A personalized mail re-filtering system based on the client

Abstract

Email is an essential communication tool, but a large number of spam emails can seriously affect the work and life of users and can even cause property damage. Due to different interests and hobbies, there may be huge differences in the definition of spam by users; the realization of personalized spam filtering has become an important issue in the field of spam filtering. When emails are misjudged, the user has to manually modify it, which brings great inconvenience to the user experience. In order to effectively solve the above problems and realize the functions of personalized email filtering and automatic correction of mis-filtered emails, this paper combined with rules and statistical methods presents a personalized email re-filtering system based on the client (PRFC) and implements the automatic modification of the mis-filtered emails. A large part of existing spam filters do not consider the difference between class prior probability and class imbalance problem; they only filter the mail online. Firstly, the proposed filter system processes the mails entering the inbox and the garbage and then designs two mutually learned filters based on the multi-task learning principle to be used for the automatic modification of the mis-filtered emails in inbox and garbage. To ensure the performance of the filter based on the interests of users and data distribution of mails varying with time, a multi-window learning framework that combines important weights to effectively implement the dynamic adaptation of the filter was designed. Finally, our proposed filtering system on the TREC 2006c and 2007p data sets that gets a significant filtering efficiency was verified.