Personalized Spam Filtering for Gray Mail

Gray mail, messages that could reasonably be considered either spam or good by different email users, is a commonly observed is-sue in production spam filtering systems. In this paper the authors study this class of mail using a large real-world email corpus and signature-based campaign detection techniques. The 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 the authors design a light-weight user model that is highly scalable and can be easily combined with a traditional global spam filter.