2009 MiningSocialNetworksforPersonal

• (Yoo et al., 2009) ⇒ Shinjae Yoo, Yiming Yang, Frank Lin, and Il-Chul Moon. (2009). “Mining Social Networks for Personalized Email Prioritization.” In: Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2009). doi:10.1145/1557019.1557124

Subject Headings:

Notes

• Categories and Subject Descriptors: I.7.m Computing Methodologies: Document and Text Processing — Miscellaneous; I.5.3 Computing Methodologies: Pattern Recognition — Clustering; I.5.4 [[Computing Methodologies]: Pattern Recognition — Applications
• General Terms: Algorithms, Experimentation, Security, Human Factors, and Languages.

Cited By

• http://scholar.google.com/scholar?q=%22Mining+social+networks+for+personalized+email+prioritization%22+2009
• http://portal.acm.org/citation.cfm?doid=1557019.1557124&preflayout=flat#citedby

Quotes

Author Keywords

Email Prioritization, Social Network, and Text Mining.

Abstract

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.

References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2009 MiningSocialNetworksforPersonal	Yiming Yang Shinjae Yoo Frank Lin Il-Chul Moon			Mining Social Networks for Personalized Email Prioritization		KDD-2009 Proceedings		10.1145/1557019.1557124		2009