Learning to Probabilistically Identify Authoritative Documents

We describe a model of document citation that learns to identify hubs and authorities in a set of linked documents, such as pages retrieved from the world wide web, or papers retrieved from a research paper archive. Unlike the popular HITS algorithm, which relies on dubious statistical assumptions, our model provides probabilistic estimates that have clear semantics. We also nd that in general, the identi ed authoritative documents correspond better to human intuition.

• Type: Conference
• Year: 2000
• Where: ICML
• Authors: David Cohn, Huan Chang
• Keywords: Authoritative Documents, Dubious Statistical Assumptions, ICML 2000, Machine Learning, Probabilistic Estimates