Learning Text Filtering Preferences

Machine Learning in Information Access

Volume

Papers from the 1996 AAAI Spring Symposium

Contents

We describe a reusable agent that learns a model of the user’s research interests for filtering conference announcements and request for proposals (RFPs) from the Web. For this task, there is a large volume of irrelevant documents and the proportion of relevant documents'is very small. It is also critical that the agent not misclassify relevant documents. Information Retrieval and Neural Network techniques were utilized to learn the model of user’s preferences. Learning was boot-strapped using papers and proposals the user had written as positive examples. The agent’s performance at startup is quite high. Information retrieval and Neural Nets were used to train the agent and experimental performance results were obtained and reported.