Abstract: This document describes to pacing digital component distribution and controlling the use of digital component distribution rate using feedback controllers implemented using secret sharing. In one aspect, a method includes, for each of one or more campaigns, initializing by a first computing system of multi-party computation (MPC) systems and in collaboration with one or more NI second computing systems of the MPC systems, a feedback controller for the campaign in secret shares using a secure MPC process. The first computing system updates, a first secret share of an output of the feedback controller based on an error parameter representing a difference between a setpoint and a measured rate for the campaign. The first computing system determines, based at least on the first secret share of the output, a first secret share of a pacing selector parameter that defines whether the campaign satisfies a pacing eligibility condition for the campaign.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, facilitate cross-platform content muting. Methods include detecting a request from a user to remove, from a user interface, a media item that is provided by a first content source and presented on a first platform. One or more tags that represent the media item are determined. These tags, which indicate that the user removed the media item represented by the one or more tags from presentation on the first platform, are stored in a storage device. Subsequently, content provided by a second content source (different from the first content source) on a second platform (different from the first platform) is prevented from being presented. This content is prevented from being presented based on a tag representing the content matching the one or more tags stored in the storage device.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, facilitate cross-platform content muting. Methods include detecting a request from a user to remove, from a user interface, a media item that is provided by a first content source and presented on a first platform. One or more tags that represent the media item are determined. These tags, which indicate that the user removed the media item represented by the one or more tags from presentation on the first platform, are stored in a storage device. Subsequently, content provided by a second content source (different from the first content source) on a second platform (different from the first platform) is prevented from being presented. This content is prevented from being presented based on a tag representing the content matching the one or more tags stored in the storage device.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, facilitate cross-platform content muting. Methods include detecting a request from a user to remove, from a user interface, a media item that is provided by a first content source and presented on a first platform. One or more tags that represent the media item are determined. These tags, which indicate that the user removed the media item represented by the one or more tags from presentation on the first platform, are stored in a storage device. Subsequently, content provided by a second content source (different from the first content source) on a second platform (different from the first platform) is prevented from being presented. This content is prevented from being presented based on a tag representing the content matching the one or more tags stored in the storage device.

Abstract: The present disclosure selects third party content based on feedback. A selector identifies several content items including first and second content items (or more) responsive to a request. A machine learning engine determines a first feature of the first content item, a second feature of the second content item, and a third feature of the web page or a device associated with the request. The machine learning engine determines, responsive to the first feature and the third feature, a first score for the first content item based on a machine learning model generated using historical signals received from devices via a metadata channel formed from an electronic feedback interface. The machine learning engine determines a second score for the second content item responsive to the second feature and the third feature. A bidding module determines a price for the first content item based on the first and second scores.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, facilitate cross-platform content muting. Methods include detecting a request from a user to remove, from a user interface, a media item that is provided by a first content source and presented on a first platform. One or more tags that represent the media item are determined. These tags, which indicate that the user removed the media item represented by the one or more tags from presentation on the first platform, are stored in a storage device. Subsequently, content provided by a second content source (different from the first content source) on a second platform (different from the first platform) is prevented from being presented. This content is prevented from being presented based on a tag representing the content matching the one or more tags stored in the storage device.

Abstract: The present disclosure selects third party content based on feedback. A selector identifies several content items including first and second content items (or more) responsive to a request. A machine learning engine determines a first feature of the first content item, a second feature of the second content item, and a third feature of the web page or a device associated with the request. The machine learning engine determines, responsive to the first feature and the third feature, a first score for the first content item based on a machine learning model generated using historical signals received from devices via a metadata channel formed from an electronic feedback interface. The machine learning engine determines a second score for the second content item responsive to the second feature and the third feature. A bidding module determines a price for the first content item based on the first and second scores.

Abstract: The present disclosure selects third party content based on feedback. A selector identifies several content items including first and second content items (or more) responsive to a request. A machine learning engine determines a first feature of the first content item, a second feature of the second content item, and a third feature of the web page or a device associated with the request. The machine learning engine determines, responsive to the first feature and the third feature, a first score for the first content item based on a machine learning model generated using historical signals received from devices via a metadata channel formed from an electronic feedback interface. The machine learning engine determines a second score for the second content item responsive to the second feature and the third feature. A bidding module determines a price for the first content item based on the first and second scores.

Abstract: The present disclosure selects third party content based on feedback. A selector identifies several content items including first and second content items (or more) responsive to a request. A machine learning engine determines a first feature of the first content item, a second feature of the second content item, and a third feature of the web page or a device associated with the request. The machine learning engine determines, responsive to the first feature and the third feature, a first score for the first content item based on a machine learning model generated using historical signals received from devices via a metadata channel formed from an electronic feedback interface. The machine learning engine determines a second score for the second content item responsive to the second feature and the third feature. A bidding module determines a price for the first content item based on the first and second scores.

Abstract: The present disclosure selects third party content based on feedback. A selector identifies several content items including first and second content items (or more) responsive to a request. A machine learning engine determines a first feature of the first content item, a second feature of the second content item, and a third feature of the web page or a device associated with the request. The machine learning engine determines, responsive to the first feature and the third feature, a first score for the first content item based on a machine learning model generated using historical signals received from devices via a metadata channel formed from an electronic feedback interface. The machine learning engine determines a second score for the second content item responsive to the second feature and the third feature. A bidding module determines a price for the first content item based on the first and second scores.