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9. User awareness in music recommender systems

  • Peter Knees , Markus Schedl , Bruce Ferwerda und Audrey Laplante
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Personalized Human-Computer Interaction
Ein Kapitel aus dem Buch Personalized Human-Computer Interaction

Abstract

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.

Abstract

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.

Kapitel in diesem Buch

  1. Frontmatter I
  2. Introduction V
  3. Contents IX
  4. List of Contributing Authors XI
  5. Part I: Foundations of user modeling
  6. 1. Theory-grounded user modeling for personalized HCI 1
  7. 2. Opportunities and challenges of utilizing personality traits for personalization in HCI 31
  8. Part II: User input and feedback
  9. 3. Automated personalization of input methods and processes 67
  10. 4. How to use socio-emotional signals for adaptive training 103
  11. 5. Explanations and user control in recommender systems 133
  12. Part III: Personalization approaches
  13. 6. Tourist trip recommendations – foundations, state of the art, and challenges 159
  14. 7. Pictures as a tool for matching tourist preferences with destinations 183
  15. 8. Towards personalized virtual reality touring through cross-object user interfaces 201
  16. 9. User awareness in music recommender systems 223
  17. 10. Personalizing the user interface for people with disabilities 253
  18. 11. Adaptive workplace learning assistance 283
  19. Index 303
https://doi.org/10.1515/9783110552485-009

Kapitel in diesem Buch

  1. Frontmatter I
  2. Introduction V
  3. Contents IX
  4. List of Contributing Authors XI
  5. Part I: Foundations of user modeling
  6. 1. Theory-grounded user modeling for personalized HCI 1
  7. 2. Opportunities and challenges of utilizing personality traits for personalization in HCI 31
  8. Part II: User input and feedback
  9. 3. Automated personalization of input methods and processes 67
  10. 4. How to use socio-emotional signals for adaptive training 103
  11. 5. Explanations and user control in recommender systems 133
  12. Part III: Personalization approaches
  13. 6. Tourist trip recommendations – foundations, state of the art, and challenges 159
  14. 7. Pictures as a tool for matching tourist preferences with destinations 183
  15. 8. Towards personalized virtual reality touring through cross-object user interfaces 201
  16. 9. User awareness in music recommender systems 223
  17. 10. Personalizing the user interface for people with disabilities 253
  18. 11. Adaptive workplace learning assistance 283
  19. Index 303
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Heruntergeladen am 7.11.2025 von https://www.degruyterbrill.com/document/doi/10.1515/9783110552485-009/html
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