Effects of Personal Characteristics on Music Recommender Systems with Different Levels of Controllability

Effects of Personal Characteristics on Music Recommender Systems with Different Levels of Controllability

Jin, Yucheng (Katholieke Universiteit Leuven)
Tintarev, N. (TU Delft Web Information Systems)
Verbert, Katrien (Katholieke Universiteit Leuven)

2018

Previous research has found that enabling users to control the recommendation process increases user satisfaction. However, providing additional controls also increases cognitive load, and different users have different needs for control. Therefore, in this study, we investigate the effect of two personal characteristics: musical sophistication and visual memory capacity. We designed a visual user interface, on top of a commercial music recommender, with different controls: interactions with recommendations (i.e., the output of a recommender system), the user profile (i.e., the top listened songs), and algorithm parameters (i.e., weights in an algorithm). We created eight experimental settings with combinations of these three user controls and conducted a between-subjects study (N=240), to explore the effect on cognitive load and recommendation acceptance for different personal characteristics. We found that controlling recommendations is the most favorable single control element. In addition, controlling user profile and algorithm parameters was the most beneficial setting with multiple controls. Moreover, the participants with high musical sophistication perceived recommendations to be of higher quality, which in turn lead to higher recommendation acceptance. However, we found no effect of visual working memory on either cognitive load or recommendation acceptance. This work contributes an understanding of how to design control that hits the sweet spot between the perceived quality of recommendations and acceptable cognitive load.

User control
personal characteristics
cognitive load
recommendation acceptance

http://resolver.tudelft.nl/uuid:712eedb8-60dd-46f1-85c1-dd788e651e5d

https://doi.org/10.1145/3240323.3240358

Association for Computer Machinery, New York, NY

978-1-4503-5901-6

RecSys '18: Proceedings of the 12th ACM Conference on Recommender Systems

12th ACM Conference on Recommender Systems, RecSys 2018, 2018-10-02 → 2018-10-07, Vancouver, Canada

Accepted author manuscript

Institutional Repository

conference paper

© 2018 Yucheng Jin, N. Tintarev, Katrien Verbert