Syskill & Webert: Identifying interesting web sites (1998)
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| Venue: | In Proc. 13th Natl. Conf. on Artificial Intelligence |
| Citations: | 271 - 5 self |
BibTeX
@INPROCEEDINGS{Pazzani98syskill&,
author = {Michael Pazzani and Jack Muramatsu and Daniel Billsus},
title = {Syskill & Webert: Identifying interesting web sites},
booktitle = {In Proc. 13th Natl. Conf. on Artificial Intelligence},
year = {1998},
pages = {54--61}
}
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Abstract
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, a standard information retrieval algorithm on this task. . 1 Introduction There is a vast amount of information on the World Wide Web (WWW) and more is becoming available daily. How can a user locate information that might be useful to that user? In this paper, we discuss Syskill & Webert, a software agent that learns a profile of a user's interest, and uses this profile to identify interesting web pages in two ways. First, by having ...
