Search

Recent efforts in recommender systems research focus increasingly on human factors that affect acceptance of recommendations, such as user satisfaction, trust, transparency, and user control. In this paper, we present a scalable visualisation to interleave the output of several recommender engines with human-generated data, such as user bookmarks and tags. Such a visualisation enables users to explore which recommendations have been bookmarked by like-minded members of the community or marked with a specific relevant tag. Results of a preliminary user study (N=20) indicate that effectiveness and probability of item selection increase when users can explore relations between multiple recommendations and human feedback. In addition, perceived effectiveness and actual effectiveness of the recommendations as well as user trust into the recommendations are higher than a traditional list representation of recommendations. ispartof: pages:28-35 ispartof: Proceedings of the Joint Workshop on Interfaces and Human Decision Making for Recommender Systems co-located with ACM Conference on Recommender Systems (RecSys 2016) vol:1679 pages:28-35 ispartof: IntRS@RecSys location:Boston, USA date:16 Sep - 16 Sep 2016 status: published

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. ispartof: pages:35-42 ispartof: Jin, Y., Cardoso, B. and Verbert, K., 2017, August. How do different levels of user control affect cognitive load and acceptance of recommendations?. In Proceedings of the 4th Joint Workshop on Interfaces and Human Decision Making for Recommender Systems co-located with ACM Conference on Recommender Systems (RecSys 2017) vol:1884 pages:35-42 ispartof: IntRS workshop @ RecSys location:Como, Italy date:27 Aug - 27 Aug 2017 status: published