A method is proposed by which parameters in ranked-output text
retrieval systems can be automatically optimized 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 proposed method is to adjust system parameters to maximize the
match between the system's document ordering and the user's desired
ordering, given by relevance feedback.  The utility of the approach is
demonstrated by estimating the similarity measure in a vector space
model of information retrieval.  The approach automatically finds a
similarity measure which performs equivalent to or better than all
``classic'' similarity measures studied.  It also performs within 1\%
of an estimated theoretically optimal measure.