15. A method according to claim 14 , wherein refining the query language model comprises analyzing a distribution of the plurality of document terms present in the selection of relevant documents in the positive feedback input and a distribution of the plurality of query terms present in the selection of relevant documents in the positive feedback input.
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.