Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: An online system determines candidate parameter values to be used by a machine learning algorithm to train a machine learning model by saving historical datasets that include historical parameter searches and the performance of prior machine learning models that were trained on the historical parameters. Using the historical datasets, the online system identifies parameter predictors associated with a relation between candidate parameter values and properties of the training dataset that will be used to train the machine learning model. The online system trains the machine learning models according to the candidate parameter values and validates that the machine learning model is performing as expected. If the online system detects that the machine learning model is performing outside of an acceptable range, the online system determines new candidate parameter values and re-trains the machine learning model.

Abstract: 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.

Abstract: 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.

Abstract: When an opportunity arises to present a content item to a user, an online system delivers a content item to a user according to a first content delivery strategy associated with the content item. For the impression of the content item to the user, the online system tracks attributes associated with the first content delivery strategy. In addition to tracking the attributes associated with the first content delivery strategy, the online system also tracks attributes associated with at least one other content delivery strategy (a second content delivery strategy). The attributes tracked for the second content delivery strategy are used to train a machine learning model for the second content delivery strategy. The model is used to deliver the content item or other items according to the second content delivery strategy.

Abstract: 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.

Abstract: An online system presents ads on behalf of advertisers to users of the online system. For an ad campaign, the online system determines bid prices to be associated with an ad for different eligible users based at least on user cost models associated with the eligible users and a value curve that specifies an amount of value the advertiser derives from each ad impression. Using user cost models and the value curve, the online system evaluates how much value an advertiser will derive from ad impressions. The online system maximizes an expected value that an advertiser can derive from ad impressions to an eligible user to determine a bid price. The online system calculates an expected value as an amount of value that the advertiser derives from the ad impression with a bid price weighted by a likelihood of winning auctions with a bid price.

Abstract: 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.

Abstract: 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.