  In this paper we develop and evaluate two methods for relevance feedback
based on endowing a suitable "semantic query space" with a Riemann metric
derived from the probability distribution of the positive samples of the
feedback. The first method uses a Gaussian distribution to model the data,
while the second uses a more complex Latent Semantic variable model. A mixed
(discrete-continuous) version of the Expectation-Maximization algorithm is
developed for this model.
  We motivate the need for the semantic query space by analyzing in some depth
three well-known relevance feedback methods, and we develop a new experimental
methodology to evaluate these methods and compare their performance in a
neutral way, that is, without making assumptions on the system in which they
will be embedded.
