Browsing and filtering in social web feeds; summarization, recommendation and personalization of messages; An extension could be a semantic enabled filtering
Prem Melville and Raymond J. Mooney and Ramadass Nagarajan. Content-Boosted Collaborative Filtering for Improved Recommendations. Proceedings of the Eighteenth National Conference on Artificial Intelligence(AAAI-2002),
pp. 187-192, Edmonton, Canada, July 2002
We have developed a systems that enables
the detection of certain common salting
tricks that are employed by criminals. Salting
is the intentional addition or distortion of
content. In this paper we describe a framework
to identify email messages that might
contain new, previously unseen tricks. To
this end, we compare the simulated perceived
email message text generated by our hidden
salting simulation system to the OCRed
text we obtain from the rendered email message.
We present robust text comparison
techniques and train a classifier based on the
differences of these two texts. In simulations
we show that we can detect suspicious emails
with a high level of accuracy.
M. McLaughlin, and J. Herlocker. SIGIR '04: Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval, page 329--336. New York, NY, USA, ACM Press, (2004)
L. Jin, Z. Feng, and L. Feng. Proceedings of the 25th ACM International on Conference on Information and Knowledge Management, page 2137--2142. New York, NY, USA, ACM, (2016)
B. Kim, Q. Li, and A. Howe. WWW '06: Proceedings of the 15th international conference on World Wide Web, page 973--974. New York, NY, USA, ACM Press, (2006)
M. Bu, S. Luo, and J. He. Computer-Aided Industrial Design Conceptual Design, 2009. CAID CD 2009. IEEE 10th International Conference on, page 973 -976. (November 2009)