Improving retrieval performance by relevance feedback

Improving retrieval performance by relevance feedback (1990)

by Gerard Salton , Chris Buckley

Venue:	Journal of the American Society for Information Science
Citations:	538 - 6 self

Datum	Value	Source
TITLE	Improving retrieval performance by relevance feedback	INFERENCE
AUTHOR NAME	Gerard Salton	SVM HeaderParse 0.2
AUTHOR AFFIL	Depattment of Computer Science, Cornell University, Ithaca	SVM HeaderParse 0.2
AUTHOR ADDR	NY 14853-7501	SVM HeaderParse 0.2
AUTHOR NAME	Chris Buckley	SVM HeaderParse 0.2
AUTHOR AFFIL	Depattment of Computer Science, Cornell University, Ithaca	SVM HeaderParse 0.2
AUTHOR ADDR	NY 14853-7501	SVM HeaderParse 0.2
ABSTRACT	Relevance feedback is an automatic process, introduced over 20 years ago, designed to produce improved query formulations following an initial retrieval operation. The principal relevance feedback methods described over the years are examined briefly, and evaluation data are included to demonstrate the effectiveness of the various methods. Prescriptions are given for conducting text re-trieval operations iteratively using relevance feedback. Introduction to Relevance Feedback It is well known that the original query formulation process is not transparent to most information system users. In particular, without detailed knowledge of the collection make-up, and of the retrieval environment, most users find	SVM HeaderParse 0.2
YEAR	1990	INFERENCE
VENUE	Journal of the American Society for Information Science	INFERENCE
VENUE TYPE	JOURNAL	INFERENCE
PAGES	288--297	INFERENCE
VOLUME	41	INFERENCE
CITATIONS	24 found	ParsCit 1.0