基于协同相似性度量的个性化电子邮件社区发现
Personalized Email Community Detection using Collaborative Similarity Measure
摘要：
在过去的十年中，电子邮件服务提供商已经采用了许多电子邮件分类和优先级系统来改善他们的服务。为了辅助电子邮件服务，我们提出了一个个性化的电子邮件社区发现方法，发现他们的结构和语义亲密度的基础上的电子邮件用户的分组。我们从一组电子邮件中提取个性化的社交图，唯一地利用每个节点的通信行为。随后，协作相似性度量（CSM）的图内聚类方法检测个性化社区。实证分析表明，由此产生的社区的有效性的评价措施，即密度，熵和f-措施。此外，从服务提供商和用户的角度来看，电子邮件过滤器，动态组预测和欺诈帐户检测建议作为潜在的应用。
英文摘要：
Email service providers have employed many email classification and prioritization systems over the last decade to improve their services. In order to assist email services, we propose a personalized email community detection method to discover the groupings of email users based on their structural and semantic intimacy. We extract the personalized social graph from a set of emails by uniquely leveraging each node with communication behavior. Subsequently, collaborative similarity measure (CSM) based intra-graph clustering approach detects personalized communities. The empirical analysis shows effectiveness of the resultant communities in terms of evaluation measures, i.e. density, entropy and f-measure. Moreover, email strainer, dynamic group prediction, and fraudulent account detection are suggested as the potential applications from both the service provider and user's point of view.
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