Incremental Relevance Feedback for Information Filtering.
James Allan:
Incremental Relevance Feedback for Information Filtering.
SIGIR 1996: 270-278@inproceedings{DBLP:conf/sigir/Allan96,
author = {James Allan},
title = {Incremental Relevance Feedback for Information Filtering},
booktitle = {SIGIR},
year = {1996},
pages = {270-278},
ee = {db/conf/sigir/Allan96.html},
bibsource = {DBLP, http://dblp.uni-trier.de}
}
Abstract
We use data from the TREC routing experiments to explore how relevance
feedback can be applied incrementally - using a few judged documents each
time - to achieve results that are as good as if the feedback occurred in one
pass. We show that relatively few judgments are needed to get high-quality
results. We also demonstrate methods that reduce the amount of information
archived from past judged documents without adversely affecting effectiveness.
A novel simulation shows that such techniques are useful for handling
long-standing queries with drifting notions of relevance.
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Printed Edition
Hans-Peter Frei, Donna Harman, Peter Schäuble, Ross Wilkinson (Eds.):
Proceedings of the 19th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR'96, August 18-22, 1996, Zurich, Switzerland (Special Issue of the SIGIR Forum).
ACM 1996, ISBN 0-89791-792-8
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