Learning to Create Customized Authority Lists
Abstract
The proliferation of hypertext and the popularity ofKleinberg's HITS algorithm have brought about an increased interest in link analysis. While HITS and its older relatives from the Bibliometrics provide a method for nding authoritative sources on a particular topic, they do not allow individual users to inject their own opinions on what sources are authoritative. This paper presents a technique for learning a user's internal model of authority. We present experimental results based on Cora on-line index, a database of approximately one million on-line computer science literature references.
Cite
Text
Chang et al. "Learning to Create Customized Authority Lists." International Conference on Machine Learning, 2000.Markdown
[Chang et al. "Learning to Create Customized Authority Lists." International Conference on Machine Learning, 2000.](https://mlanthology.org/icml/2000/chang2000icml-learning/)BibTeX
@inproceedings{chang2000icml-learning,
title = {{Learning to Create Customized Authority Lists}},
author = {Chang, Huan and Cohn, David and McCallum, Andrew},
booktitle = {International Conference on Machine Learning},
year = {2000},
pages = {127-134},
url = {https://mlanthology.org/icml/2000/chang2000icml-learning/}
}