Creating customized authority lists
The proliferation of hypertext and the popularity of Kleinberg’s HITS algorithm have brought about an increased inter est in link analysis. While HITS and its older relatives from the Bibliometrics literature provide a method for finding au thoritative sources on a particular topic, they do not allow individual users to inject their own opinions about what sourc es are authoritative. This paper presents a technique that inc orporates user feedback by adjusting the measure of authority to match an individual’s internal notion of what sources are important. By “lifting” the authority of a few user-specifie d sources, the eigenvectors of the entire link matrix are realigned, resulting in a computationally cheap method that is much more rich than simple “spreading activation.” We present experimental results based on a database of about on e million references collected as part of the Coraon-line index of the computer science literature.