Learning Text Filtering Preferences
Conference Paper, Proceedings of AAAI '96 Spring Symposium on Machine Learning in Information Access, pp. 116 - 118, March, 1996
Abstract
We describe a reusable agent that learns a model of the user's research interests for ltering 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 bootstrapped 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.
BibTeX
@conference{Sycara-1996-16296,author = {Katia Sycara and Anandeep S. Pannu},
title = {Learning Text Filtering Preferences},
booktitle = {Proceedings of AAAI '96 Spring Symposium on Machine Learning in Information Access},
year = {1996},
month = {March},
pages = {116 - 118},
}
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