Abstract: An online system presenting content items to a user generates a model that predicts a latent metric describing user actions that occur at least a reasonable amount of time after presentation of content items. To determine the latent metric, the online system retrieves one or more models predicting likelihoods of the user performing various interactions when presented with the content items and determines weights associated with different retrieved models. Combining the weighted retrieved models generates a model for determining the latent metric. As the retrieved models are based on data accessible to the online system in less than the reasonable amount of time after presenting content items, weighing the retrieved models allows the online system to predict the latent metric describing user actions occurring after content items are presented. When selecting content items for the user, the online system accounts for the latent metric determined by the generated model.

Abstract: In one embodiment, a method includes determining television content that a particular user is currently watching on a television. The method further includes determining, using one or more of a plurality of sensors, an attention level for the particular user. The attention level indicates an amount of attention paid by the particular user to the television content on the television. The method further includes determining, using one or more of the plurality of sensors, demographic information for the particular user. The method further includes storing the determined demographic information for the particular user and the particular user's attention level for the television content in the storage media.

Abstract: An online system selects content items for a user to increase probabilities of the user remembering the content items after presentation. The online system generates one or more models based on information describing amounts of time users have viewed previously presented content items. Hence, a model associated with a user predicts an amount of time the user will view a content item. When selecting content items for the user, the online system selects one or more content items that the user is predicted to view for an amount of time within a specific range, which may be based on amounts of times other users have viewed the content item or content items similar to the content item. For example, the online system increases a probability of selecting a content item the user is predicted to view for an amount of time within the specific range.

Abstract: In one embodiment, a method includes determining television content that a particular user is currently watching on a television and determining, using one or more sensors, an attention level for the particular user. The attention level indicates an amount of attention paid by the particular user to the television content. The method further includes generating an attention profile for the television content by aggregating the particular user's attention level for the television content with stored information associated with a plurality of other users about the television content. The attention profile indicates a number of users who paid attention to the television content. The method further includes determining digital content available on a social-networking system that is related to the television content and providing a comparison of the number of users who paid attention to the television content with engagement on the social-networking system with the related digital content.

Abstract: An online system selects content items for a user to increase probabilities of the user remembering the content items after presentation. The online system generates one or more models based on information describing amounts of time users have viewed previously presented content items. Hence, a model associated with a user predicts an amount of time the user will view a content item. When selecting content items for the user, the online system selects one or more content items that the user is predicted to view for an amount of time within a specific range, which may be based on amounts of times other users have viewed the content item or content items similar to the content item. For example, the online system increases a probability of selecting a content item the user is predicted to view for an amount of time within the specific range.

Abstract: An online system presenting content items to a user generates a model that predicts a latent metric describing user actions that occur at least a reasonable amount of time after presentation of content items. To determine the latent metric, the online system retrieves one or more models predicting likelihoods of the user performing various interactions when presented with the content items and determines weights associated with different retrieved models. Combining the weighted retrieved models generates a model for determining the latent metric. As the retrieved models are based on data accessible to the online system in less than the reasonable amount of time after presenting content items, weighing the retrieved models allows the online system to predict the latent metric describing user actions occurring after content items are presented. When selecting content items for the user, the online system accounts for the latent metric determined by the generated model.

Abstract: An online system selects content items for a user to increase probabilities of the user remembering the content items after presentation. The online system generates one or more models based on information describing amounts of time users have viewed previously presented content items. Hence, a model associated with a user predicts an amount of time the user will view a content item. When selecting content items for the user, the online system selects one or more content items that the user is predicted to view for an amount of time within a specific range, which may be based on amounts of times other users have viewed the content item or content items similar to the content item. For example, the online system increases a probability of selecting a content item the user is predicted to view for an amount of time within the specific range.

Abstract: An advertisement system measures an ad lift metric for advertisement campaigns, which indicates the increase in conversions that can be attributed to the advertisement campaign. As impression opportunities become available for users for the ad in the lift study, the advertisement system determines whether the user is in a test group or a control group. To limit bias in the lift study, rather than holding out ads from being provided to users after the ad has been selected for the user and right before the impression, the system holds out the ads at a higher level in the ad selection process. In this manner, not all test group users receive the advertisement. The system computes the lift metric as e.g., the incremental lift (difference between conversion rates in the test and control groups), and this is divided by conversion rate of an exposed target group minus the incremental lift.