NDLI: Predicting Email Recipients

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Predicting Email Recipients

Content Provider	ACM Digital Library
Author	Sofershtein, Zvi Cohen, Sara
Abstract	The ability to accurately predict recipients of an email, while it is being composed, is of great practical importance for two reasons. First, prediction of recipients allows for effective "auto-complete" of this field, thereby improving user experience and reducing the overhead of manual typing of the recipient. Second, this capability allows the system to alert the user when she has typed unlikely recipients. Such alerts can help avoid human error that might result in forgetting relevant recipients, or, even worse, disclosure of personal or classified information. In this demonstration, a system that effectively predicts email recipients, given an email history, will be exhibited. The system takes into consideration a variety of email related features to achieve high accuracy. Extensive experimentation on diverse email corpora has shown that our system adapts well to a variety of domains (such as business, personal and political email). Conference participants will be able to view real emails sent, and to observe how well our system predicted the recipients. In addition, they will be able to "impersonate" users whose email history is already available to the system, to compose a new email, and to view the recipient predictions.
Starting Page	761
Ending Page	764
Page Count	4
File Format	PDF
ISBN	9781450338547
DOI	10.1145/2808797.2808805
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2015-08-25
Publisher Place	New York
Access Restriction	Subscribed
Content Type	Text
Resource Type	Article

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