@article{citeulike:277299,
title = {Learning and Revising User Profiles: The Identification of Interesting Web Sites},
author = {Michael J. Pazzani and Daniel Billsus},
journal = {Machine Learning},
number = {3},
pages = {313--331},
url = {http://citeseer.ist.psu.edu/pazzani97learning.html},
volume = {27},
year = {1997},
abstract = {. We discuss algorithms for learning and revising user profiles that can determine which World Wide
Web sites on a given topic would be interesting to a user. We describe the use of a naive Bayesian classifier for this
task, and demonstrate that it can incrementally learn profiles from user feedback on the interestingness of Web sites.
Furthermore, the Bayesian classifier may easily be extended to revise user provided profiles. In an experimental
evaluation we compare the Bayesian classifier to ...},
comment = {recommended by Myra as a means to filter information ...}, citeulike-article-id = {277299}, priority = {2},
keywords = {information-filtering }
}
::