document: 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 social roles from the viewpoint of a particular user . We also developed a novel semi-supervised ( transductive ) learning algorithm that propagates importance labels from training examples to test examples through message and user nodes in a personal email network . These methods together enable us to obtain an enriched vector representation of each new email message , which consists of both standard features of an email message ( such as words in the title or body , sender and receiver IDs , etc. ) and the induced social features from the sender and receivers of the message . Using the enriched vector representation as the input in SVM classifiers to predict the importance level for each test message , we obtained significant performance improvement over the baseline system ( without induced social features ) in our experiments on a multi-user data collection . We obtained significant performance improvement over the baseline system ( without induced social features ) in our experiments on a multi-user data collection : the relative error reduction in MAE was 31 % in micro-averaging , and 14 % in macro-averaging .
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