Learning and Revising User Profiles: The Identification of Interesting Web Sites

Learning and Revising User Profiles: The Identification of Interesting Web Sites (1997)

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by Michael Pazzani , Daniel Billsus , S. Michalski , Janusz Wnek

Venue:	Machine Learning
Citations:	228 - 14 self

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Datum	Value	Source
TITLE	Learning and revising user profiles: The identification of interesting web sites	INFERENCE
AUTHOR NAME	Michael Pazzani	SVM HeaderParse 0.1
AUTHOR NAME	Daniel Billsus	SVM HeaderParse 0.1
AUTHOR AFFIL	Department of Information and Computer Science, University of California, Irvine, Irvine	SVM HeaderParse 0.2
AUTHOR ADDR	CA 92697	SVM HeaderParse 0.1
AUTHOR NAME	S. Michalski	SVM HeaderParse 0.1
AUTHOR NAME	Janusz Wnek	SVM HeaderParse 0.1
ABSTRACT	. We discuss algorithms for learning and revising user profiles that can determine which World Wide Web sites on a given topic would be interesting to a user. We describe the use of a naive Bayesian classifier for this task, and demonstrate that it can incrementally learn profiles from user feedback on the interestingness of Web sites. Furthermore, the Bayesian classifier may easily be extended to revise user provided profiles. In an experimental evaluation we compare the Bayesian classifier to computationally more intensive alternatives, and show that it performs at least as well as these approaches throughout a range of different domains. In addition, we empirically analyze the effects of providing the classifier with background knowledge in form of user defined profiles and examine the use of lexical knowledge for feature selection. We find that both approaches can substantially increase the prediction accuracy. Keywords: Information filtering, intelligent agents, multistrategy lea...	user correction - Legacy Corrections
YEAR	1997	INFERENCE
VENUE	Machine Learning	INFERENCE
VENUE TYPE	CONFERENCE	INFERENCE
PAGES	313--331	INFERENCE
VOLUME	27	INFERENCE
CITATIONS	29 found	ParsCit 1.0