TY - GEN
T1 - Predicting email recipients
AU - Sofershtein, Zvi
AU - Cohen, Sara
N1 - Funding Information: V. ACKNOWLEDGEMENTS The authors were partially supported by the Israel Science Foundation (Grant 1467/13) and the Ministry of Science and Technology (Grant 3-9617).
PY - 2015/8/25
Y1 - 2015/8/25
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=84962559646&partnerID=8YFLogxK
U2 - https://doi.org/10.1145/2808797.2808805
DO - https://doi.org/10.1145/2808797.2808805
M3 - منشور من مؤتمر
T3 - Proceedings of the 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2015
SP - 761
EP - 764
BT - Proceedings of the 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2015
A2 - Pei, Jian
A2 - Tang, Jie
A2 - Silvestri, Fabrizio
T2 - IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2015
Y2 - 25 August 2015 through 28 August 2015
ER -