Title

Mining Interaction Behaviors for Email Reply Order Prediction

Publication Type

Conference Proceeding Article

Publication Date

8-2010

Abstract

In email networks, user behaviors affect the way emails are sent and replied. While knowing these user behaviors can help to create more intelligent email services, there has not been much research into mining these behaviors. In this paper, we investigate user engagingness and responsiveness as two interaction behaviors that give us useful insights into how users email one another. Engaging users are those who can effectively solicit responses from other users. Responsive users are those who are willing to respond to other users. By modeling such behaviors, we are able to mine them and to identify engaging or responsive users. This paper proposes four types of models to quantify engagingness and responsiveness of users. These behaviors can be used as features in the email reply order prediction task which predicts the email reply order given an email pair. Our experiments show that engagingness and responsiveness behavior features are more useful than other non-behavior features in building a classifier for the email reply order prediction task. When combining behavior and non-behavior features, our classifier is also shown to predict the email reply order with good accuracy.

Discipline

Databases and Information Systems | Numerical Analysis and Scientific Computing

Research Areas

Data Management and Analytics

Publication

ASONAM 2010: Proceedings: 2010 International Conference on Advances in Social Network Analysis and Mining: 9-11 August 2010, Odense, Denmark

First Page

306

Last Page

310

ISBN

9780769541389

Identifier

10.1109/ASONAM.2010.60

Publisher

IEEE

City or Country

Odense, Denmark

Additional URL

http://dx.doi.org/10.1109/ASONAM.2010.60

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