The elements of computer credibility
Proceedings of the SIGCHI conference on Human Factors in Computing Systems
Explaining collaborative filtering recommendations
CSCW '00 Proceedings of the 2000 ACM conference on Computer supported cooperative work
The role of transparency in recommender systems
CHI '02 Extended Abstracts on Human Factors in Computing Systems
Information Filtering: Overview of Issues, Research and Systems
User Modeling and User-Adapted Interaction
Prominence-interpretation theory: explaining how people assess credibility online
CHI '03 Extended Abstracts on Human Factors in Computing Systems
Adaptive interfaces and agents
The human-computer interaction handbook
The role of trust in automation reliance
International Journal of Human-Computer Studies - Special issue: Trust and technology
User Involvement in Automatic Filtering: An Experimental Study
User Modeling and User-Adapted Interaction
Trust building with explanation interfaces
Proceedings of the 11th international conference on Intelligent user interfaces
Key issues in interactive problem solving: an empirical investigation on users attitude
INTERACT'05 Proceedings of the 2005 IFIP TC13 international conference on Human-Computer Interaction
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User-adaptive information filters can be a tool to achieve timely delivery of the right information to the right person, a feat critical in crisis management. This paper explores interaction issues that need to be taken into account when designing a user-adaptive information filter. Two case studies are used to illustrate which factors affect trust and acceptance in user-adaptive filters as a starting point for further research. The first study deals with user interaction with user-adaptive spam filters. The second study explores the user experience of an art recommender system, focusing on transparency. It appears that while participants appreciate filter functionality, they do not accept fully automated filtering. Transparency appears to be a promising way to increase trust and acceptance, but its successful implementation is challenging. Additional observations indicate that careful design of training mechanisms and the interface will be crucial in successful filter implementation.