Mining social networks for personalized email prioritization

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 multiuser data collection: the relative error reduction in MAE was 31% in micro-averaging, and 14% in macro-averaging.

Suggesting friends using the implicit social graph

“…Often, researchers build their data sets by surveying a small set of users who are willing to provide the ground truth about their online social relationships [8,9,21]. By asking users to categorize their contacts into groups, or rate contacts as "close to me" or "not close to me", researchers can build a labeled data set that serves for both training and testing.…”

“…Yoo et al find that including social features along with message contentbased features in the vector of classifier input led to a significant reduction in prediction error when learning to identify the emails that a given user will consider important [21].…”

Feature Weight Optimization Mechanism for Email Spam Detection based on Two-step Clustering Algorithm and Logistic Regression Method

“…Investigating how different groups of features influence the filtering accuracy rate Yoo, S., et al [12] Priority E-mail Personalized technique (PEP) Analyzing the personal social networks to detect user groups and to achieve the user viewpoint based on the user social roles and then applying them for email message classification Largilliere and Peyronnet [15] Combination approach for internet email spamming PageRank method Liu et al [16] A hybrid machine learning system aided by user-behavior to filter spam pages The presented improved model and its constituent systems upgraded strategies in current circumstances have broad achievement in numerous true complex critical thinking. The significance of a joint system is not debatable, in light of the way that an individual system has its shortcoming, and an enhanced system is intended to complement the shortcoming of these individual shrewd systems.…”

“…Their empirical results presented that the SVM technique is precise and faster than the Random Forests (RF) algorithm [11]. Yoo, S., et al presented an email classification method called Priority Email Personalized technique (PEP) [12]. The PEP focused on analyzing the personal social networks to detect user groups and to achieve the user viewpoint based on the user social roles and then apply them for email message classification.…”

Finding email correspondents in online social networks

“…Their study led to two interesting Gmail Labs features on contact suggestions. As another example, in the context of a "personal email social network" on people's email accounts, Yoo et al [29] tackled the email overloading problem using the importance of email messages according to the email senders' priorities. A sender's priority is calculated based on three features: the social clusters that the sender belongs to in the social network, the social importance that is the sender's centrality level in the social network, and the importance propagation level in the range from 1 to 5.…”