论文信息 - Webert : Identifying interesting web sites

Webert : Identifying interesting web sites

We describe Syskill & Webert, a software agent that learns to rate pages on the World Wide Web (WWW), deciding what pages might interest a user. The user rates explored pages on a three point scale, and Syskill & 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

M. Pazzani | Daniel Billsus | Jack Muramatsu

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