14. A method according to claim 13 , wherein the plurality of documents comprises relevant documents and non-relevant documents relative to the user query, the method further comprising: receiving a positive feedback input comprising a selection of at least one of the relevant documents; receiving a negative feedback input comprising a selection of at least one of the non-relevant documents; refining the query language model based at least in part on the initial query and at least one of the positive feedback input and the negative feedback input; re-computing a language-modeling kernel as an integration of the query language model and the document language models based at least in part upon replacing the query language model component of the language-modeling kernel with the refined query language model; generating a decision boundary in a new vector space determined at least in part by the re-computed language-modeling kernel between the document language models corresponding to the selected relevant documents and the document language models corresponding to the selected non-relevant documents such that the decision boundary is substantially equidistant from the document language models corresponding to the relevant documents and the document language models corresponding to the non-relevant documents; and re-ranking each of the plurality of documents based at least in part according to the generated boundary in the new vector space.
13. A method for sorting a plurality of documents based at least in part on a relationship between each of the plurality of documents and a user query, relevance feedback, and relationships among the plurality of document, the method comprising: converting each of the plurality of documents into a corresponding document language model, each document language model being associated with a distribution of a plurality document terms present in the plurality of documents and a plurality document terms present in each of the plurality of documents; converting the user query into a corresponding query language model, the query language model being associated with a distribution of a plurality of query terms present in the user query and the distribution of the plurality of document terms present in the plurality of documents; defining a kernel function configured to evaluate a similarity relationship between two document language models under the influence of the query language model; obtaining automatically via the defined kernel function a first vector space having a plurality of dimensions associated with at least two of the distribution of the plurality document terms present in the plurality of documents, the distribution of the plurality document terms present in each of the plurality of documents, and the distribution of the plurality of query terms present in the user query; mapping via the defined kernel function each of the document language models and the query language model in the first vector space; and ranking each of the plurality of documents based at least in part on a similarity relationship between each of the document language models and the query language model in the first vector space to determine a relative relevance of each of the plurality of documents to the user query.