Change search
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
User Awareness in Music Recommender Systems
TU Wien. (Institute of Information Systems Engineering)
Johannes Kepler University. (Institute of Computational Perception)
Jönköping University, School of Engineering, JTH, Computer Science and Informatics. (HCI)ORCID iD: 0000-0003-4344-9986
Université de Montréal. (École de bibliothéconomie et des sciences de l’information)
2019 (English)In: Personalized human-computer interaction / [ed] M. Augstein, E. Herder & W. Wörndl, Berlin: Walter de Gruyter, 2019Chapter in book (Refereed)
Abstract [en]

Music recommender systems are a widely adopted application of personalized systems and interfaces.By tracking the listening activity of their users and building preference profiles, a user can be given recommendations based on the preference profiles of all users (collaborative filtering), characteristics of the music listened to (content-based methods), meta-data and relational data (knowledge-based methods; sometimes also considered content-based methods) or a mixture of these with other features (hybrid methods).In this chapter, we focus on the listener's aspects of music recommender systems.We discuss different factors influencing relevance for recommendation on both the listener's and the music's side and categorize existing work. In more detail, we then review aspects of (i) listener background in terms of individual, i.e., personality traits and demographic characteristics, and cultural features, i.e., societal and environmental characteristics, (ii) listener context, in particular modeling dynamic properties and situational listening behavior, and (iii) listener intention, in particular by studying music information behavior, i.e., how people seek, find, and use music information.This is followed by a discussion of user-centric evaluation strategies for music recommender systems. We conclude the chapter with a reflection on current barriers, by pointing out current and longer-term limitations of existing approaches and outlining strategies for overcoming these.

Place, publisher, year, edition, pages
Berlin: Walter de Gruyter, 2019.
Series
De Gruyter Textbook
Keywords [en]
music recommender systems, personalization, user modeling, user context, user intent
National Category
Interaction Technologies Media Engineering Psychology
Identifiers
URN: urn:nbn:se:hj:diva-43620ISBN: 9783110552485 (print)ISBN: 9783110552614 (print)OAI: oai:DiVA.org:hj-43620DiVA, id: diva2:1314609
Available from: 2019-05-09 Created: 2019-05-09 Last updated: 2019-05-28Bibliographically approved

Open Access in DiVA

No full text in DiVA

Authority records BETA

Ferwerda, Bruce

Search in DiVA

By author/editor
Ferwerda, Bruce
By organisation
JTH, Computer Science and Informatics
Interaction TechnologiesMedia EngineeringPsychology

Search outside of DiVA

GoogleGoogle Scholar

isbn
urn-nbn

Altmetric score

isbn
urn-nbn
Total: 44 hits
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