Mining social networks for personalized email prioritization

Email is one of the most prevalent communication tools today, and solving the email overload problem is pressingly urgent. A good way to alleviate email overload is to automatically prioritize received messages according to the priorities of each user. However, research on statistical learning methods for fully personalized email prioritization (PEP) has been sparse due to privacy issues, since people are reluctant to share personal messages and importance judgments with the research community. It is therefore important to develop and evaluate PEP methods under the assumption that only limited training examples can be available, and that the system can only have the personal email data of each user during the training and testing of the model for that user. This paper presents the first study (to the best of our knowledge) under such an assumption. Specifically, we focus on analysis of personal social networks to capture user groups and to obtain rich features that represent the socia...

• Type: Conference
• Year: 2009
• Where: KDD
• Authors: Shinjae Yoo, Yiming Yang, Frank Lin, Il-Chul Moon
• Keywords: Data Mining, Email Overload Problem, Enriched Vector Representation, KDD 2009, Personal Email Data