Refereed full papers (journals, book chapters, international conferences)

1998

@article{BCB1998,
	vgclass =	{refpap},
	author =	{Brian T. Bartell and Garrison W. Cottrell and Richard K.
	Belew},
	title =	{Optimizing similarity using multi-query relevance
	feedback},
	journal =	{Journal of the American Society for Information Science},
	volume =	{49},
	number =	{8},
	pages =	{742--761},
	year =	{1998},
	url =	{http://www3.interscience.wiley.com/cgi-bin/abstract/28112/},
	abstract =	{We propose a novel method for automatically adjusting
	parameters in ranked-output text retrieval systems to improve retrieval
	performance. A ranked-output text retrieval system implements a ranking
	function which orders documents, placing documents estimated to be more
	relevant to the user's query before less relevant ones. The system
	adjusts its parameters to maximize the match between the system's
	document ordering and a target ordering. The target ordering is
	typically given by user feedback on a set of sample queries, but is
	more generally any document preference relation. We demonstrate the
	utility of the approach by using it to estimate a similarity measure
	(scoring the relevance of documents to queries) in a vector space model
	of information retrieval. Experimental results using several
	collections indicate that the approach automatically finds a similarity
	measure which performs equivalently to or better than all classic
	similarity measures studied. It also performs within 1\% of an
	estimated optimal measure (found by exhaustive sampling of the
	similarity measures). The method is compared to two alternative
	methods: A Perceptron learning rule motivated by Wong and Yao's (1990)
	Query Formulation method, and a Least Squared learning rule, motivated
	by Fuhr and Buckley's (1991) Probabilistic Learning approach. Though
	both alternatives have useful characteristics, we demonstrate
	empirically that neither can be used to estimate the parameters of the
	optimal similarity measure.},
}