File information
File name FULLTEXT01.pdfFile size 195 kBChecksum SHA-512
ad60c332d0bd354c2fb4d9e6ae680b48cc31bb59401611ab6ce89d29bef913011d0b545a7ffd956eb942a89eee8e83cdcaae915985db5cffaa127d8a84b5c321
Type fulltextMimetype application/pdf

Other links

https://arxiv.org/abs/1808.07314

Authority records BETA

Ferwerda, Bruce

Search in DiVA

By author/editor
Ferwerda, Bruce
By organisation
JTH, Computer Science and Informatics
Other Electrical Engineering, Electronic Engineering, Information EngineeringMedia and Communications

Search outside of DiVA

GoogleGoogle Scholar
Total: 9 downloads
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

urn-nbn

Altmetric score

urn-nbn
Total: 83 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • 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