Personalized Spam Filtering for Gray Mail
CEAS(2008)
摘要
Gray mail, messages that could reasonably be considered either spam or good by differ- ent email users, is a commonly observed is- sue in production spam filtering systems. In this paper we study this class of mail using a large real-world email corpus and signature- based campaign detection techniques. Our analysis shows that even an optimal filter will inevitably perform unsatisfactorily on gray mail, unless user preferences are taken into account. To overcome this difficulty we de- sign a light-weight user model that is highly scalable and can be easily combined with a traditional global spam filter. Our approach is able to incorporate both partial and com- plete user feedback on message labels and catches up to 40% more spam from gray mail in the low false-positive region.
更多查看译文
关键词
user model,false positive
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络