Dynamic versus static menus: an exploratory comparison
ACM SIGCHI Bulletin
GroupLens: an open architecture for collaborative filtering of netnews
CSCW '94 Proceedings of the 1994 ACM conference on Computer supported cooperative work
Apple guide: a case study in user-aided design of online help
CHI '95 Conference Companion on Human Factors in Computing Systems
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CHI '95 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Social information filtering: algorithms for automating “word of mouth”
CHI '95 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
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Designing the User Interface: Strategies for Effective Human-Computer Interaction
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Amazon.com Recommendations: Item-to-Item Collaborative Filtering
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An Adaptive User Interface Based On Personalized Learning
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Usability Meanings and Interpretations in ISO Standards
Software Quality Control
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IEEE Transactions on Knowledge and Data Engineering
A hybrid learning system for recognizing user tasks from desktop activities and email messages
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A field evaluation of an adaptable two-interface design for feature-rich software
ACM Transactions on Computer-Human Interaction (TOCHI)
A personalized English learning recommender system for ESL students
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Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
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UIST '10 Proceedings of the 23nd annual ACM symposium on User interface software and technology
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ACM Transactions on Computer-Human Interaction (TOCHI)
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Proceedings of the 24th annual ACM symposium on User interface software and technology
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Proceedings of the 24th annual ACM symposium on User interface software and technology
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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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We explore the use of modern recommender system technology to address the problem of learning software applications. Before describing our new command recommender system, we first define relevant design considerations. We then discuss a 3 month user study we conducted with professional users to evaluate our algorithms which generated customized recommendations for each user. Analysis shows that our item-based collaborative filtering algorithm generates 2.1 times as many good suggestions as existing techniques. In addition we present a prototype user interface to ambiently present command recommendations to users, which has received promising initial user feedback.