The anatomy of a large-scale hypertextual Web search engine
WWW7 Proceedings of the seventh international conference on World Wide Web 7
Authoritative sources in a hyperlinked environment
Journal of the ACM (JACM)
Email as spectroscopy: automated discovery of community structure within organizations
Communities and technologies
Email overload at work: an analysis of factors associated with email strain
CSCW '06 Proceedings of the 2006 20th anniversary conference on Computer supported cooperative work
Spam and the ongoing battle for the inbox
Communications of the ACM - Spam and the ongoing battle for the inbox
Improving spam detection based on structural similarity
SRUTI'05 Proceedings of the Steps to Reducing Unwanted Traffic on the Internet on Steps to Reducing Unwanted Traffic on the Internet Workshop
Semi-supervised spam filtering: does it work?
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
E-mail research: targeting the enterprise
Human-Computer Interaction
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
Suggesting friends using the implicit social graph
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Modeling personalized email prioritization: classification-based and regression-based approaches
Proceedings of the 20th ACM international conference on Information and knowledge management
Social feature-based enterprise email classification without examining email contents
Journal of Network and Computer Applications
Full-text search in email archives using social evaluation, attached and linked resources
Proceedings of the 21st international conference companion on World Wide Web
Security and Communication Networks
Finding email correspondents in online social networks
World Wide Web
Group affinity based social trust model for an intelligent movie recommender system
Multimedia Tools and Applications
Event detection using user interaction behavior models
Artificial Intelligence Review
Automated content labeling using context in email
Proceedings of the 17th International Conference on Management of Data
Effective email network visualization techniques by means of user behaviors
Intelligent Data Analysis
Hi-index | 0.00 |
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.