Proceedings:
Machine Learning in Information Access
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Papers from the 1996 AAAI Spring Symposium
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Abstract:
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
Spring
Papers from the 1996 AAAI Spring Symposium