Facebook, Inc.

Meta Platforms, Inc.

Kurt Dodge Runke

Andrew Donald Yates

Gunjit Singh

Ramnik Arora

An online system generates predicted outcomes for a content distribution program that distributes content to users of the online system, the predicted outcome indicating a likelihood for the occurrence of an outcome of a content presentation. The online system transmits the one or more predicted outcomes to the third-party system, and receives prediction improvement data from the third-party system, the prediction improvement data indicating an adjustment to errors in the predicted outcomes based on a prediction by the third-party system. The online system updates the properties of a content distribution program based on the prediction improvement data, the updated content distribution program causing the online system to generate new predicted outcomes based on the prediction improvement data in content presentation opportunities. The online system also transmits content to users of the online system based on the updated content distribution program.

Data transmission between two systems to improve outcome predictions

An online system, such as a social networking system, generates shared models for one or more clusters of categories. A shared model for a cluster is common to the categories assigned to the cluster. In this manner, the shared models are specific to the group of categories (e.g., selected content providers) in each cluster while requiring a reasonable computational complexity for the online system. The categories are clustered based on the performance of a model specific to a category on data for other categories.

Shared per content provider prediction models

An online system receives explicit user data and explicit event data, and implicit user data and implicit event data from a third party system. The online system generates an implicit users/implicit events data feature, an explicit users/explicit events data feature, and an explicit users/implicit events data feature. The online system generates a prediction of the counterfactual rate based on the implicit users/implicit events data feature, the explicit users/explicit events data feature, and the explicit users/explicit events data feature, the counterfactual rate indicating the likelihood that target users matching certain characteristics caused an event to occur when the target are not been presented with content by the online system, the content configured to induce users to cause the event to occur. A combined prediction rate is presented to the third party system based on the counterfactual rate.

Externally informed counterfactual prediction

An online system generates content feature entries, with each content feature entry describing a content item from a third party system. The online system generates user feature entries, each user feature entry describing a user. The online system generates a combination score for a target user and a selected content item by computing a combination of the content feature entries associated with the selected content item and the user feature entries associated with the target user using a combining function. The combination score indicates an estimated increase in value for the third party system when the target user is presented with the selected content item. The online system selects content items to transmit to a client device of a target user of the online system for presentation to the target user based on the combination score for the content items and the target user.

Joint modeling of user and content feature vector data based on third party source data

An online system uses multiple machine learning models to select content for providing to a user of the online system. Specifically, the online system trains a general model that intakes a first set of features and outputs predictions at a general level. The online system further trains a residual model that intakes a second set of features. The residual model predicts a residual (e.g., an error) of the predictions outputted by the general model. Therefore, the predicted residual from the residual model is combined with the prediction from the general model in order to correct for the over-generality of the general model. The online system may use the combined prediction to send content to users.

Content delivery based on corrective modeling techniques

An online system generates predicted outcomes for a content distribution program that distributes content to users of the online system, the predicted outcome indicating a likelihood for the occurrence of an outcome of a content presentation. The online system transmits the one or more predicted outcomes to the third party system, and receives prediction improvement data from the third party system, the prediction improvement data indicating an adjustment to errors in the predicted outcomes based on a prediction by the third party system. The online system updates the properties of a content distribution program based on the prediction improvement data, the updated content distribution program causing the online system to generate new predicted outcomes based on the prediction improvement data in content presentation opportunities. The online system also transmits content to users of the online system based on the updated content distribution program.

An online system generates, based on previously recorded content presentations, user value distributions for users of the online system. The online system also receives third party specifications from a third party system. Using this information, the online system generates a simulation for presenting content to users of the online system based on the third party specifications. For each iteration of the simulation, the online system randomly selects a user of the online system matching specifications, accesses the user value distribution for the randomly selected user, computes a selection probability of a successful content presentation to the randomly selected user, and simulates a content transmission based on the selection probability. The results of the simulation are reported to the third party system.

Prediction of content distribution statistics using a model simulating a content distribution program for a specified set of users over a time period

An online system determines how presenting an awareness campaign to a user will affect the user's likelihood of converting to a related direct response campaign. For the user, the online system creates a benchmark exposure profile representing the user's exposure history before the awareness campaign. Similarly, the online system determines the user's simulated exposure profile, which represents the user's brand exposure history after having been exposed to the awareness campaign. A response prediction for the direct response campaign is determined for the benchmark exposure profile and the simulated exposure profile. The online system estimates the difference between the response prediction and the simulated response prediction to determine a delivery control value of presenting the awareness campaign to a user. The delivery control value is used to determine an effective impression value for the awareness campaign and conversion value for the related direct response campaign.

Exposure profile optimization

An online system receives third party source data from a third party system including content feature vector entries and user feature vector entries, each content feature vector entry describing an corresponding user of the third party system, each component in each user feature vector related to a characteristic of the corresponding user. The online system generates a combination score for a target user and a selected content item by computing a combination of the content feature vector entry associated with the selected content item and the user feature vector entry associated with the target user using a combining function, the combination score indicating an estimated increase in value for the third party system when the target user is presented with the selected content item.

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Los Altos, CA, United States of America

Kurt Dodge Runke