Learning Text Filtering Preferences

Katia Sycara and Anandeep S. Pannu
Symposium on Machine Learning And Information Access, 1996.


Download
  • Adobe portable document format (pdf) (91KB)
Copyright notice: This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. These works may not be reposted without the explicit permission of the copyright holder.

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.

Notes

Text Reference
Katia Sycara and Anandeep S. Pannu, "Learning Text Filtering Preferences," Symposium on Machine Learning And Information Access, 1996.

BibTeX Reference
@inproceedings{Sycara_1996_2181,
   author = "Katia Sycara and Anandeep S. Pannu",
   title = "Learning Text Filtering Preferences",
   booktitle = "Symposium on Machine Learning And Information Access",
   year = "1996",
}