Recommending content using discriminatively trained document similarity

A generalized discriminative training framework for reconciling the training and evaluation objectives for document similarity is provided. Prior information about document relations and non-relations, are used to discriminatively train an ensemble of document similarity classification models. This result is a model set that can be used to compute similarity between seen documents in the training sets and new documents. The measure of similarity forms the basis of recommending documents to a user as well as being able to obtain metadata information such as keywords and tags for new documents not having such information.