Abstract
This paper reports our work to build an 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.
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McCreath, E., Kay, J. (2003). Iems: Helping Users Manage Email. In: Brusilovsky, P., Corbett, A., de Rosis, F. (eds) User Modeling 2003. UM 2003. Lecture Notes in Computer Science(), vol 2702. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44963-9_35
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DOI: https://doi.org/10.1007/3-540-44963-9_35
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