Inferring Web communities from link topology
Proceedings of the ninth ACM conference on Hypertext and hypermedia : links, objects, time and space---structure in hypermedia systems: links, objects, time and space---structure in hypermedia systems
The anatomy of a large-scale hypertextual Web search engine
WWW7 Proceedings of the seventh international conference on World Wide Web 7
Managing gigabytes (2nd ed.): compressing and indexing documents and images
Managing gigabytes (2nd ed.): compressing and indexing documents and images
Authoritative sources in a hyperlinked environment
Journal of the ACM (JACM)
Proceedings of the 9th international World Wide Web conference on Computer networks : the international journal of computer and telecommunications netowrking
Finding authorities and hubs from link structures on the World Wide Web
Proceedings of the 10th international conference on World Wide Web
Learning to rank using gradient descent
ICML '05 Proceedings of the 22nd international conference on Machine learning
Google's PageRank and Beyond: The Science of Search Engine Rankings
Google's PageRank and Beyond: The Science of Search Engine Rankings
Web Dragons: Inside the Myths of Search Engine Technology
Web Dragons: Inside the Myths of Search Engine Technology
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
A learning algorithm for web page scoring systems
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
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Search engines -- "web dragons" -- are the portals through which we access society's treasure trove of information. They do not publish the algorithms they use to sort and filter information, yet how they work is one of the most important questions of our time. Google's PageRank is a way of measuring the prestige of each web page in terms of who links to it: it reflects the experience of a surfer condemned to click randomly around the web forever. The HITS technique distinguishes "hubs" that point to reputable sources from "authorities," the sources themselves. This helps differentiate communities on the web, which in turn can tease out alternative interpretations of ambiguous query terms. RankNet uses machine learning techniques to rank documents by predicting relevance judgments based on training data. This article explains in non-technical terms how the dragons work.