. 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 ...
%0 Journal Article
%1 citeulike:277299
%A Pazzani, Michael J.
%A Billsus, Daniel
%D 1997
%J Machine Learning
%K information-filtering
%N 3
%P 313--331
%T Learning and Revising User Profiles: The Identification of Interesting Web Sites
%U http://citeseer.ist.psu.edu/pazzani97learning.html
%V 27
%X . 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 ...
@article{citeulike:277299,
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 ...},
added-at = {2007-07-06T10:33:42.000+0200},
author = {Pazzani, Michael J. and Billsus, Daniel},
biburl = {https://www.bibsonomy.org/bibtex/2351cd5e874a4d39bc836fd3a3fea6232/schaal},
citeulike-article-id = {277299},
comment = {recommended by Myra as a means to filter information ...},
interhash = {e6e5f30b524bfabf9ac8c79d1ef5beae},
intrahash = {351cd5e874a4d39bc836fd3a3fea6232},
journal = {Machine Learning},
keywords = {information-filtering},
number = 3,
pages = {313--331},
priority = {2},
timestamp = {2007-07-06T10:33:49.000+0200},
title = {Learning and Revising User Profiles: The Identification of Interesting Web Sites},
url = {http://citeseer.ist.psu.edu/pazzani97learning.html},
volume = 27,
year = 1997
}