Automatic Personalized Spam Filtering through Significant Word Modeling

Typically, spam filters are built on the assumption that the characteristics of e-mails in the training set is identical to those in individual users´ inboxes on which it will be applied. This assumption is oftentimes incorrect leading to poor performance of the filter. A personalized spam filter is built by taking into account the characteristics of e-mails in individual users´ inboxes. We present an automatic approach for personalized spam filtering that does not require users´ feedback. The proposed algorithm builds a statistical model of significant spam and non-spam words from the labeled training set and then updates it in multiple passes over the unlabeled individual user´s inbox. The personalization of the model leads to improved filtering performance. We evaluate our algorithm on two publicly available datasets. The results show that our algorithm is robust and scalable, and a viable solution to the server-side personalized spam filtering problem. Moreover, it outperforms published results on one dataset and its performance is equivalent to the others on the second dataset.