Mental models: towards a cognitive science of language, inference, and consciousness
Mental models: towards a cognitive science of language, inference, and consciousness
Smalltalk scaffolding: a case study of minimalist instruction
CHI '90 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
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CSCW '00 Proceedings of the 2000 ACM conference on Computer supported cooperative work
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Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
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Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
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Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
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International Journal of Human-Computer Studies
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Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
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Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
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Proceedings of the 12th ACM international conference on Ubiquitous computing
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VLHCC '10 Proceedings of the 2010 IEEE Symposium on Visual Languages and Human-Centric Computing
Human model evaluation in interactive supervised learning
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Interaction Design: Beyond Human - Computer Interaction
Interaction Design: Beyond Human - Computer Interaction
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What does a user need to know to productively work with an intelligent agent? Intelligent agents and recommender systems are gaining widespread use, potentially creating a need for end users to understand how these systems operate in order to fix their agent's personalized behavior. This paper explores the effects of mental model soundness on such personalization by providing structural knowledge of a music recommender system in an empirical study. Our findings show that participants were able to quickly build sound mental models of the recommender system's reasoning, and that participants who most improved their mental models during the study were significantly more likely to make the recommender operate to their satisfaction. These results suggest that by helping end users understand a system's reasoning, intelligent agents may elicit more and better feedback, thus more closely aligning their output with each user's intentions.