7. A system for ranking documents using a ranking model, the system comprising: a component for obtaining a data set, wherein the data set includes a plurality of documents and document metadata, wherein the document metadata describes relationships between the documents; a component for maximizing a likelihood figure by employing a gradient ascent that utilizes the document metadata and the relationship descriptions to determine the likelihood figure, wherein maximizing the likelihood figure is performed by a processor executing instructions stored in a memory; a component for modifying parameters of the ranking model, wherein the parameters are modified by the use of the likelihood, wherein the ranking model employs a Continuous Conditional Random Field, and wherein the ranking model utilizes a conditional probability distribution representing a mapping relationship from the plurality of documents to associated ranking scores represented by continuous variables; and a component for ranking the documents by the use of the modified parameters, wherein the ranking of the documents is consistent with the document metadata.
8. The system of claim 7 , wherein the component for maximizing a likelihood includes the use of a specific function that processes data describing content similarity relationships between the documents.