1. A system that facilitates re-ranking search results retrieved by a search engine, comprising one or more computers configured with: a query log component that, responsive to a target query, builds a language model specific to the target query based on queries stored in a query log of a search engine, the query log component comprising: a compilation module that determines absolute frequencies of search terms corresponding to the queries related lexically including a number of queries submitted by users that include the target query, and a session module that determines relative frequencies of the queries related temporally and submitted during a user session; a matching component that compares a lexical property of an initial search result for the target query and the language model and computes a match score that indicates a probability that two or more search results are related by utilizing a cosine similarity and a KL-divergence for comparing; a ranking component that re-ranks a subset of the initial search result based on the match score and a rank of the initial search result, wherein the re-ranking of the subset of the initial search result comprises processing a URL, one or more page titles, a plurality of page content and a plurality of snippets resulting from a document query pairing, and further wherein, the plurality of snippets are search-engine dependent; a user control selection component that allows a user to further re-rank the initial search result, wherein the user control selection component is configured to accept user input to manually control the further re-ranking of the initial search result; and a user control component that applies a selectable modification to at least one of the system components to change the re-ranking of the subset of the initial search result and the selectable modification is one of an increase in a diversity of top re-ranked search results and a decrease in the diversity of the top re-ranked search results.
1. A system that facilitates re-ranking search results retrieved by a search engine, comprising one or more computers configured with: a query log component that, responsive to a target query, builds a language model specific to the target query based on queries stored in a query log of a search engine, the query log component comprising: a compilation module that determines absolute frequencies of search terms corresponding to the queries related lexically including a number of queries submitted by users that include the target query, and a session module that determines relative frequencies of the queries related temporally and submitted during a user session; a matching component that compares a lexical property of an initial search result for the target query and the language model and computes a match score that indicates a probability that two or more search results are related by utilizing a cosine similarity and a KL-divergence for comparing; a ranking component that re-ranks a subset of the initial search result based on the match score and a rank of the initial search result, wherein the re-ranking of the subset of the initial search result comprises processing a URL, one or more page titles, a plurality of page content and a plurality of snippets resulting from a document query pairing, and further wherein, the plurality of snippets are search-engine dependent; a user control selection component that allows a user to further re-rank the initial search result, wherein the user control selection component is configured to accept user input to manually control the further re-ranking of the initial search result; and a user control component that applies a selectable modification to at least one of the system components to change the re-ranking of the subset of the initial search result and the selectable modification is one of an increase in a diversity of top re-ranked search results and a decrease in the diversity of the top re-ranked search results. 4. The system of claim 1 , the query log component utilizes the absolute frequencies and the relative frequencies of the queries in the query log to derive the language model.