12. The memory as recited in claim 9 wherein the acts further comprise: obtaining a training query and documents to be processed as ranked query-document pairs; indexing the training query and documents as training data in a vector space model having a dimension; producing a training database via the indexing; applying a learning latent semantic space for ranking algorithm; receiving ordinal information of indexed documents; integrating the ordinal information of the indexed documents into ranking in a learned subspace for ranking; projecting data from the learned subspace for ranking to a document database; and reporting the learned subspace including unranked documents.
12. The memory as recited in claim 9 wherein the acts further comprise: obtaining a training query and documents to be processed as ranked query-document pairs; indexing the training query and documents as training data in a vector space model having a dimension; producing a training database via the indexing; applying a learning latent semantic space for ranking algorithm; receiving ordinal information of indexed documents; integrating the ordinal information of the indexed documents into ranking in a learned subspace for ranking; projecting data from the learned subspace for ranking to a document database; and reporting the learned subspace including unranked documents. 15. The memory as recited in claim 12 wherein applying the learning latent semantic space for ranking algorithm transforms the training data to a lower-dimensional feature subspace.