Abstract: Systems and methods described herein may determine audiences for content delivery. A server receives configuration inputs for identifying a target audience from a content-generating user. The configuration inputs indicate the target audience, baseline audience, and special audience. The target audience includes targeted end-users for the content. The baseline audience includes a broad population of end-users having a population characteristic (e.g., geography). The special audience includes a selected population of end-users having a special characteristic in the topics previously accessed by the end-users, such that the server correlates the baseline audience with the special audience to identify the target audience of target users who accessed a webpage having the topic and who are located in, e.g., a geography. The server uses topics terms from all target users to generate a ranked-list of context terms the content-user applies to predict whether webpages have the target audience.

Abstract: Systems and methods described herein may determine audiences for content delivery. A server receives a context term from a client device. The server generates a set of context terms including the context term by selecting one or more of a plurality of corpus terms having a relationship with the context term. The server may calculate an implication score for each topic term of a plurality of topic terms based on a co-occurrence between the topic term and one or more of the set of context terms on a plurality of webpages associated with the topic term. The server may select a topic term from the plurality of topic terms based on the implication score for the topic term. The server may determine an audience identifying users having accessed at least one of the plurality of webpages associated with the topic term.

Abstract: Systems and methods described herein may determine audiences for content delivery. A server receives configuration inputs for identifying a target audience from a content-generating user. The configuration inputs indicate the target audience, baseline audience, and special audience. The target audience includes targeted end-users for the content. The baseline audience includes a broad population of end-users having a population characteristic (e.g., geography). The special audience includes a selected population of end-users having a special characteristic in the topics previously accessed by the end-users, such that the server correlates the baseline audience with the special audience to identify the target audience of target users who accessed a webpage having the topic and who are located in, e.g., a geography. The server uses topics terms from all target users to generate a ranked-list of context terms the content-user applies to predict whether webpages have the target audience.

Abstract: Systems and systems described herein may generate campaigns and efficiently calculate bids for placement of campaign data into Internet data. Embodiments may calculate context scores for campaign data based on campaign terms and beacon terms. The context scores may be used to identify Internet content that has a high page score. If the page score of particular Internet content exceeds a predetermined threshold, the system may place a bid for a campaign based on disclosed algorithms taking as inputs performance scores, context scores, page scores, campaign budgets, and other parameters. The systems therefore are capable of quickly and effectively calculating optimal bids to place for a particular campaign given parameters disclosed herein.

Abstract: Systems and methods described herein may generate campaigns and efficiently calculate bids for placement of campaign data into Internet data. Embodiments may calculate context scores for campaign data based on campaign terms and beacon terms. The context scores may be used to identify Internet content that has a high page score. If the page score of particular Internet content exceeds a predetermined threshold, the system may place a bid for a campaign based on disclosed algorithms taking as inputs performance scores, context scores, page scores, campaign budgets, and other parameters. The systems therefore are capable of quickly and effectively calculating optimal bids to place for a particular campaign given parameters disclosed herein.

Abstract: Systems and methods described herein may generate campaigns and efficiently calculate bids for placement of campaign data into Internet data. Embodiments may calculate context scores for campaign data based on campaign terms and beacon terms. The context scores may be used to identify Internet content that has a high page score. If the page score of particular Internet content exceeds a predetermined threshold, the system may place a bid for a campaign based on disclosed algorithms taking as inputs performance scores, context scores, page scores, campaign budgets, and other parameters. The systems therefore are capable of quickly and effectively calculating optimal bids to place for a particular campaign given parameters disclosed herein.

Abstract: Systems and methods described herein may generate campaigns and efficiently calculate bids for placement of campaign data into Internet data. Embodiments may calculate context scores for campaign data based on campaign terms and beacon terms. The context scores may be used to identify Internet content that has a high page score. If the page score of particular Internet content exceeds a predetermined threshold, the system may place a bid for a campaign based on disclosed algorithms taking as inputs performance scores, context scores, page scores, campaign budgets, and other parameters. The systems therefore are capable of quickly and effectively calculating optimal bids to place for a particular campaign given parameters disclosed herein.