Stuff I've seen: a system for personal information retrieval and re-use
Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
A family of additive online algorithms for category ranking
The Journal of Machine Learning Research
Findex: search result categories help users when document ranking fails
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Accurately interpreting clickthrough data as implicit feedback
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
Clustering versus faceted categories for information exploration
Communications of the ACM - Supporting exploratory search
Fast, flexible filtering with phlat
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Revisiting Whittaker & Sidner's "email overload" ten years later
CSCW '06 Proceedings of the 2006 20th anniversary conference on Computer supported cooperative work
FacetMap: A Scalable Search and Browse Visualization
IEEE Transactions on Visualization and Computer Graphics
Minimally invasive randomization for collecting unbiased preferences from clickthrough logs
AAAI'06 proceedings of the 21st national conference on Artificial intelligence - Volume 2
Intelligent email: aiding users with ai
Intelligent email: aiding users with ai
Examining repetition in user search behavior
ECIR'07 Proceedings of the 29th European conference on IR research
Extending faceted navigation for RDF data
ISWC'06 Proceedings of the 5th international conference on The Semantic Web
'Dealing with My Emails': Latent user needs in email management.
Computers in Human Behavior
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Growing email volumes cause flooded inboxes and swelled email archives, making search and new email processing difficult. While emails have rich metadata, such as recipients and folders, suitable for creating filtered views, it is often difficult to choose appropriate filters for new inbox messages without first examining messages. In this work, we consider a system that automatically suggests relevant view filters to the user for the currently viewed messages. We propose several ranking algorithms for suggesting useful filters. Our work suggests that such systems quickly filter groups of inbox messages and find messages more easily during search.