EmailValet: Learning User Preferences for Wireless Email [postscript] [pdf]
(Published at IJCAI-99 Workshops: Learning About Users and Machine Learning for Information Filtering, 1999.)

This paper presents EmailValet, a system that learns users' email-reading preferences on email-capable wireless platforms -- specifically, on two-way pagers with small "qwerty" keyboards and an 8-line 30-character display. In use by the authors for about three months, it has gathered data on email-reading preferences over more than 8900 email messages received by the authors during this period. The paper presents results comparing the ability of different learning methods to form models that can predict whether a given message should be forwarded to the user's wireless device. Our results show that the best performance of one method, over a range of established learning methods developed on the information retrieval and machine learning communities, was able to achieve a break-even point of over 53% for one user that had received over 5000 messages. We also find that, in general, all methods are able to achieve better performance than what would be achieved by a baseline of simply forwarding all messages to the wireless device, and that many methods are able to find procedures that, although they forward only a small fraction of the messages that a user would want, achieve 100% precision on those messages that it does actually choose to forward.