ContextPlay: Evaluating User Control for Context-Aware Music Recommendation

Yucheng Jin, Nyi Nyi Htun, Nava Tintarev, Katrien Verbert

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

8 Citations (Scopus)
176 Downloads (Pure)


Music preferences are likely to depend on contextual characteristics such as location and activity. However, most recommender systems do not allow users to adapt recommendations to their current context. We therefore built ContextPlay, a context-aware music recommender that enables user control for both contextual characteristics and music preferences. By conducting a mixed-design study (N=114) with four typical scenarios of music listening, we investigate the effect of controlling contextual characteristics in a music recommender system on four aspects: perceived quality, diversity, effectiveness, and cognitive load. Compared to our baseline which only allows to specify music preferences, having additional control for context leads to higher perceived quality and does not increase cognitive load. We also find that the contexts of mood, weather, and location tend to influence user perception of the system. Moreover, we found that users are more likely to modify contexts and their profile during relaxing activities.

Original languageEnglish
Title of host publicationUMAP'19
Subtitle of host publicationProceedings of the 27th ACM Conference on User Modeling, Adaptation and Personalization
Place of PublicationNew York, NY, USA
PublisherAssociation for Computing Machinery (ACM)
Number of pages9
ISBN (Electronic)978-1-4503-6021-0
Publication statusPublished - 2019
EventUMAP 2019: 27th ACM Conference on User Modeling, Adaptation and Personalization - Larnaca, Cyprus
Duration: 9 Jun 201912 Jun 2019
Conference number: 27


ConferenceUMAP 2019


  • Context-aware recommendation
  • Music recommendation
  • User control


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