1. One or more computer-readable media storing computer-useable instructions that, when used by one or more computing devices, causes the one or more computing devices to perform a method of generating a machine-learned model for ranking search results using click-based data comprising: referencing data from one or more user queries, wherein the data is referenced from one or more of a general search engine and a vertical search engine; generating a training set of data, wherein the training set comprises one or more search results extracted from the data; associating one or more click-based judgments with each of the one or more search results in the training set, wherein associating one or more click-based judgments with each of the one or more search results in the training set comprises: (1) determining that a plurality of queries are tail queries, wherein a tail query satisfies a threshold for the data referenced from one or more queries; (2) aggregating the search results of the plurality of tail queries into one or more classes of tail queries, such that the training set comprises at least one class of tail queries having a plurality of tail queries; and (3) associating one or more click-based judgments with each of the one or more classes of tail queries in the training set; based on associating one or more click-based judgments with each of the one or more search results in the training set, determining one or more identifiable features from the training set; and based on determining one or more identifiable features, generating a rule set for ranking subsequent search results of one or more user queries.
1. One or more computer-readable media storing computer-useable instructions that, when used by one or more computing devices, causes the one or more computing devices to perform a method of generating a machine-learned model for ranking search results using click-based data comprising: referencing data from one or more user queries, wherein the data is referenced from one or more of a general search engine and a vertical search engine; generating a training set of data, wherein the training set comprises one or more search results extracted from the data; associating one or more click-based judgments with each of the one or more search results in the training set, wherein associating one or more click-based judgments with each of the one or more search results in the training set comprises: (1) determining that a plurality of queries are tail queries, wherein a tail query satisfies a threshold for the data referenced from one or more queries; (2) aggregating the search results of the plurality of tail queries into one or more classes of tail queries, such that the training set comprises at least one class of tail queries having a plurality of tail queries; and (3) associating one or more click-based judgments with each of the one or more classes of tail queries in the training set; based on associating one or more click-based judgments with each of the one or more search results in the training set, determining one or more identifiable features from the training set; and based on determining one or more identifiable features, generating a rule set for ranking subsequent search results of one or more user queries. 4. The one or more computer-readable media of claim 1 , wherein associating one or more click-based judgments with each of the one or more search results in the training set comprises: receiving click-based judgments from one or more human judges, wherein the click-based judgments correspond to user preferences regarding one or more of the plurality of search results in the training set.