通信学报 ›› 2013, Vol. 34 ›› Issue (Z2): 121-132.doi: 10.3969/j.issn.1000-436x.2013.Z2.024

• 数字化校园与典型应用 • 上一篇    下一篇

高校误判拉圾邮件自动召回系统的研究与实现

林海卓,王继龙,吴建平,扬家海,徐聪   

  1. 清华大学 网络科学与网络空间研究院,北京 100084
  • 出版日期:2013-12-25 发布日期:2017-06-16
  • 基金资助:
    国家重点基础研究发展计划(“973计划)基金资助项目

Effect of cold-rolling cladding on microstructure and properties of composite aluminum alloy foil

Hai-zhuo LIN,Ji-long WANG,Jian-ping WU,Jia-hai YANG,Cong XU   

  1. Academy of Network Science and Cyber Space,Tsinghua University,Beijing 100084,China
  • Online:2013-12-25 Published:2017-06-16
  • Supported by:
    The National Basic Research Program of China (973 Program)

摘要:

垃圾邮件的误判问题一直是反垃圾邮件领域中未能得到根本解决的难点。基于清华大学邮箱系统及反垃圾邮件网关系统进行了一整年的部署和实验(2011年9月至2012年10月),通过用户对可疑垃圾邮件点击召回的历史行为进行分析,井采用对其感兴趣的垃圾邮件进行文本相似度计算以及关键参数预测的方法来智能化预测用户对当前某一封垃圾邮件的感兴趣程度,即基于用户主观的选择和体验来帮助用户自动召回其可能感兴趣、然而却被反垃圾邮件网关误判的垃圾邮件,解决了传统过滤方法无法杜绝误判的问题。

关键词: 隐性索引模型, 垃圾邮件, 自动召回, 向量空间模型, 神经网络集成, KNN, 贝叶斯

Abstract:

The misjudgement of spam has always been the difficulty in the anti-spam area.Experiments based on Tsinghua university E-mail systems and anti-spam gateway system(Sep,2011–Oct,2012) were deployed and made,analyzing the history of recalling spam behavior of mail users,and using spam text similarity calculation and intelligent key parameters prediction method to predict the user's interest in the current pending spam,which can help users automatically recall their potential interested spams which were misjudged,based on the users' subjective choices and experience,solving the problem that cannot be eliminated by traditional filtering methods.

Key words: LSI, spam, automatic recall, VSM, neural network ensemble, KNN, Bayesian

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