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Effects of personal characteristics in control-oriented user interfaces for music recommender systems

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Abstract

Music recommender systems typically offer a “one-size-fits-all” approach with the same user controls and visualizations for all users. However, the effectiveness of interactive interfaces for music recommender systems is likely to be affected by individual differences. In this paper, we first conduct a comprehensive literature review of interactive interfaces in recommender systems to motivate the need for personalized interaction with music recommender systems, and two personal characteristics,  visual memory and musical sophistication. More specifically, we studied the influence of these characteristics on the design of (a) visualizations for enhancing recommendation diversity and (b) the optimal level of user controls while minimizing cognitive load. The results of three experiments show a benefit for personalizing both visualization and control elements to musical sophistication. We found that (1) musical sophistication influenced the acceptance of recommendations for user controls. (2) musical sophistication also influenced recommendation acceptance, and perceived diversity for visualizations and the UI combining user controls and visualizations. However, musical sophistication only strengthens the impact of UI on perceived diversity (moderation effect) when studying the combined effect of controls and visualizations. These results allow us to extend the model for personalization in music recommender systems by providing guidelines for interactive visualization design for music recommender systems, with regard to both visualizations and user control.

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Notes

  1. https://www.gold.ac.uk/music-mind-brain/gold-msi/, accessed June 2018.

  2. http://www.chambers.co.uk.

  3. https://humansystems.arc.nasa.gov/groups/tlx.

  4. https://developer.spotify.com/web-api/get-recommendations, accessed June 2018.

  5. https://developer.spotify.com/web-api/get-recommendations/#tablepress-220., accessed June 2018.

  6. https://api.spotify.com/v1/me/top , retrieved July 2018.

  7. https://api.spotify.com/v1/recommendations , retrieved July 2018.

  8. http://www.gold.ac.uk/music-mind-brain/gold-msi/, accessed June 2018.

  9. https://www.humanbenchmark.com/tests/memory, accessed June 2018.

  10. https://developer.spotify.com/web-api, accessed June 2018.

  11. https://humansystems.arc.nasa.gov/groups/tlx.

  12. AVE is short for average variance extracted. For a given factor, it is the average of the \(R^2\) values of the factor’s question items.

  13. http://lavaan.ugent.be/, accessed August 2019.

  14. https://d3js.org/, accessed June 2018.

  15. http://lavaan.ugent.be/, accessed February 2019.

  16. http://lavaan.ugent.be/, accessed February 2019.

  17. https://cran.r-project.org/web/packages/rockchalk/index.html.

  18. We note that the result for perceived diversity in Experiment 1 was inconclusive as the item did not fit.

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Acknowledgements

This research has been supported by the KU Leuven Research Council (grant agreement C24/16/017).

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  1. KU Leuven, Leuven, Belgium

    Yucheng Jin, Nyi Nyi Htun & Katrien Verbert

  2. TU Delft, Delft, The Netherlands

    Nava Tintarev

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Jin, Y., Tintarev, N., Htun, N.N. et al. Effects of personal characteristics in control-oriented user interfaces for music recommender systems. User Model User-Adap Inter 30, 199–249 (2020). https://doi.org/10.1007/s11257-019-09247-2

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