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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 each 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... (Update)
Context of citations to this paper: More
...if semantic contexts are also used in addition to action sequences for mining user intention. For Syskill Webert, the authors of [15] have compared several learning algorithms in user s preference modeling and prediction, including Bayesian classifier, Nearest Neighbor,...
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BibTeX entry: (Update)
M. Pazzani, J. Muramatsu and D. Billsus, "Syskill & Webert: Identifying interesting Web Sites," In Proceedings of the Thirteenth National Conference on Artificial Intelligence, pp. 54-61, Portland, 1996. http://citeseer.ist.psu.edu/article/pazzani96syskill.html More
@inproceedings{ pazzani96syskill,
author = "Michael J. Pazzani and Jack Muramatsu and Daniel Billsus",
title = "Syskill Webert: Identifying Interesting Web Sites",
booktitle = "{AAAI}/{IAAI}, Vol. 1",
pages = "54-61",
year = "1996",
url = "citeseer.ist.psu.edu/article/pazzani96syskill.html" }
Citations (may not include all citations):2133 Pattern classification and scene analysis (context) - Duda, Hart - 1973
271 Improving retrieval performance by relevance feedback (context) - Salton, Buckley - 1990
222 Adaptive switching circuits (context) - Widrow, Hoff - 1960
207 WebWatcher: A learning apprentice for the World Wide Web - Armstrong, Freitag et al. - 1995
55 Using probabilistic models of document retrieval without rel.. (context) - Croft, Harper - 1979
36 NewsWeeder: Learning to filter news (context) - Lang - 1995
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