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Symposium on User Interface Software and Technology archive
Proceedings of the 22nd annual ACM symposium on User interface software and technology table of contents
Victoria, BC, Canada
SESSION: A.I./U.I. table of contents
Pages 193-202  
Year of Publication: 2009
ISBN:978-1-60558-745-5
Authors
Justin Matejka  Autodesk Research, Toronto, ON, Canada
Wei Li  Autodesk Research, Toronto, ON, Canada
Tovi Grossman  Autodesk Research, Toronto, ON, Canada
George Fitzmaurice  Autodesk Research, Toronto, ON, Canada
Sponsors
ACM: Association for Computing Machinery
SIGGRAPH: ACM Special Interest Group on Computer Graphics and Interactive Techniques
SIGCHI: ACM Special Interest Group on Computer-Human Interaction
Publisher
ACM  New York, NY, USA
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ABSTRACT

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.


REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

 
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