Syskill & Webert: Identifying Interesting Web Sites

Michael Pazzani, Jack Muramatsu, and Daniel Billsus

We describe Syskill and Webert, a software agent that learns to rate pages on the Worm Wide Web (WWW), deciding what pages might interest a user. The user rates explored pages on a three point scale, and Syskill and Webert learns a user profile by analyzing the information on a page. The user profile can be used in two ways. First, it can be used to suggest which links a user would be interested in exploring. Second, it can be used to construct a LYCOS query to find pages that would interest a user. We compare four different learning algorithms and TF-IDF, an approach to weighting words used in information retrieval


This page is copyrighted by AAAI. All rights reserved. Your use of this site constitutes acceptance of all of AAAI's terms and conditions and privacy policy.