Does "Authority" Mean Quality? Predicting Expert Quality Ratings of Web Documents

Does "Authority" Mean Quality? Predicting Expert Quality Ratings of Web Documents (2000)

by Brian Amento , Loren Terveen , Will Hill

Venue:	In Proc. ACM SIGIR 2000
Citations:	81 - 2 self

Datum	Value	Source
TITLE	Does "Authority " Mean Quality? Predicting Expert Quality Ratings of Web Documents	SVM HeaderParse 0.1
AUTHOR NAME	Brian Amento	SVM HeaderParse 0.1
AUTHOR NAME	Loren Terveen	SVM HeaderParse 0.1
AUTHOR ADDR	180 Park Avenue; Florham Park, NJ 07932 USA	SVM HeaderParse 0.1
AUTHOR NAME	Will Hill	SVM HeaderParse 0.1
AUTHOR ADDR	180 Park Avenue; Florham Park, NJ 07932 USA	SVM HeaderParse 0.1
ABSTRACT	structure For many topics, the World Wide Web contains hundreds or thousands of relevant documents of widely varying quality. Users face a daunting challenge in identifying a small subset of documents worthy of their attention. Link analysis algorithms have received much interest recently, in large part for their potential to identify high quality items. We report here on an experimental evaluation of this potential. We evaluated a number of link and content-based algorithms using a dataset of web documents rated for quality by human topic experts. Link-based metrics did a good job of picking out high-quality items. Precision at 5 is about 0.75, and precision at 10 is about 0.55; this is in a dataset where 0.32 of all documents were of high quality. Surprisingly, a simple content-based metric performed nearly as well; ranking documents by the total number of pages on their containing site.	SVM HeaderParse 0.1
CITATIONS	13 found	ParsCit 1.0