How do different levels of user control affect cognitive load and acceptance of recommendations?

User control has been recognised as an important feature in recommender system, as it allows users to steer the recommendation process. Most typical user controls relate to providing ratings, editing user data, and adjusting weights of the algorithm. The cognitive load of the user may increase when using more advanced user controls. We divided common user controls into three levels (high, middle, and low) and conducted a study (N=90) to investigate how different levels of user control affect cognitive load and quality of recommendations. We designed a visualisation on top of a music recommender system that incorporates three levels of control. The study results show that high level control tends to produce the best recommendations, while requiring the highest cognitive load. However, only participants with rich experience in recommender systems are more likely to tweak such high level control, while the majority of participants still prefers low and middle level control. We validated the robustness of our findings with three different algorithms.

Duke Scholars

Author Yucheng Jin DKU Faculty