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ECML '98 Proceedings of the 10th European Conference on Machine Learning
"In vivo" spam filtering: a challenge problem for KDD
ACM SIGKDD Explorations Newsletter
Spam Filtering using a Markov Random Field Model with Variable Weighting Schemas
ICDM '04 Proceedings of the Fourth IEEE International Conference on Data Mining
Boosting trees for clause splitting
ConLL '01 Proceedings of the 2001 workshop on Computational Natural Language Learning - Volume 7
The foundations of cost-sensitive learning
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 2
Support vector machines for spam categorization
IEEE Transactions on Neural Networks
Trusting spam reporters: A reporter-based reputation system for email filtering
ACM Transactions on Information Systems (TOIS)
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Spam filtering of the email stream at the enterprise level poses many challenges especially at the scale of large Email Service Providers (ESPs). The problem is compounded if filtering is to be done on a personal level, with different configurations being adapted on a per-user basis. Commonly, the cost and performance issues are avoided by pushing personalized filtering to the client machine owned by the user, but this changes the user experience depending on the client used to access the mailbox. When inplementing personal spam filters as a services, the benefits stemming from increased spam-detection accuracy need to be carefully balanced with the associated costs, especially in view of a large users population and co-existence with user-independent detection engines. The paper describes the challenges associated with implementing large-scale personalized spam-filtering service ranging from the need to scale with the user population to the challenge of being constrained by a fixed budget.