9. An advertising server network for finding predictive cross-category search queries for behavioral targeting, comprising: a module for aggregating, using a computer, at least one training model dataset formed by a particular configuration of a data structure, the training model dataset comprising multiple configured data structures each representing an advertisement impression and including at least a history of clicks corresponding to historical advertisement information, a plurality of page features including a position of an advertisement within the page as shown to a particular user, and a plurality of internet property features, and the training model dataset comprising a plurality of targeting categories derived from the historical advertisement information; a module for training a baseline training model dataset with an initial feature set including page information features and advertisement information features, wherein the initial feature set is used to model a prior distribution of clicks and absence of clicks in a training set; a module for determining historical query and targeting category pairs such that the user historical query of the pair is predictive of clicks on display ads with the targeting category of the pair; a module for selecting, using a computer, a plurality of features from the at least one training model dataset, wherein the selected plurality of features include initial features and at least one candidate feature, wherein the candidate feature varies to fit training data and provides measuring likelihood gain of the candidate feature when added to the baseline training model dataset; a module for calculating a click probability for a subject advertisement to be clicked by a user from a page, said calculating using at least the selected plurality of features, wherein the initial features include features of the page, and wherein the at least one candidate feature is different from the initial features of the at least one training model dataset, and said calculating being normalized for queries that have a high click propensity and no relation to any user interest in a behavioral targeting taxonomy; and serving the subject advertisement to the user, when the click probability of the subject advertisement is predictive of clicks on display ads based on the determined historical query and targeting category pairs.
9. An advertising server network for finding predictive cross-category search queries for behavioral targeting, comprising: a module for aggregating, using a computer, at least one training model dataset formed by a particular configuration of a data structure, the training model dataset comprising multiple configured data structures each representing an advertisement impression and including at least a history of clicks corresponding to historical advertisement information, a plurality of page features including a position of an advertisement within the page as shown to a particular user, and a plurality of internet property features, and the training model dataset comprising a plurality of targeting categories derived from the historical advertisement information; a module for training a baseline training model dataset with an initial feature set including page information features and advertisement information features, wherein the initial feature set is used to model a prior distribution of clicks and absence of clicks in a training set; a module for determining historical query and targeting category pairs such that the user historical query of the pair is predictive of clicks on display ads with the targeting category of the pair; a module for selecting, using a computer, a plurality of features from the at least one training model dataset, wherein the selected plurality of features include initial features and at least one candidate feature, wherein the candidate feature varies to fit training data and provides measuring likelihood gain of the candidate feature when added to the baseline training model dataset; a module for calculating a click probability for a subject advertisement to be clicked by a user from a page, said calculating using at least the selected plurality of features, wherein the initial features include features of the page, and wherein the at least one candidate feature is different from the initial features of the at least one training model dataset, and said calculating being normalized for queries that have a high click propensity and no relation to any user interest in a behavioral targeting taxonomy; and serving the subject advertisement to the user, when the click probability of the subject advertisement is predictive of clicks on display ads based on the determined historical query and targeting category pairs. 15. The advertising server network of claim 9 , wherein the selecting includes at least one of, a threshold feature, a top n feature, a CTR ratio feature, a top n gain feature, an in-category feature.