Adaptive user modeling for filtering electronic news

A prototype system for the fine-grained filtering of news items has been developed and a pilot test has been conducted. The system is based on an adaptive user model that integrates stereotypes and artificial neural networks. The stereotypes are based on newspaper sections and sub-sections, along with editor specified and user specified keywords. Eight subjects trained the system over six days of news papers (986 news items) and then tested the system on a seventh day (171 news items). Five users were simply asked to 'read the news' while three users developed 'corporate' profiles with explicit information needs. The evaluations suggests that such an integrated adaptive user model did, in fact, reflect the difference between the two different types of task. In both cases, the results also reflect the quality of the training of the adaptive neural network by the user in creating the user profile.

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