Abstract: Systems and methods for optimizing offsite content delivery are provided. A content request is received from a content exchange and multiple candidate content delivery campaigns are identified in response to the content request. A computerized method includes, for each candidate content delivery campaign, determining a resource usage per conversion on a particular content platform, determining a conversion rate on one or more third-party content platforms, and determining a conversion rate on the one or more third-party content platforms. The resource usage per impression is computed based on the resource usage per conversion, the resource usage per selection, and the conversation rate. A particular candidate content delivery campaign is selected from among multiple candidate content delivery campaigns based on the resource usage per impression and the particular candidate content delivery campaign is caused to be transmitted over a computer network to the content exchange.

Abstract: Techniques for predicting an offsite entity interaction rate are provided. One approach involves using a first machine-learned model that includes a first plurality of features that correspond to entity and campaign attributes. The approach also involves training a second machine-learned model that includes a second plurality of features that includes a particular feature corresponding to predicted entity interaction rates. Thus, output of the first machine-learned model is input to the second machine-learned model. The second machine-learned model includes multiple weights that include a particular weight for the particular feature. A content request is received and a set of campaigns is identified based on an entity identifier associated with the content request. Scores are generated based on the first and second machine-learned models. Based on the scores, a campaign is selected and campaign data associated with the campaign is transmitted over a computer network.

Abstract: Systems and methods for optimizing offsite content delivery are provided. A content request is received from a content exchange and multiple candidate content delivery campaigns are identified in response to the content request. A computerized method includes, for each candidate content delivery campaign, determining a resource usage per conversion on a particular content platform, determining a conversion rate on one or more third-party content platforms, and determining a conversion rate on the one or more third-party content platforms. The resource usage per impression is computed based on the resource usage per conversion, the resource usage per selection, and the conversation rate. A particular candidate content delivery campaign is selected from among multiple candidate content delivery campaigns based on the resource usage per impression and the particular candidate content delivery campaign is caused to be transmitted over a computer network to the content exchange.

Abstract: Techniques for predicting an offsite entity interaction rate are provided. One approach involves using a first machine-learned model that includes a first plurality of features that correspond to entity and campaign attributes. The approach also involves training a second machine-learned model that includes a second plurality of features that includes a particular feature corresponding to predicted entity interaction rates. Thus, output of the first machine-learned model is input to the second machine-learned model. The second machine-learned model includes multiple weights that include a particular weight for the particular feature. A content request is received and a set of campaigns is identified based on an entity identifier associated with the content request. Scores are generated based on the first and second machine-learned models. Based on the scores, a campaign is selected and campaign data associated with the campaign is transmitted over a computer network.