Name
Abstract | ||
|---|---|---|
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. |
Year | Venue | Keywords |
|---|---|---|
2009 | IJCAI | new inbox message,new email,email view filter,filtered view,relevant view filter,ranking algorithm,email archives,rich metadata,inbox message,email volume,appropriate filter,data collection,user interface,machine learning |
Field | DocType | Citations |
Learning to rank,Metadata,World Wide Web,Computer science,Triage | Conference | 5 |
PageRank | References | Authors |
0.47 | 15 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Mark Dredze | 1 | 3092 | 176.22 |
| Bill N. Schilit | 2 | 2797 | 650.38 |
| Peter Norvig | 3 | 425 | 61.47 |