CLUES: dynamic personalized message filtering
CSCW '96 Proceedings of the 1996 ACM conference on Computer supported cooperative work
Mining navigation history for recommendation
Proceedings of the 5th international conference on Intelligent user interfaces
IEEE Internet Computing
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Proceedings of the 5th international conference on Multimodal interfaces
Preference elicitation for interface optimization
Proceedings of the 18th annual ACM symposium on User interface software and technology
Matching attentional draw with utility in interruption
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
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SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
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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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BI'10 Proceedings of the 2010 international conference on Brain informatics
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Brainput: enhancing interactive systems with streaming fnirs brain input
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
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Today's users interact with an increasing amount of information, demanding a similar increase in attention and cognition. To help cope with information overload, recommendation engines direct users' attention to content that is most relevant to them. We suggest that functional near-infrared spectroscopy (fNIRS) brain measures can be used as an additional channel to information filtering systems. Using fNIRS, we acquire an implicit measure that correlates with user preference, thus avoiding the cognitive interruption that accompanies explicit preference ratings. We explore the use of fNIRS in information filtering systems by building and evaluating a brain-computer movie recommender. We find that our system recommends movies that are rated higher than in a control condition, improves recommendations with increased interaction with the system, and provides recommendations that are unique to each individual.