Triggers and barriers to customizing software
CHI '91 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Efficient top-down induction of logic programs
ACM SIGART Bulletin
Automated learning of decision rules for text categorization
ACM Transactions on Information Systems (TOIS)
Email overload: exploring personal information management of email
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
MailCat: an intelligent assistant for organizing e-mail
Proceedings of the third annual conference on Autonomous Agents
SIGIR '00 Proceedings of the 23rd annual international ACM SIGIR conference on Research and development in information retrieval
Do users tolerate errors from their assistant?: experiments with an E-mail classifier
Proceedings of the 7th international conference on Intelligent user interfaces
IEMS - The Intelligent Email Sorter
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
Challenges of the Email Domain for Text Classification
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Usability engineering for the adaptive web
The adaptive web
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This paper reports our work to buildan email interface which can learn how to predict a user's email classifications at the same time as ensuring user control over the process. We report our exploration to answer the question: does the classifier work well enough to be effective? There has been considerable work to automate classification of email. Yet, it does not give a good sense of how well we are able to model user's classification of email. This paper reports the results of our own evaluations, including a stark observation that evaluation of this class of adaptive system needs to take account of the fact that the user can be expected to adapt to the system. This is important for the long term evaluation of such systems since we may find that this effect means that our systems may be performing better than classic evaluations might suggest.